ABSTRACT
Since the first introduced Collaborative Filtering Recommenders (CFR) there have been many attempts to improve their performance by enhancing the prediction accuracy. Even though rating prediction is the prevailing paradigm in CFR, there are other issues which have gained significant attention with respect to the content and its variety. Coverage, which constitutes the degree to which recommendations cover the set of available items, is an important factor along with diversity of the items proposed to an individual, often measured by an average dissimilarity between all pairs of recommended items. In this paper, we argue that coverage and diversity cannot be effectively addressed by conventional CFR with pure similarity-based neighborhood creation processes, especially in sparse datasets. Motivated by the need for including wider content characteristics, we propose a novel neighbor selection technique which emphasizes on variety in preferences (to cover polyphony in selection). Our approach consists of a new metric, named "Exploriometer", which acts as a personality trait for users based on their rating behavior. We favor users who are explorers in order to increase polyphony, and subsequently coverage and diversity; but we still select similar users when we create neighborhoods as a solid basis in order to keep accuracy levels high. The proposed approach has been experimented by two real-world datasets (MovieLens and Yahoo! Music ) with coverage, diversity and accuracy aware recommendations extracted by both traditional CFR and CFR enhanced with our neighborhood creation process. We also introduce a new metric, inspired by the Pearson Correlation Coefficient, to estimate the diversity of recommended items. The derived results demonstrate that our neighbor selection technique can enhance coverage and diversity of the recommendations, especially on sparse datasets.
- Su, X., & Khoshgoftaar, T. M. A survey of collaborative filtering techniques. . Advances in Artificial Intelligence. 2009. Google ScholarDigital Library
- Linden, G., B. Smith, and J. York. Amazon.com Recommendations: Item-to-Item Collaborative Filtering. IEEE Internet Computing. Jan.-Feb. 2003. Google ScholarDigital Library
- Sean M. McNee, J. R. and Konstan, J. A. Accurate is not always good: How accuracy metrics have hurt recommender systems. In proceedings of the ACM WebKDD Workshop. 2006.Google Scholar
- Bobadilla, J., Hernando, A., Ortega, F., & Bernal, J. A framework for collaborative filtering recommender systems. Expert Systems with Applications, 38, 14609--14623. http://dx.doi.org/10.1016/j.eswa.2011.05.021. 2011. Google ScholarDigital Library
- Ge, M., Delgado-Battenfeld, C., & Jannach, D. Beyond accuracy: evaluating recommender systems by coverage and serendipity. In Proceedings of the fourth ACM conference on Recommender systems (pp. 257--260). ACM. 2010, September. Google ScholarDigital Library
- Brynjolfsson, E., Hu. Y J., and Simester, D. Goodbye Pareto Principle, Hello Long Tail: The effect of Search Costs on the Concetration of Product Sales. Net Institute Working Paper. 2007.Google Scholar
- Fleder, D. and Hosanagar, K. Blockbuster Culture's Next Rise or Fall: The Impact of Recommender Systems on Sales Diversity. Management Science, 55(5), pp. 697--712. 2009. Google ScholarDigital Library
- Ziegler, C., McNee, S. M., Konstan, J. A., and Lausen, G. Improving recommendation lists through topic diversification. In Proceedings of the 14th International Conference on World Wide Web. 22--32. 2005. Google ScholarDigital Library
- Grar, Miha, et al. Data sparsity issues in the collaborative filtering framework. Springer Berlin Heidelberg. s.l. : Springer Berlin Heidelberg, 2006.Google Scholar
- Wilson, D. C., Smyth, B., & Sullivan, D. O. Sparsity reduction in collaborative recommendation: A case-based approach. International journal of pattern recognition and artificial intelligence, 17(05), 863--884. 2003.Google Scholar
- Balabanovic, M., & Shoham, Y. Fab: content-based, collaborative recommendation. . Communications of the ACM, 40(3), 66--72. 1997. Google ScholarDigital Library
- Billsus, D., & Pazzani, M. J. User modeling for adaptive news access. User modeling and user-adapted interaction, 10(2--3), 147--180. 2000. Google ScholarDigital Library
