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Using Agents for Generating Personalized Recommendations of Multimedia Contents

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AI*IA 2013: Advances in Artificial Intelligence (AI*IA 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8249))

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Abstract

Agent-based recommender systems assist users based on their preferences and those of similar users. However, when dealing with multimedia contents they need of: (i) selecting as recommenders those users that have similar profiles and that are reliable in providing suggestions and (ii) considering the effects of the device currently exploited. To address these issues, we propose a multi-agent architecture, called MART, conceived to this aim and based on a particular trust model. Some experimental results are presented to evaluate our proposal, that show MART is more effective, in terms of suggestion quality, than other agent-based recommenders.

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References

  1. Adomaviciu, G., Tuzhilin, A.: Using Data Mining Methods to Build Customer Profiles. Computer 34, 74–82 (2001)

    Article  Google Scholar 

  2. Aldua, M., Sanchez, F., Alvarez, F., Jimenez, D., Menendez, J.M., Cebrecos, C.: System Architecture for Enriched Semantic Personalized Media Search and Retrieval in the Future Media Internet. Communication Magazine 49(3), 144–151 (2011)

    Article  Google Scholar 

  3. Amazon (2013), http://www.amazon.com

  4. Breese, J., Heckerman, D., Kadie, C.: Empirical Analysis of Predictive Algorithms for Collaborative Filtering. In: Proc. 14th Int. Conf. on Uncertainty in Artificial Intel., pp. 43–52. Morgan Kaufmann (1998)

    Google Scholar 

  5. Buccafurri, F., Rosaci, D., Sarné, G.M.L., Palopoli, L.: Modeling Cooperation in Multi-Agent Communities. Cognitive Systems Research 5(3), 171–190 (2004)

    Article  Google Scholar 

  6. Burke, R.D.: Hybrid Recommender Systems: Survey and Experiments. User Modeling and User-Adaptivity Interaction 12(4), 331–370 (2002)

    Article  MATH  Google Scholar 

  7. Caire, G.: LEAP 3.0: User Guide, TLAB

    Google Scholar 

  8. CDNOW (2010), http://www.cdnow.com

  9. Dandang (2013), http://www.dandang.com

  10. De Meo, P., Rosaci, D., Sarnè, G.M.L., Terracina, G., Ursino, D.: EC-XAMAS: Supporting e-Commerce Activities by an XML-Based Adaptive Multi-Agent System. Applied Artificial Intelligence 21(6), 529–562 (2007)

    Article  Google Scholar 

  11. eBay (2013), http://www.ebay.com

  12. Garruzzo, S., Rosaci, D.: The Roles of Reliability and Reputation in Competitive Multi Agent Systems. In: Meersman, R., Dillon, T.S., Herrero, P. (eds.) OTM 2010, Part I. LNCS, vol. 6426, pp. 326–339. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  13. Greenstette, G.: Explorations in Authomatic Thesaurus Construction. Kluwer (1994)

    Google Scholar 

  14. Huynh, T.D., Jennings, N.R., Shadbolt, N.R.: An Integrated Trust and Reputation Model for Open Multi-Agent System. Autonmous Agent and Multi Agent Systems 13 (2006)

    Google Scholar 

  15. Jacobsson, M., Rost, M., Holmquist, L.H.: When Media Gets Wise: Collaborative Filtering with Mobile Media Agents. In: Proc. 11th Int. Conf. on Intel. User Interfaces, pp. 291–293. ACM (2006)

    Google Scholar 

  16. Jogalekar, P., Woodside, M.: Evaluating the Scalability of Distributed Systems. IEEE Trans. on Parallel Distributed Sys. 11(6), 589–603 (2000)

    Article  Google Scholar 

  17. Kamvar, S., Schlosser, M., Garcia-Molina, H.: The Eigentrust Algorithm for Reputation Management in P2P Networks. In: Proc. 12th Conf. WWW, pp. 640–651. ACM (2003)

    Google Scholar 

  18. Kim, J.K., Kim, H.K., Cho, Y.H.: A User-Oriented Contents Recommendation System in Peer-to-Peer Architecture. Expert Sys. with App. 34(1), 300–312 (2008)

