Handbook of Multimedia for Digital Entertainment and Arts- P3

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Handbook of Multimedia for Digital Entertainment and Arts- P3: The advances in computer entertainment, multi-player and online games, technology-enabled art, culture and performance have created a new form of entertainment and art, which attracts and absorbs their participants. The fantastic success of this new field has influenced the development of the new digital entertainment industry and related products and services, which has impacted every aspect of our lives.

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  1. 46 N. Kamimaeda et al. Fig. 15 Automatic Metadata Expansion title, keywords, and so on. Content profiles (CP) are created from these data and they consist of some vectors like CP (i) D (ContentId, Attribute Id, Value Id). ContentId is a primary key to distinguish between content. Attribute is a class unit such as cast, genre, and so on. Value is an instance of the class. For example, comedy, sports, or drama is a value of an attribute of the genre. We can also find that A, B, C, D, or E is a value of an attribute of cast (Figure 15). AME creates new content metadata, which are (ContentId, Destination Attribute, Destination Value), from the original content metadata, which are (ContentId, Ori- gin Attribute, Origin Value). Consider the following example with content named 001. It has cast of Mr. A, which becomes vector D (001, Cast, Mr. A). The con- tent gets new metadata (001, Personality, Intelligent) since the ACD declares that the customers think Mr. A is an intelligent person. Therefore, our system can use not only original content metadata but also these expanded metadata for recommen- dation purposes. These processes are performed in our system as a part of content metadata creation in the mining engine. If the ACD has knowledge based on the lifestyle, for example a person who likes the title EB is Early Adapter or Follower likes Cast A, it is very efficient to apply this method to cross-category recommendation. Lifestyle is very suitable as common metadata. Moreover, if the content metadata is expanded using lifestyle knowledge, it becomes easier to realize advertisement recommendation.
  2. 2 Cross-category Recommendation for Multimedia Content 47 ICF The second method is ICF. ICF can recommend items unknown to the user. This has the same advantage as traditional CF methods, but the ICF also works well to recommend completely new items, less reusable items such as TV programs, and high merchandise turnover rate items. As mentioned earlier, item-based CF selects recommendation items based on groups of similar items and user-based CF selects recommendation items based on groups of similar users. In either case, recommendation items are directly predicted based on these groups (Figure 16). On the other hand, ICF does not directly select favorable items. ICF predicts the user preferences. These are registered as the expected user preferences as a part of the user preference vector. Then, the recommendation items are selected based on an existing recommendation method like the VSM by using not only the original user preferences but also the expected user preferences. This is why this method is called an “indirect” method (Figure 17). ICF necessitates the following two steps: 1. The calculation of similarity between the users based on the original user pref- erences. Similarities between the users are calculated based on the original user preferences such as lifestyle, viewer’s age, viewing style, cast, and so on, as men- tioned earlier. Formula (2) indicates how to calculate the similarities between user X and user Y. P .Xv X/.Yvi Y / si mxy D qP v qP ::: (2) v .Xv X/2 v .Yv Y /2 Xv : User X’s preference value of V X : User X’s average preference value. Fig. 16 Traditional (Direct) CF Fig. 17 ICF
  3. 48 N. Kamimaeda et al. When the number of users becomes considerable, we need to decrease the calcu- lation effort to find similar users. In our system, hundreds of typical users can be found by employing some clustering algorithms before performing the similarity calculations. 2. Expectations of user preferences User preferences that are not contained with the original user preferences are predicted. They are referred to as the “expected user preferences.” Formula (3) indicates how to calculate user X’s expected user preference value of V . The expected user preferences are registered in the database for recommendation as a part of the user preference vectors. P N .Nv N /simXN ExpectX v D X C P ::: (3) N jsimXN j N : User who is similar to user X. Therefore, the user can enjoy the recommendation based on not only the normal user preference vectors but also the expected user preference vectors. The user can find favorable items that she/he has never seen before. Moreover, this method is also beneficial to a system administrator of a recommendation system. This method is easy to apply to an existing recommendation system such as the VSM since the expected user preference vectors have the same style as normal user preference vec- tors and can be stored in the same table in the database. The system administrator can easily create a multi-algorithm recommendation system with ICF. In VE, ICF is used as a part of the user preference creation in the mining engine. RCF As mentioned earlier, CF methods cannot often work well with completely new items. RCF attempts to resolve this problem by using not only traditional CF- based similar items but also CBF-based similar items’ CF-based similar items. RCF requires the following two steps: 1. The calculation of similar items based on traditional item-based CF for all of the items 2. The calculation of RCF-based similar items using CBF-based similar items and CF-based similar items The following four small steps are involved in this process. 1) Select several attributes used for the CBF calculation from the metadata. 2) By using these attributes, CBF-based similar items are calculated based on the cosine measure or inner product. 3) By using equation (4), calculate the RCF-based similarity based on the CBF- based similarities calculated in step (2) and CF-based similarities calculated in step (1).