- Palit, G. P. and Taillie, C. Diversity as a concept and its measurements. J. Amer. Statist. Assoc. 77,379, 548--561. 1982.Google Scholar
- Hyndman, Rob J. Koehler, Anne B. Another look at measures of forecast accuracy. International Journal of Forecasting: 679--688. doi:10.1016/j.ijforecast.2006.03.001. 2006.Google Scholar
- D. Goldberg, D. Nichols, B. M. Oki, and D. Terry. Using collaborative filtering to weave an information tapestry. Communications of the ACM. 1992, 35(12):61--70. Google ScholarDigital Library
- Rich, E. User modeling via stereotypes. Cognitive science. 1979, Vols. 3(4):329--354.Google Scholar
- Desrosiers, Christian, and George Karypis. A comprehensive survey of neighborhood-based recommendation methods. Recommender systems handbook. Springer US, 2011, Vols. 107--144.Google Scholar
- Koren, Y., & Bell, R. Advances in collaborative filtering. In Recommender Systems Handbook (pp. 145--186). Springer US, 2011.Google ScholarCross Ref
- Said, A., Jain, B. J., & Albayrak, S. Analyzing weighting schemes in collaborative filtering: cold start, post cold start and power users. In Proceedings of the 27th Annual ACM Symposium on Applied Computing (pp. 2035--2040). ACM, 2012. Google ScholarDigital Library
- Herlocker, J. L., Konstan, J. A., Borchers, A., & Riedl, J. An algorithmic framework for performing collaborative filtering. In Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval (pp. 230--237). ACM, 1999. Google ScholarDigital Library
- Jannach, D., Lerche, L., Gedikli, F., & Bonnin, G. What recommenders recommend-an analysis of accuracy, popularity, and sales diversity effects. In User Modeling, Adaptation, and Personalization (pp. 25--37). Springer Berlin Heidelberg, 2013.Google Scholar
- A. Ghose, P. Ipeirotis, and B. Li. Designing ranking systems for hotels on travel search engines by mining user-generated and crowd-sourced content. Marketing Science. 2012. Google ScholarDigital Library
- Said, A., Kille, B., Jain, B. J., & Albayrak, S. Increasing diversity through furthest neighbor-based recommendation. . Proceedings of the WSDM, 12. 2012.Google Scholar
- Said, A., Fields, B., Jain, B. J., & Albayrak, S. User-centric evaluation of a k-furthest neighbor collaborative filtering recommender algorithm. . In Proceedings of the 2013 conference on Computer supported cooperative work (pp. 1399--1408). ACM, 2013. Google ScholarDigital Library
- Anderson, C. The long tail. New York: Hyperion. 2006.Google Scholar
- Resnick, P., N. Iakovou, M. Sushak, P. Bergstrom, and J. Riedl. GroupLens: An open architecture for collaborative filtering of netnews. In Proceedings of the 1994 Computer Supported Cooperative Work Conference. 1994. Google ScholarDigital Library
- Sarwar, B., G. Karypis, J. Konstan, and J. Riedl. Item-based Collaborative Filtering Recommendation Algorithms. In Proc. of the 10th International WWW Conference,. 2001. Google ScholarDigital Library
Index Terms
- Exploriometer: Leveraging Personality Traits for Coverage and Diversity Aware Recommendations
Recommendations
Relevance Meets Coverage: A Unified Framework to Generate Diversified Recommendations
Regular Papers, Survey Papers and Special Issue on Recommender System BenchmarksCollaborative filtering (CF) models offer users personalized recommendations by measuring the relevance between the active user and each individual candidate item. Following this idea, user-based collaborative filtering (UCF) usually selects the local ...
Incorporating user rating credibility in recommender systems
AbstractThere have been many research efforts aimed at improving recommendation accuracy with Collaborative Filtering (CF). Yet there is still a lack of investigation into the integration of CF algorithms with the analysis of users’ rating behaviors. In ...
Diversity, Serendipity, Novelty, and Coverage: A Survey and Empirical Analysis of Beyond-Accuracy Objectives in Recommender Systems
What makes a good recommendation or good list of recommendations?
Research into recommender systems has traditionally focused on accuracy, in particular how closely the recommender’s predicted ratings are to the users’ true ratings. However, it has been ...
Comments