    Article  Google Scholar 

  19. Kim, Y.S., Yum, B.J., Song, J., Kim, S.M.: Development of a Recommender System Based on Navigational and Behavioral Patterns of Customers in e-Commerce Sites. Expert Sys. with App. 28, 381–393 (2005)

    Article  Google Scholar 

  20. Lax, G., Sarné, G.M.L.: CellTrust: A Reputation Model for C2C Commerce. Electronic Commerce Research 8(4), 193–216 (2006)

    Article  Google Scholar 

  21. Lops, P., Gemmis, M., Semeraro, G.: Content-based Recommender Systems: State of the Art and Trends. In: Recommender Systems Handbook, pp. 73–105. Springer (2011)

    Google Scholar 

  22. Macskassy, S.A., Dayanik, A.A., Hirsh, H.: Information Valets for Intelligent Information Access. In: Proc. AAAI Spring Symp. Series on Adaptive User Interfaces. AAAI (2000)

    Google Scholar 

  23. Melville, P., Mooney, R.J., Nagarajan, R.: Content-boosted Collaborative Filtering for Improved Recommendations. In: Proc. 18th National Conf. on AI, pp. 187–192. AAAI (2002)

    Google Scholar 

  24. O’Donovan, J., Smyth, B.: Trust in Recommender Systems. In: Proc of 10th Int. Conf. on Intelligent User Interfaces, pp. 167–174. ACM (2005)

    Google Scholar 

  25. Olson, T.: Bootstrapping and Decentralizing Recommender Systems. Ph.D. Thesis, Dept. of Information Technology, Uppsala Univ. (2003)

    Google Scholar 

  26. Palopoli, L., Rosaci, D., Sarné, G.M.L.: A Multi-Tiered Recommender System Architecture for Supporting E-Commerce. In: Fortino, G., Badica, C., Malgeri, M., Unland, R. (eds.) Intelligent Distributed Computing VI. SCI, vol. 446, pp. 71–80. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  27. Palopoli, L., Rosaci, D., Sarné, G.M.L.: Introducing Specialization in e-Commerce Recommender Systems. Concurrent Engineering: Research and Applications 21(3), 187–196 (2013)

    Article  Google Scholar 

  28. Parsons, J., Ralph, P., Gallagher, K.: Using Viewing Time to Infer User Preference in Recommender Systems. In: AAAI Work. Semantic Web Personalization, pp. 52–64. AAAI (2004)

    Google Scholar 

  29. Pu, P., Chen, L., Hu, R.: Evaluating Recommender Systems from the User’s Perspective: Survey of the State of the Art. UMUAI 22(4), 317–355 (2012)

    Google Scholar 

  30. Ramchurn, S.D., Huynh, D., Jennings, N.R.: Trust in Multi-Agent Systems. Knowledge Engeenering Review 19(1), 1–25 (2004)

    Google Scholar 

  31. Rosaci, D., Sarnè, G.M.L.: MASHA: A Multi-Agent System Handling User and Device Adaptivity of Web Sites. User Modeling User-Adaptivity Interaction 16(5), 435–462 (2006)

    Article  Google Scholar 

  32. Rosaci, D., Sarnè, G.M.L.: Efficient Personalization of e-Learning Activities Using a Multi-Device Decentralized Recommender System. Computational Intelligence 26(2), 121–141 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  33. Rosaci, D., Sarnè, G.M.L.: EVA: An Evolutionary Approach to Mutual Monitoring of Learning Information Agents. Applied Artificial Intelligence 25(5), 341–361 (2011)

    Article  Google Scholar 

  34. Rosaci, D., Sarnè, G.M.L.: A Multi-Agent Recommender System for Supporting Device Adaptivity in e-Commerce. Journal of Intelligent Information System 38(2), 393–418 (2012)

    Article  Google Scholar 

  35. Rosaci, D., Sarnè, G.M.L.: Cloning Mechanisms to Improve Agent Performances. Journal of Network and Computer Applications 36(1), 402–408 (2012)