  4. 2 Cross-category Recommendation for Multimedia Content 49 Fig. 18 RCF Step 1:Calculation of Similar Items Based on Traditional Item-based CF Fig. 19 RCF Step 2: Calculation of RCF-based Similar Items P SimCBF .A; n/SimCF .n; B/ n2N SimRCF .A; B/ D .1 ˇ/SimCF .A; B/ C ˇ P SimCBF .A; n/ n2N (4) In this equation, A, B, and n are items. Here, n represents all of the CBF-based similar items for A. Moreover, Sim represents the calculation of similarity among the two items. 4) Based on step (3), RCF-based similar items are selected as the recommended items. The images of this algorithm are shown in Figures 18 and 19. Evidently, this method can work well for almost all of the items even if they are completely new items, unless the CBF-based similar items for the seed item do not exist or all of the CBF-based similar items do not have CF-based similar items. In VE, RCF is used as a part of content profiling in the mining engine. Example of Practical Applications Multimedia Content Recommendation There have already been many systems and studies regarding multimedia recom- mendation. Here, we introduce two practical systems as examples for multimedia
  5. 50 N. Kamimaeda et al. content recommendation: branco [33] and SensMe [34]. branco is an IPTV recom- mendation service. This recommendation function is realized using the VE. SensMe is an automatic music playlist generator used in mobile phones made by Sony Ericsson [35]. This application is based on the 12 Tone technology [36] developed by Sony Corporation. branco branco is an example of content-meta-based search and user-preference-based search. It is an IPTV service that uses IP multicast network and has several chan- nels for broadcast. Therefore, a user can watch content like TV. Moreover, branco adopts an advertising model. Therefore, the users who can connect to the IP multi- cast network can use branco for free. The recommendation function for this service is realized using VE. As an example, a user-preference-based search is shown in Figure 20. The recommended programs by VE are shown as “anapita” which means just fitting you in Japanese. SensMe SensMe is an example of content-meta-based search. It is an automatic playlist gen- erator from the music content of the user. An image of the SensMe application is shown in Figure 21. SensMe analyzes the music automatically and then extracts the music features like speed, tone and mood. After that, SensMe maps the music to an X–Y axis using tempo and mood. On this X–Y dimension, the user can see the mu- sic listened to by the user. Moreover, SensMe automatically generates 11 different Fig. 20 Recommendation in branco
  6. 2 Cross-category Recommendation for Multimedia Content 51 Fig. 21 SensMe music channels such as “Morning,” “Relax,” and “Upbeat.” Therefore, by choosing a channel instead of selecting individual tracks, the user can listen to a music playlist generated by SensMe as a recommendation. Cross-category Recommendation In this section, we introduce two systems as examples of cross-category recommen- dation services: VAIO Giga Pocket Digital [37] and TV Kingdom service [12]. Both these systems use the VE to realize recommendation functions. Giga Pocket Digi- tal is a TV content manager for the VAIO system. The TV Kingdom service is an online TV guide service in So-net (So-net Entertainment Corporation). Giga Pocket Digital is an example of crossing categories only for user preference. TV Kingdom is an example of crossing categories not only for user preference but also for recom- mendation. The explanations for these two systems are described in the following two sections. VAIO Giga Pocket Digital Giga Pocket Digital is a TV content manager. Using this system, a user can watch TV programs in real time and record them manually by favorite keyword and user preference. In this system, the VE realizes content-meta-based search and user-preference-based search. For example, Figure 22 shows an image of the user- preference-based search. VE recommends only TV programs, because this application handles only TV programs. However, VE learns the user preference from not only the user’s behav- ior on this application but also what kind of music the user possesses. This means
  7. 52 N. Kamimaeda et al. Fig. 22 User-preference-based search that the user preference is generated from the user’s behavior in the TV and music categories. It is very efficient to have cross-categories even if only the user prefer- ences are crossed. By crossing the user preference for the TV and music categories, VE can recommend TV programs in which the user’s favorite artist appears. VE also renders the edit function for user preferences, as shown in Figure 23. This pane is called “My Carte”. Here, the user can see his/her own preferences, such as frequently viewed casts, genres, and keywords. These casts include not only the persons watched by the user but also the persons whose music the user possesses. Users can also check their own TV-viewing style e.g., the user’s frequently watched sports programs and infrequently watched drama programs. Moreover, users can edit their own preferences to customize the recommendation result. TV Kingdom Service TV Kingdom now has 800,000 unique users per month who enjoy not only a con- ventional TV guide but also a personalized TV guide, which can work together with a consumer electronics appliance or a personal computer. Figure 24 indicates the basic services offered by TV Kingdom. 1. A user can easily find TV programs and enjoy a useful EPG service having interesting functions, such as category-oriented list, recording-ranking list, and cast-oriented list. 2. The user can record and reserve TV programs on her/his local personal video recorder (PVR) through the TV Kingdom EPG by just clicking the iEPG icons.
