Davide Ronca
ABSTRACT
Identifying the products to suggest to the target customer in order to best fit his profile of interests is a challenging task in the Social Commerce domain. In this paper we investigate the application of different model-based Collaborative Filtering (CF) techniques and we propose a framework able to provide different suggestions to the customers. The framework is aimed at expanding the customer suggestions in order to guarantee a serendipitous discovery of wished products and at the same time, it is aimed at assisting merchants in discovering potential interests of their customers. We compare several approaches by means of Epinions, MovieLens and Poste Italiane dataset (with real customers). Experimental results show an increased value of coverage of the recommendations provided by our approach without affecting recommendation quality. Moreover, personalized strategy guarantees a high level recommendation for different user navigation behaviors.
- G. Adomavicius and A. Tuzhilin. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. Knowledge and Data Engineering, IEEE Transactions on, 17(6):734--749, June 2005. Google Scholar
Digital Library
- R. Agrawal, T. Imieliński, and A. Swami. Mining association rules between sets of items in large databases. In SIGMOD '93: Proc. of the 1993 ACM SIGMOD Int. Conf. on Management of data, pages 207--216, New York, NY, USA, 1993. ACM. Google ScholarDigital Library
- C. Birtolo, D. De Chiara, M. Ascione, and R. Armenise. A generative approach to product bundling in the e-Commerce domain. In Nature and Biologically Inspired Computing (NaBIC), 2011 Third World Congress on, pages 169--175, oct. 2011.Google ScholarCross Ref
- C. Birtolo, D. Ronca, R. Armenise, and M. Ascione. Personalized suggestions by means of collaborative filtering: A comparison of two different model-based techniques. In NaBIC, pages 444--450. IEEE, 2011.Google ScholarCross Ref
- C. Birtolo, D. Ronca, and G. Aurilio. Trust-aware clustering collaborative filtering: Identification of relevant items. In Artificial Intelligence Applications and Innovations, volume 381 of IFIP Advances in Information and Communication Technology, pages 374--384. Springer Berlin Heidelberg, 2012.Google ScholarCross Ref
- R. D. Burke. Hybrid web recommender systems. In P. Brusilovsky, A. Kobsa, and W. Nejdl, editors, The Adaptive Web, volume 4321 of Lecture Notes in Computer Science, pages 377--408. Springer, 2007. Google ScholarDigital Library
- D. Goldberg, D. Nichols, B. M. Oki, and D. Terry. Using collaborative filtering to weave an information tapestry. Commun. ACM, 35:61--70, December 1992. Google ScholarDigital Library
- S. Gong. A flexible electronic commerce recommendation system. Physics Procedia, 24, Part B(0):806--811, 2012.Google Scholar
- J. L. Herlocker, J. A. Konstan, A. Borchers, and J. Riedl. Explaining collaborative filtering recommendations. In Proceedings of the ACM 2000 Conference on Computer Supported Cooperative Work, pages 241--250, December 2000. Google ScholarDigital Library
- J. L. Herlocker, J. A. Konstan, L. G. Terveen, and J. T. Riedl. Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems, 22(1):5--53, January 2004. Google ScholarDigital Library
- C. Huang and J. Yin. Effective association clusters filtering to cold-start recommendations. In Fuzzy Systems and Knowledge Discovery (FSKD), 2010 Seventh Int. Conf. on, volume 5, pages 2461--2464, August 2010.Google ScholarCross Ref
- B. A. Huberman, E. Adar, and L. R. Fine. Valuating privacy. IEEE Security and Privacy, 3:22--25, 2005. Google ScholarDigital Library
- S. Kitisin and C. Neuman. Reputation-based trust-aware recommender system. In Securecomm and Workshops, 2006, pages 1--7, 28 2006-sept. 1 2006.Google ScholarCross Ref
- G. Linden, B. Smith, and J. York. Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing, 7(1):76--80, 2003. Google ScholarDigital Library
- B. Liu and Z. Yuan. Incorporating social networks and user opinions for collaborative recommendation: local trust network based method. In Proc. of the Workshop on Context-Aware Movie Recommendation, CAMRa10, pages 53--56, New York, USA, 2010. ACM. Google ScholarDigital Library
- P. Massa and P. Avesani. Trust-aware recommender systems. In Proceedings of the 2007 ACM conference on Recommender systems, RecSys '07, pages 17--24, New York, NY, USA, 2007. ACM. Google ScholarDigital Library
- P. Resnick, N. Iacovou, M. Suchak, P. Bergstorm, and J. Riedl. Grouplens: An open architecture for collaborative filtering of netnews. In Proc. of ACM 1994 Conference on Computer Supported Cooperative Work, pages 175--186. ACM, 1994. Google ScholarDigital Library
- H.-F. Wang and C.-T. Wu. A strategy-oriented operation module for recommender systems in e-commerce. Comput. Oper. Res., 39(8):1837--1849, Aug. 2012. Google ScholarDigital Library
- W. Wang. Recommendation strategies analysis in e-commerce. In Information Science and Engineering (ICISE), 2010 2nd International Conference on, pages 1394--1397, dec. 2010.Google ScholarCross Ref
- K. Wei, J. Huang, and S. Fu. A survey of e-commerce recommender systems. In Service Systems and Service Management, 2007 Int. Conf. on, pages 1--5, june 2007.Google ScholarCross Ref
Index Terms
- A flexible framework for context-aware recommendations in the social commerce domain
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