    Article  Google Scholar 

  36. Rosaci, D., Sarnè, G.M.L.: Recommending Multimedia Web Services in a Multi-Device Environment. Information Systems 38(2) (2013)

    Google Scholar 

  37. Rosaci, D., Sarné, G.M.L.: REBECCA: A Trust-Based Filtering to Improve Recommendations for B2C e-Commerce. In: Zavoral, F., Jung, J.J., Badica, C. (eds.) Intelligent Distributed Computing VII. SCI, vol. 511, pp. 31–36. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  38. Rosaci, D., Sarnè, G.M.L., Garruzzo, S.: MUADDIB: A Distributed Recommender System Supporting Device Adaptivity. ACM Transansacion on Information Systems 27(4) (2009)

    Google Scholar 

  39. Rosaci, D., Sarné, G.M.L., Garruzzo, S.T.: An integrated Reliability-Reputation Model for Agent Societies. In: Proceedings of 12th Workshop dagli Oggetti agli Agenti, WOA 2011. CEUR Workshop Proceedings, vol. 741. CEUR-WS.org (2011)

    Google Scholar 

  40. Rosaci, D., Sarnè, G.M.L., Garruzzo, S.: Integrating Trust Measures in Multiagent Systems. International Journal of Intelligent Systems 27(1), 1–15 (2012)

    Article  Google Scholar 

  41. Sabater, J., Sierra, C.: Reputation in Gregarious Societies. In: Proc. 5th Int. Conf. on Autonomous Agents, pp. 194–195. ACM (2001)

    Google Scholar 

  42. Sabater-Mir, J., Paoulucci, M.: On Open Representation and Aggregation of Social Evaluationa in Computational Trust and Reputation Models. International Journal of Approximate Reasoning 46(3), 458–483 (2007)

    Article  Google Scholar 

  43. Sullivan, D.O., Smyth, B., Wilson, D.C., McDonald, K., Smeaton, A.: Improving the Quality of the Personalized Electronic Program Guide. UMUAI 14, 5–36 (2004)

    Google Scholar 

  44. JADE (2012), http://www.jade.tilab.org

  45. Wang, J., Pouwelse, J., Lagendijk, R.L., Reinders, M.J.T.: Distributed Collaborative Filtering for Peer-to-Peer File Sharing Systems. In: Proc. 2006 ACM Symp. on Applied Computing, pp. 1026–1030. ACM (2006)

    Google Scholar 

  46. Yoshii, K., Goto, M., Komatani, K., Ogata, T., Okuno, H.G.: An Efficient Hybrid Music Recommender System Using an Incrementally Trainable Probabilistic Generative Model. IEEE Trans. on Audio, Speech, and Language Processing 16(2), 435–447 (2008)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. DIIES, University “Mediterranea” of Reggio Calabria, Via Graziella, Feo di Vito, 89122, Reggio Calabria, RC, Italy

    Domenico Rosaci

  2. DICEAM, University “Mediterranea” of Reggio Calabria, Via Graziella, Feo di Vito, 89122, Reggio Calabria, RC, Italy

    Giuseppe M. L. Sarné

Authors
  1. Domenico Rosaci
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  2. Giuseppe M. L. Sarné
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Editor information

Editors and Affiliations

  1. Dipartimento di Informatica, Università degli Studi di Torino, via Pessinetto 12, 10149, Torino, Italy

    Matteo Baldoni , Cristina Baroglio , Guido Boella  & Roberto Micalizio , ,  & 

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Rosaci, D., Sarné, G.M.L. (2013). Using Agents for Generating Personalized Recommendations of Multimedia Contents. In: Baldoni, M., Baroglio, C., Boella, G., Micalizio, R. (eds) AI*IA 2013: Advances in Artificial Intelligence. AI*IA 2013. Lecture Notes in Computer Science(), vol 8249. Springer, Cham. https://doi.org/10.1007/978-3-319-03524-6_35

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  • DOI: https://doi.org/10.1007/978-3-319-03524-6_35

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-03523-9

  • Online ISBN: 978-3-319-03524-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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