  8. 2 Cross-category Recommendation for Multimedia Content 53 Fig. 23 My Carte Fig. 24 Basic Services of TV Kingdom 3. The user can record programs on her/his local PVR even when away from home using a mobile PC or cell phones provided by companies such as NTT-DoCoMo, Softbank, and au. 4. The PVR automatically records the programs having the same keywords, such as genre, title, or cast, as registered by the user. These basic functions are really useful for the PVR user. However, it is somewhat monotonous for the user to find out her/his favorite programs and make a reserva- tion for recording. The automatic recording function resolves this problem on some
  9. 54 N. Kamimaeda et al. Fig. 25 Example of Cross-category Recommendation level, but the user has to set her/his favorite keywords manually. We have tried to offer a better solution for this issue by using a VE. Here, content-meta-based search and user-preference-based search are realized by the VE. Moreover, the TV Kingdom service offers not only TV program recommenda- tions but also video, e-Book, DVD, CD, book, and cross-category recommendation among these categories. An image of the cross-category recommendation service is shown in Figure 25. VE learns the user preferences from the user’s behavior on all the categories. By employing these user preferences, the VE realizes cross-category recommendation among these categories. Difficulties There are three difficulties for realizing multimedia content recommendation and cross-category recommendation: how to extract features from content itself for mul- timedia content recommendation and how to generate common metadata and to merge each category’s user preference for cross-category recommendation. The first problem involves the extraction of features from the content itself. This creates difficulty in realizing multimedia content recommendation. It is necessary to develop feature extraction tools for each category such as music, picture or motion
  10. 2 Cross-category Recommendation for Multimedia Content 55 picture. For example, in the motion picture category, scene detection or face recog- nition tools are necessary to be extracted. However, it is cumbersome to implement these tools for each category. Moreover, sufficient metadata cannot be extracted from some content (e.g., motion picture) for efficient recommendation. Therefore, we need to consider recommendation algorithms using limited metadata when real- izing recommendation functions for such content. The second problem involves the generation of common metadata. This creates difficulty in realizing cross-category recommendation. We can easily employ per- son and keyword attributes as direct common metadata because almost all of these items have these attributes. However, it is difficult to select other common attributes, because most of these items have different metadata. Therefore, in order to realize a cross-category recommendation system, it is necessary to investigate what kind of metadata the system can actually use. If common metadata do not exist, we should consider an appropriate way to treat such content. For example, VE has personal- ity data for each cast in the ACD and the content metadata are expanded using the AME. Lifestyle segmentation data also can be used as common metadata. Here, a large amount of time is normally required to generate such kinds of data. However, common metadata is a key to successfully realize cross-category recommendation. Therefore, it is very important to consider this issue. The final problem is how to merge each category’s user preference. This cre- ates difficulty in realizing cross-category recommendation. Moreover, this is also essential for successfully realizing cross-category recommendation, because rec- ommended items will be changed based on how to merge the user preference. The best way to merge depends on the system requirements. Therefore we determine