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A holistic approach for personalization, relevance feedback & recommendation in enriched multimedia content

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Abstract

As the prerequisites of production houses, broadcasters, advertising agencies and online publishing companies for enriched multimedia content increase rapidly, the need of innovative methods for the effective creation of enriched multimedia content is undeniable. Stemming from this need, in this paper we focus on the design, development and evaluation of a framework consisting of personalization, relevance feedback and recommendation mechanisms, as a principal method for the creation of enriched multimedia content targeted to each user’s needs, preferences and interests. As the multimedia content proliferates along with its consumption by the users, more effective ways of presenting it to the viewers are demanded in order to facilitate them with the multimedia content search and selection and improve their Quality of Experience (QoE). The main contribution of the paper is the introduction of a holistic framework that offers personalized enriched multimedia content, by extending the recommendation process to the set of enrichments that accompany the video except from the video itself and by collecting explicit and implicit relevance feedback from the interactions of the user with both the video and its enrichments. We evaluate the proposed framework following a two-step approach. Firstly, we perform extended experiments by applying reasonably simulated user interactions, in order to calibrate its parameters that refer to multiple aspects of the enriched multimedia content, aiming at high performance in terms of QoE. Here, most importantly, we have shown that appropriately designing the enrichments and considering users’ interactions with them allows for achieving a better quality in inferring users’ profiles in many realistic cases. Secondly, we integrated our proposed recommender framework within the MECANEX streaming platform in order to perform user studies about its usability within a realistic environment of use.

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References

  1. Balabanovic M, Shoham Y (1997) Content-based, collaborative recommendation. Commun ACM 40(3):66–72

    Article  Google Scholar 

  2. Bobadilla J, Ortega F, Hernando A, Gutierrez A (2013) Recommender systems survey. knowledge-based systems. Elsevier 46:109–132

    Google Scholar 

  3. Cha S-H (2007) Comprehensive survey on distance/similarity measures between probability density functions. International Journal of Mathematical Models and Methods in Applied Sciences 1(4):300–307

    Google Scholar 

  4. Gauch S, Speretta M, Chandramouli A, Micarelli A (2007) User profiles for personalized information access. pp. 54–89, Book: The adaptive web, lecture notes in computer Science, Springer, Berlin Heidelberg

  5. Gentili G, Micarelli A, Sciarrone F (2003) Infoweb: an adaptive information filtering system for the cultural heritage domain. Applied artificial intelligence: An International Journal, Taylor & Francis 17(8–9)

  6. Hanani U, Shapira B, Shoval P (2001) Information filtering: overview of issues, research and systems. Journal of User Modeling and User-Adapted Interaction, Kluwer Academic Publishers 11(3):203–259

  7. Hopfgartner F, Jose J (2007) Evaluating the implicit feedback models for adaptive video retrieval. MIR, September 2007

  8. http://www.digitaltrends.com/social-media/why-consumers-are-increasingly-willing-to-trade-data-for-personalization/#ixzz2g8dgrqko. Last accessed 15 May 2016

  9. Introduction to Information Retrieval Evaluation (2013) http://nlp.stanford.edu/IR-book/html/htmledition/irbook.html, Stanford University, 21 Apr 2013. Retrieved 4 Sept 2015

  10. Kobsa A (2001) Generic user modeling systems. Journal of User Modeling and User-Adapted Interaction 11(1–2):49–63Kluwer Academic Publishers

    Article  MATH  Google Scholar 

  11. Lior R, Maimon O (2005) Clustering methods. Data mining and knowledge discovery handbook, Springer US p 321–352

  12. Manning CD, Raghavan P, Schutze H (2012) Scoring, term weighting, and the vector space model. Book: Introduction to information retrieval, Cambridge University Press p 100–123

  13. Meteren R, Someren M (2000) Using content-based filtering for recommendation. In Proc. of ECML 2000 workshop: Maching Learning in Information Age, pp. 47–56

  14. Micarelli A, Sciarrone F (2004) Anatomy and empirical evaluation of an adaptive web-based information filtering system. Journal of User Modeling and User-Adapted Interaction 14:159–200Kluwer Academic Publishers

    Article  Google Scholar 

  15. Normalized Discounted Cumulative Gain (2015) https://www.kaggle.com/wiki/NormalizedDiscountedCumulativeGain. Retrieved 4 Sept 2015

  16. Online shopping personalization - statistics and trends available at http://www.invespcro.com/blog/online-shopping-personalization/. Accessed 15 May 2016

  17. Pazzani M (1999) A framework for collaborative, content-based, and demographic filtering. Artificial Intelligence Review-Special issue on Data Mining on the Internet, Kluwer Academic Publishers 13:393–408

  18. Pazzani M, Billsus D (1997) Learning and revising user profiles: the identification of interesting web sites. Mach Learn 27(3):313–331Kluwer Academic Publishers

    Article  Google Scholar 

  19. Porcel C, Tejeda-Lorente A, Martinez MA, Herrera-Viedma E (2012) A hybrid recommender system for the selective dissemination of research resources in a technology transfer office. Information Sciences, Elsevier 184(1):13–32

    Article  MATH  Google Scholar 

  20. Salton G, McGill M (1983) Introduction to modern information retrieval. McGraw-Hill, New York

    MATH  Google Scholar 

  21. Schafer B, Frankowski D, Herlocker J, Sen S (2007) Collaborative filtering recommender systems. The adaptive web chapter 9, Springer p 291–324

  22. Shani G, Gunawardana A (2009) Evaluating recommendation systems. Technical report, No. MSR-TR-2009-159

  23. Shoppers want marketing e-mails tailored to their preferences available at https://www.internetretailer.com/2015/01/08/shoppers-want-marketing-e-mails-tailored-their-preferences. Accessed 15 May 2016

  24. The LinkedTV project (2016) available at http://www.linkedtv.eu/, Last accessed 30 May 2016

  25. The MECANEX project available at http://www.mecanex.eu. Last accessed 15 May 2016

  26. The NoTube project available at https://notube.tv/, Last accessed 30 May 2016

  27. Tsiropoulou EE, Stai E, Kafetzoglou S, Rapantzikos K, Papavassiliou S, Zwicklbauer M, van Leeuwen P, Karampali V (2015) Multimedia content annotations for rapid exploitation in multi-screen environments: The MECANEX project vision. IEEE STCSN E-letter, Special Issue on: Impact of Social Media on TV Content Consumption: New Market Strategies, Scenarios and Trends. Vol. 3, No. 2, December 2015

  28. Why consumers are increasingly willing to trade data for personalization, available at http://www.infosys.com/newsroom/press-releases/Documents/genome-research-report.pdf. Accessed 15 May 2016

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Authors and Affiliations

  1. Institute of Communications and Computer Systems (ICCS), National Technical University of Athens (NTUA), Athens, Zografou, 15780, Greece

    Eleni Stai, Stella Kafetzoglou, Eirini Eleni Tsiropoulou & Symeon Papavassiliou

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  1. Eleni Stai
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  2. Stella Kafetzoglou
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  3. Eirini Eleni Tsiropoulou
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  4. Symeon Papavassiliou
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Correspondence to Eleni Stai.

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Stai, E., Kafetzoglou, S., Tsiropoulou, E.E. et al. A holistic approach for personalization, relevance feedback & recommendation in enriched multimedia content. Multimed Tools Appl 77, 283–326 (2018). https://doi.org/10.1007/s11042-016-4209-1

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  • DOI: https://doi.org/10.1007/s11042-016-4209-1

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