Abstract
This paper presents an approach to build a TV recommendation system called PersonalTV that enables the use of multiple classifiers, each one specialized on selected attributes of detailed program information. For generating adequate recommendations, the system makes use of content filtering and the preferences directly specified by the user within an MPEG-7 profile. By tracking user actions and interpreting their semantics, the system is able to individually weight these actions and dynamically adjusts the process to the user’s evolving preferences. We show how specialized spam fighting methods can successfully be transferred to the area of recommendation systems and adapted accordingly. Being lightweight, these methods are especially applicable in resource-constrained environments such as TV set-top boxes or mobile devices. Moreover, the use of the variance of the beta-distribution as a confidence value for each recommendation is presented.
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Notes
TV Guide Plus+ - http://www.europe.guideplus.com/
TV Movie - http://www.tvmovie.de/
TiVo - http://www.tivo.com/
MY Personal TV Digital - http://mypersonaltvdigital.net/
XMLTV—http://www.xmltv.org/
MythTV—http://www.mythtv.org/
MeediOS—http://www.meedios.com/
SageTV—http://www.sagetv.com/
Video Disk Recorder—http://www.cadsoft.de/vdr/
International Movie Database—http://www.imdb.com/
TV250—http://www.tv250.de/
Eplists—http://eplists.constabel.net/
TV Addicted—http://www.tvaddicted.de/
References
Ali K, van Stam W (2004) Tivo: making show recommendations using a distributed collaborative filtering architecture. In: KDD ’04: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, New York, pp 394–401
Bär A, Berger A, Egger S, Schatz R (2008) A lightweight mobile tv recommender. In: Tscheligi M, Obrist M, Lugmayr A (eds) EuroITV. Lecture notes in computer science, vol 5066. Springer, New York, pp 143–147
Bernhaupt R, Wilfinger D, Weiss A, Tscheligi M (2008) An ethnographic study on recommendations in the living room: implications for the design of itv recommender systems. In: EuroITV, pp 92–101
Cleland-Huang J, Mobasher B (2008) Using data mining and recommender systems to scale up the requirements process. In: ULSSIS ’08: Proceedings of the 2nd international workshop on Ultra-large-scale software-intensive systems. ACM, New York, pp 3–6
European Telecommunications Standards Institute (2006) ETSI TS 102 822-3-1: broadcast and on-line services: search, select, and rightful use of content on personal storage systems (“TV-Anytime”); Part 3: metadata; Sub-part 1: phase 1 - metadata schemas. European Telecommunications Standards Institute, Sophia Antipolis
European Telecommunications Standards Institute (2008) ETS ES 300 468: digital video broadcasting (DVB); specification for service information (SI) in DVB systems . European Telecommunications Standards Institute, Sophia Antipolis
Fisher RA (1954)Statistical methods for research workers. Oliver and Boyd, Edinburgh
Garfinkel R, Gopal R, Pathak B, Yin F (2008) Shopbot 2.0: integrating recommendations and promotions with comparison shopping. Decis Support Syst 46(1):61–69
Gotardo RA, Teixeira CAC, Zorzo SD (2008) Ip2 model—content recommendation in web-based educational systems using user’s interests and preferences and resources’ popularity. In: COMPSAC ’08: proceedings of the 2008 32nd annual IEEE international computer software and applications conference. IEEE Computer Society, Washington, DC, pp 460–463
Graham P (2002) A Plan for spam. http://www.paulgraham.com/spam.html
Graham P (2003) Better Bayesian filtering. http://www.paulgraham.com/better.html
Graham P (2004) Hackers and painters: big ideas from the computer age. O’Reilly Media, Sebastopol
Gude M, Grünvogel SM, Pütz A (2008) Predicting future user behaviour in interactive live tv. In: EuroITV, pp 117–121
ISO/IEC (2004) Information technology—multimedia content description interface—part 5: multimedia description schemes—final proposed draft admendment 2 of iso/iec 15938-5, n6398. http://www.chiariglione.org/mpeg/
Leino J, Räihä K-J (2007) Case amazon: ratings and reviews as part of recommendations. In: RecSys ’07: Proceedings of the 2007 ACM conference on Recommender systems. ACM, New York, pp 137–140
Louis G (2003) Bogofilter calculations: comparing bayes chain rule with fisher’s method for combining probabilities. http://www.bgl.nu/bogofilter/BcrFisher.html
Louis G (2003) Testing bogofilter’s calculation methods. http://www.bgl.nu/bogofilter/test6000.html
Manning CD, Raghavan P, Schütze H (2008) Introduction to information retrieval. Cambridge University Press, New York
Martinez JM (2003) MPEG-7 overview. ISO/IEC JTC1/SC29/WG11, Pattaya
Melville P, Mooney RJ, Nagarajan R (2001) Content-boosted collaborative filtering. In: Proceedings of the 2001 SIGIR workshop on recommender systems
Papanikolaou KA, Mabbott A, Bull S, Grigoriadou M (2006) Designing learner-controlled educational interactions based on learning/cognitive style and learner behaviour. Interact Comput 18(3):356–384
Reimers U (2008) DVB—Digitale Fernsehtechnik Datenkompression und Übertragung. Springer, Berlin
Robinson G (2002) Spam detection. http://radio.weblogs.com/0101454/stories/2002/09/16/spamDetection.html
Robinson G (2003) A statistical approach to the spam problem. Linux J 2003(107):3
Robinson G (2004) Handling redundancy in email token probabilities. http://garyrob.blogs.com/handlingtokenredundancy94.pdf
Salembier P, Smith JR (2002) Overview of multimedia description schemes and schema tools. In: Manjuta BS, Salembier P, Sikora T (eds) Introduction to MPEG-7, chap 6. Wiley, Chichester, pp 83–93
Segaran T (2007) Programming collective intelligence: building Smart Web 2.0 applications. O’Reilly, Sebastopol
Stumpf S, Rajaram V, Li L, Burnett M, Dietterich T, Sullivan E, Drummond R, Herlocker J (2007) Toward harnessing user feedback for machine learning. In: IUI ’07: proceedings of the 12th international conference on Intelligent user interfaces. ACM, New York, pp 82–91
WeißD, Scheuerer J, Wenleder M, Erk A, Gülbahar M, Linnhoff-Popien C (2008) A user profile-based personalization system for digital multimedia content. In: DIMEA ’08: proceedings of the 3rd international conference on digital interactive media in entertainment and arts. ACM, New York, pp 281–288
Zaslow J (2002) If tivo thinks you are gay, here’s how to set it straight. Wall Street J
Zdziarski JA (2005) Ending spam: Bayesian content filtering and the art of statistical language classification. No Starch, San Francisco
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The authors would like to thank epgData.com for providing us with their EPG data.
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Hölbling, G., Pleschgatternig, M. & Kosch, H. PersonalTV. Multimed Tools Appl 46, 259–288 (2010). https://doi.org/10.1007/s11042-009-0352-2
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DOI: https://doi.org/10.1007/s11042-009-0352-2