how to merge the user preference based on the results of the evaluation experiments for each application and each system. In addition, on the basis of these results, we change the weights of each attribute such as genre and keyword. Summary and Future Prospects This article has introduced cross-category recommendation technologies for mul- timedia content. First, an overview of the recommendation technologies has been outlined. After that, practical applications and services realizing multimedia recom- mendation and cross-category recommendation have been described. Then, we have mentioned difficulties in cross-category recommendation for multimedia recom- mendation. These features are imperative to realize a good recommendation system. Recently, multimedia recommendation technologies have become more impor- tant because many products and services that involve multimedia content have been developed. In the near future, cross-category recommendation technologies will be more important. For example these technologies are necessary to realize advertise- ment recommendations based on TV programs watched by the user or based on the music listened to by the user. Moreover, for enhancing user experience, these tech- nologies will be important, too. This is because these technologies eliminate the differences among categories and support the users to explore the huge information
  11. 56 N. Kamimaeda et al. space. In addition, we predict that not only cross-category recommendation but also cross-device recommendation will be important. By realizing cross-device recom- mendation, user experience can be improved. To turn this into reality, we may need to resolve several issues such as how to translate the user preference among devices. We may need to decide some abstract metadata schema and translation rules. We need to research these kinds of issues. Acknowledgement We would like to thank our colleagues at the PAO Gp., Intelligent Systems Research Laboratory, System Technologies Laboratories, Corporate R&D, Sony Corporation and Sec.5, Intelligence Application Development Dept., Common Technology Division, Technology Development Group, Corporate R&D, Sony Corporation for their invaluable assistance. References 1. NetCraft “January 2009 Web Server Survey.” server survey.html 2. P. Resnick et al. (1994). GroupLens: An Open Architecture for Collaborative Filtering of Netnews. Proc. ACM 1994 Conf. Computer Supported Cooperative Work, ACM Press, pp. 175–186. 3. G. Linden, B. Smith, and J. York (2003). Recommendations: Item-to-Item Col- laborative Filtering. IEEE Internet Computing: Industry Report. 0301/d/w1lind.htm 4. 5. MusicStrands (MyStrands). 6. SoundFlavor. 7. George Chang, Marcus J. Healey, James A.M. McHugh, Jason T.L. Wang, “Mining the World Wide Web”, Kluwer Academic Publishers, 2001. 8. Jeongphee Yi, Tetsuya Nasukawa, Razvan Bunescu, Wayne Niblack, “Sentiment Analyzer: Extracting Sentiments about a Given Topic Using Natural Language Processing Techniques”, ICDM 2003. 9. All Media Guide. 10. Pandora Internet Radio. 11. Gracenote. 12. TV Kingdom. (in Japanese) 13. T. Tsunoda, M. Hoshino, “Automatic Metadata Expansion and Indirect Collaborative Filtering for TV Program Recommendation System”, Euro ITV 2006. 14. Google. 15. Google Page Rank technology. 16. Milan Petkovic, Willem Jonker, “Content-Based Video Retrieval”, Kluwer Academic Publishers, 2002. 17. M. Flickner et al., “Query by Image and Video. Content: The QBIC System”, IEEE Computer, Vol. 28, No. 9, pp. 23–32, 1995. 18. J. R. Smith, S.-F. Chang, “VisualSEEk: A Fully Automated Content-Based Image Query Sys- tem”, ACM Multimedia, 1996, pp. 87–98. 19. Yossi Rubner, Leonidas J. Guibas, Carlo Tomasi, “The Earth Mover’s Distance, Multi- Dimensional Scaling, and Color-Based Image Retrieval”, in Proceedings of the ARPA Image Understanding Workshop, New Orleans, LA, May 1997, pp. 661–668. 20. JSEG: Color Image Segmentation.
  12. 2 Cross-category Recommendation for Multimedia Content 57 21. David G. Lowe, “Distinctive Image Features from Scale Invariant Keypoints”, International Journal of Computer Vision, Vol. 60, No. 2, pp. 91–110, 2004. 22. Sony’s Picture Motion Browser. id/ 1224842106509 23. Aymeric Zils, Francois Pachet, “Automatic Extraction of Music Descriptors from Acoustic Signals Using EDS”, in Proceedings of the 116th AES Convention, May 2004. 24. Sony’s Hard-drive-based Music Systems “Giga Juke.” hdd-audio 25. P. Cano, E. Battle, T. Kalker, J. Haitsma, “A Review of Algorithm for Audio Fingerprinting”, in Workshop on Multimedia Signal Processing, 2002. 26. Shazam. 27. Ericsson: System Overview Mobile Positioning System (MPS). com/mobilityworld/sub/open/technologies/mobile positioning/about/mps system overview/ 28. Brian Clarkson, Alex Pentland, “Unsupervised Clustering of Ambulation Audio and Video”, ICASSP 98. 29. Tom M. Mitchell, “Machine Learning”, WCB/McGraw-Hill, 1997, pp. 177–184. 30. N. Yamamoto, M. Saito, M. Miyazaki, H. Koike, “Recommendation Algorithm Focused on Individual Viewpoints”, IEEE CCNC 2005 pp. 65–70, 2005. 31. Amazon. 32. Voyager Engine.1 (in Japanese) from theme/contents 01.html 33. Sony Marketing (Japan) Inc. (2009). branco home page (in Japanese) 34. SensMe. (in Japanese) contents 01.html 35. Sony Ericsson Mobile Communications AB. 36. 12 Tone Technology. (in Japanese) theme/12tonealalysis 01.html 37. Sony Corporation (2008). VAIO Giga Pocket Digital homepage (in Japanese) http://www.vaio. 1 “Voyager Engine” is a trademark of Sony Corporation and So-net Entertainment Corporation
  13. Chapter 3 Semantic-Based Framework for Integration and Personalization of Television Related Media Pieter Bellekens, Lora Aroyo, and Geert-Jan Houben Introduction The online information locomotive drives on at an ever increasing pace. Constantly we see expansion of existing methods and systems, while at the same time, new innovations and techniques sprout out of nowhere. These changes bring new possi- bilities and challenges that affect the whole media chain: from content production, via distribution, to last but not least the end-user (the consumer). Lately however, the consumer himself transformed more and more into a content producer, as shown by Berman [4], making the circle round and the speed of information growth even larger. Subsequently, this breaks the traditional business model where companies and institutions are the sole content providers. We describe in this paper our re- search focusing on the synergy between available content on various media sources and the consumer at home who wants to experience multimedia content through a connected media centre. As an effect, the new forms of home media that emerge as digital systems are converging. Different content, e.g., from TV, social networks, music, homemade im- ages and videos, is no longer bound to separate devices or to local storage, and the development of the Internet makes the media boundaries become less limiting. As envisioned by for instance IBM [4], the future media may become more pervasive and offer a more ubiquitous and immersive experience, as increasing technological sophistication brings new media environments. The transfer to digital content along L. Aroyo ( ) Department of Computer Science, Free University of Amsterdam, Amsterdam, Netherlands e-mail: P. Bellekens Department of Mathematics & Computer Science, Eindhoven University of Technology, Eindhoven, Netherlands e-mail: G.-J. Houben Department of Software Technology, Delft University of Technology, Delft, Netherlands e-mail: B. Furht (ed.), Handbook of Multimedia for Digital Entertainment and Arts, 59 DOI 10.1007/978-0-387-89024-1 3, c Springer Science+Business Media, LLC 2009
  14. 60 P. Bellekens et al. with technologies and standards like DVB11 , HDTV, voice over IP, Blu-ray2 and TV-Anytime3 create opportunities to bring new interactivity to the traditional TV concept and change it drastically. The television industry always has been a con- servative one. It has not yet experienced a major revolution for the past fifty years, which constitutes a strong contrast to the Internet which has quickly evolved from mere textual information to multimedia content. We believe that using Semantic Web technology in the concept of TV content interaction may provide a change from a traditional one-way communication to a two-way communication where the user changes from a passive viewer to a more active participant and program struc- tures change from fixed to dynamic. In this paper we try to identify requirements, opportunities and problems in home media centers and we propose an approach to address them by describing an intel- ligent home media environment. The major issues investigated are coping with the information overflow in the current provision of TV programs and channels and the need for personalization to specific users by adapting to their age, interests, language abilities, and various context characteristics. The research presented in this paper follows from a collaboration between Eindhoven University of Technol- ogy, the Philips Applied Technologies group and Stoneroos Interactive Television. The work has been partially carried out within the ITEA-funded European project Passepartout, which also includes partners like Thomson, INRIA and ETRI. In the following chapter we describe the motivation and research problem in re- lation to related work, followed by an illustrative use case scenario. Afterwards, we explain our data model which starts with explaining the TV-Anytime structure and its enrichments with semantic knowledge from various ontologies and vocabularies. The data model description then serves as the background for understanding our proposed system architecture SenSee. Afterwards we go deeper into the user mod- eling part and explain how our personalization approach works. The latter elaborates on a design targeting interoperability and on semantic techniques for enabling in- telligent context-aware personalization. In the implementation chapter we describe some practical issues as well as our main interface showcase, iFanzy. Future work and conclusions end this chapter. Related Work We investigate the design of a home media architecture of connected devices that can provide access to a wide range of media sources, yet at the same time avoid an overflow of information for the user. In our framework called SenSee, for sensing the user and seeing the content, and the iFanzy application (a personalized EPG running on top of SenSee), we aim to connect different devices, such as shared 1 2 3
  15. 3 Semantic-Based Framework for Integration and Personalization 61 (large) screens with set-top boxes, personal (small) handheld devices and biosensor- based interfaces, and different media sources like IP, broadcast and local storage. This intentionally goes beyond the traditional limited solution of a single TV screen and simple remote control and thus creates the foundation for an ambient home environment to collect various data about the users and to subsequently use this data for the personalization of his/her interaction with the TV content. Related work on connected homes can be found in the field of ambient intelligence, investigated for instance at the Philips HomeLab [9]. Regarding the information overflow aspect, we assume that the amount of avail- able digital content will increase enormously with the current digital development, as also indicated by Murugesan and Deshpande [18]. Both paper program guides and simple EPGs are thus likely to turn inefficient in terms of helping the user in choosing from an overwhelming amount of content, a situation previously also shown by both Chorianopoulos and O’ Sullivan et al. [7, 20]. This creates a need for media systems to support the user by providing intelligent search and recom- mendations to propose the most relevant and interesting programs. Similar research focused on filtering for interactive TV systems in home environments have previ- ously been done by e.g., Goren-Bar and Glinansky [10]. Here content filtering and user stereotypes were used for capturing and using user preferences. Various researchers furthermore emphasize that there is a need for person- alization in dealing with a vast amount of TV content [1]. We believe that a personalization approach in home media centers is significant in order to handle the user’s preferences as basis for the interaction both regarding content and de- vices. Since users differ in ages, interests, abilities and language preferences, it is important that these preferences can be reflected in the system. For instance, an eight year old person will have very different favorite programs than an adult, and a given user might want the movies to always be displayed on the biggest screen, but private content only on his or her handheld device. By creating a user model, as described by Kobsa [13], for each user of the system, such personal preferences may be stored. This needs to capture both a user profile, with the user’s preferences, and a user context, which describes the current situation that the user is in, for ex- ample whether the user is alone or with a group, what the available devices are at the moment, what the time is, what the location is etc. [27, 26, 25] argue that not taking contextual information into account for recommendations, seriously limits the relevance of the results, and as in SenSee and iFanzy, they advocate context- awareness as a promising approach to enhance the performance of recommenders. While Yap et al. [27] and Tung et al. [25] illustrate their framework with a restau- rant recommender, which takes location, weather and restaurant-related data into account, Woerndl and Groh [26] apply context-aware recommender systems (by us- ing location and acceleration) in the domain of inter-networked cars. The models furthermore constitute a necessary requirement for enabling intelligent filtering of content to make recommendations like explained by van Setten [24]. By this we mean finding and suggesting content that should be interesting for the user, while filtering out unwanted or uninteresting information. Various filtering techniques for recommending movies have previously been explored by Masthoff [15], in which
  16. 62 P. Bellekens et al. several user models are combined to create group filtering. Other related work is the PTVPlus online recommendation system for the television domain by O’Sullivan et al. [20] and the Adaptive Assistant for Personalized TV by Yu and Zhou [27]. However, in general when new users (with empty profiles) are introduced, recom- mender systems have a hard time since they miss essential information to provide recommendations. To fill this informational gap, we make use of social networks like Hyves4 as they often harbor a vast amount of useful preferences and interests. Also in work from Alshabib et al. [1] a social network (LinkedIn) is used to aggre- gate ratings based on the structure of the network, by calculating the neighborhood of users. Apart from supplying semantic models of the user, it is also necessary to have sufficient metadata descriptions of the content. This constitutes the basis for content classification, i.e., sorting the content into different types like fiction, non-fiction, news, sports, etc. Intelligent search and filtering of content moreover benefit from metadata descriptions suitable for reasoning, to deduce new information and to enrich content search. Current ongoing research in this area by the W3C Multi- media Annotation on the Semantic Web Task Force has been described by Stamou et al. [23]. Similar research as presented in this paper has furthermore been performed by Hong and Lim [12] who also propose using TV-Anytime for handling content in a personalized way. However, they focus on broadcasted content, whereas we also consider content from IP and removable media like Blu-ray. Furthermore, their so- lution for content search also uses keywords and user history to recommend content, although the architecture differs in that all processing occurs at a metadata server. As will be described later, we propose the modeling of TV content with the use of ontologies. Relevant related work in this field can therefore be found in the domain of the Semantic Web. Necib and Freytag [19] have focused on using ontologies in query processing with a similar approach to ours, which aims at refining search queries with synonyms (and yet avoiding homonyms). However, we intend to take this one step further in our process as we also use other semantically related concepts and a measurement for semantic closeness. Application Scenario In this section we describe a scenario to illustrate the target functionality of our demonstrator. The setting is in the home of a European, well-off family in the year 2010, which is living in a region outside their original parental background. While they wish for the children to integrate with the local community and live and learn from their neighbors, they also value their heritage (linguistic, cultural and reli- gious), to effectively communicate with distant relatives and friends. The family consists of a mother, a father, a four year old child, a deaf nine year old, and a 4
  17. 3 Semantic-Based Framework for Integration and Personalization 63 teenager. The parents are determined that the children should be effectively multi- lingual and multi-cultural, and will invest time to adapt the multimedia content in the home. They therefore act as media guides and to some extent teachers for their children, by selecting and adapting the content. Since the parents have immigrated to the region, they will have a different preference of content than the default local selection and they use their home media centre to include also programs from their original home area, e.g., for news, music and movies. They may also choose to alter the language or subtitles of the content. As the family gathers for a movie night together, the home media centre has suggested a movie that suits each family member’s preference and interest. The mother has briefly scanned the story of the movie and discovered that the ending is in her opinion not suitable for the children. She therefore changes to an alternative ending. As they start the movie, they all together use a shared big screen. Although they use subtitles on this shared screen, this evening the deaf child also includes additional sign language on his personal small screen. The teenager on the other hand needs to practice her second language so her parents asked her this time to listen to an alternative language version with her headphones. Although they enjoy the movie together, the father also wants to follow a live soccer game broadcast, and therefore uses his own handheld screen to view this private video stream. The media devices in the home are all connected to the ambient home media environment. TV-Anytime A content structure which goes beyond a fixed linear time structure and allows mul- tiple languages, alternative versions, etc. puts high demands on the content model. It needs to have a dynamic structure, rich metadata, and be suitable for various media. We believe that the TV-Anytime5 standard can serve as the basis for such requirements and therefore we have built our demonstrator upon the TV-Anytime concepts. TV-Anytime is a full and synchronized set of XML specifications estab- lished by the TV-Anytime forum built to enable search, selection, acquisition and rightful use of content from both broadcast and online services. It basically consists out of two main parts usually referred to as Phase I and Phase II, each serving their specific goals. TV-Anytime Phase I The TV-Anytime Phase I specification is a very extensive metadata schema which describes all content retrieved by the system—a fundamental feature for searching 5
  18. 64 P. Bellekens et al. and filtering. A program description consists out of a set of information tables where each describes a specific aspect of a program P : ProgramInformationTable: Overview of metadata fields like title, synopsis, etc. GroupInformationTable: Describes the groups P belongs to ProgramLocationTable: States where/when P can be found/will be broadcasted ServiceInformationTable: States who is the rightful owner/broadcaster of P CreditsInformationTable: Contains all the credits of people in P ProgramReviewTable: Lists of reviews of P SegmentInformationTable: Contains metadata about specific segments of P PurchaseInformationTable: Contains the information about how to obtain P TV-Anytime is built to suit the needs of the future. It contains constructs to model metadata which is currently not widely available yet, e.g. metadata describing spe- cific scenes (segments) within a specific program. This property proves that the TV-Anytime specification is ready when the television market evolves and more metadata will be generated. Among these the tables, the most important one for us is the ProgramInformationTable as it contains all the essential program metadata. In the following example we show an abbreviated example of the metadata in this table for an arbitrary program P : All Stars Football, friendship and ... Comedy Football (soccer) EN 2008-10-11 20:00:00 London Every ProgramInformationTable consists of a list of ProgramInformation compo- nents, and each has a BasicDescription block which contains P ’s main description. Apart from technical descriptions such as the screen aspect ratio and the number of audio channels (not shown in the example), we see here for example a title, a synopsis and a list of genres. In order to keep a grip on what metadata creators use as values in these TV- Anytime fields, the TV-Anytime specification contains a set of controlled term hierarchies which are the only valid values for such properties. A good example is the genre field. The genre description in TV-Anytime is a fine graded taxonomy structure, going from general concepts like fiction/non-fiction down to specific cat- egories in the leaf nodes of the structure (typically well known genres like comedy, drama, daily news, weather forecast, etc.). It should for example be avoided that
  19. 3 Semantic-Based Framework for Integration and Personalization 65 people can create their own genres considering that one wants to keep interoper- ability and consistency between various content descriptions intact. The following example shows a part of the genre hierarchy: NON-FICTION/INFORMATION News Sport News News of sport events Every genre has an id which exemplifies its depth in the tree and shows which other genre is the parent (e.g. genre 3.1 is the parent of genre 3.1.1). The genre ‘sport news’ is a specialization of the genre ‘news’ which in turn belongs to the group of ‘non-fiction/information’ genres. To identify a program in TV-Anytime, the notion of a Content Reference Identi- fier (CRID) is used, following an RFC standard [8]. The program above for example is identified by the CRID “crid://”. With such a CRID, which always uniquely identifies a program, we can retrieve the program’s metadata. TV- Anytime describes a Metadata Service (MS) which is responsible for the provision of metadata. For every CRID existing, the MS can provide a metadata description, given of course that whoever created the program belonging to this CRID made this description publicly. However, people cannot just start creating CRIDs, as this typi- cally is a process which should be centrally controlled. Therefore, the TV-Anytime specification describes the concept of a CRID Authority (CA) which main purpose is to watch over CRID creation. Everybody who wants to create a new program in- stance, first needs to ask for a new unique CRID at the CA. In turn, this CRID can then be used to both refer to this program and to obtain its metadata via the MS. TV-Anytime Phase II Currently, an important evolution in data retrieval is that different pieces (or sets) of content are being linked together. Whether this is done via similarity properties for recommendations (e.g. Amazon’s “Maybe you are also interested in: : :”) or via clustering (e.g. connecting all episodes of Friends), it all serves the need for proper navigation through structured information. Also TV-Anytime accommodates these kinds of data structuring via its packaging concept, described in Phase II of the specification. A package is an interconnected structure where each piece of content is referred to by a CRID, which can here be used for several purposes. Besides in- dentifying program instances, CRIDs are also used to define locators, which give the actual location where the content is stored, or for referring to some other set of CRIDs. The TV-Anytime package is thus a structured collection of related CRIDs.
  20. 66 P. Bellekens et al. Fig. 1 Package structure The data model of a package adopts the multi-level structure of the MPEG-21 Digital Item Declaration Language [6], i.e., a container-item-component structure, with some extensions. For example, a language course structured as a package could be organized and divided into chapters and sections where each chapter or section is identified by a CRID. Figure 1 shows an example “Short break in Paris” language learning package consisting of three exercises, where one of them has additional video clips. Each content element is not stored in the package itself, but is referenced using a locator. Thus some part can be distributed on a disc and another via IP. The main content of the language course could for instance be on a disc that the user has bought, while extra interactive content and trailer for the next course may be distributed via IP. This packaging structure is very dynamic since parts can easily be modified or extended, for example the course could be extended with a new chapter by simply adding a CRID reference. Since packages are complex collections of CRIDs, they need to be resolved to discover which items are contained, as well as to get the locator(s) when viewing the actual content. This resolving process is also performed by the previously described CRID Authority. The response of such resolving request is an XML document containing a list of all CRIDs and locators in which it resolved. Like can be seen in the figure, one object can reside in multiple locations, such that in a certain situation the most appropriate content location in terms of availability, quality, connection speed, etc. can be chosen. Semantically Enriched Content Model The content metadata previously described, is fundamental for searching and fil- tering of content. However, we imagine that due to the potentially vast amount of content, it is not enough to simply describe and classify the content, there must also be more intelligent ways of handling it. We therefore propose adding semantic



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