Alan Said
Brijnesh J. Jain
ABSTRACT
Collaborative filtering recommender systems provide their users with relevant items based on information from other similar users. Popular collaborative filtering approaches such as Pearson correlation coefficient and cosine similarity, compute the similarity between users based on the set of their co-rated items. However, similarities are commonly computed without taking the popularity of the set of two users' co-rated items into consideration, e.g. an item rated by very many users should have less impact on the similarity measure, and analogously an item rated by few should have a larger impact on the similarity score of two users. In this paper, we investigate the effects of common weighting schemes on different types of users, i.e. new users with few ratings (so-called cold start users), post cold start users, and power users. Empirical studies over two datasets have shown in which of these cases weighting schemes are beneficial in terms of recommendation quality.
- Linas Baltrunas, 'Dynamic item weighting and selection for collaborative filtering', in Web mining 2.0 Workshop, ECML-PKDD 2007, (2007).Google Scholar
- J S Breese, D Heckerman, and C Kadie, Empirical analysis of predictive algorithms for collaborative filtering, volume 461, 43--52, San Francisco, CA, 1998. Google ScholarDigital Library
- Jonathan L. Herlocker, Joseph A. Konstan, Al Borchers, and John Riedl, 'An algorithmic framework for performing collaborative filtering', in Proc. of the 22nd annual Intl. ACM SIGIR conference on Research and development in information retrieval - SIGIR '99, volume 54, pp. 230--237, New York, New York, USA, (1999). ACM Press. Google ScholarDigital Library
- Jonathan L. Herlocker, Joseph A. Konstan, Loren G. Terveen, and John T. Riedl, 'Evaluating collaborative filtering recommender systems', ACM Trans. Inf. Syst., 22, 5--53, (January 2004). Google ScholarDigital Library
- Dietmar Jannach, Markus Zanker, Alexander Felfernig, and Gerhard Friedrich, Recommender Systems: An Introduction, volume 40, Cambridge University Press, 2010. Google ScholarDigital Library
- Rong Jin, Joyce Y. Chai, and Luo Si, 'An automatic weighting scheme for collaborative filtering', in Proc. of the 27th annual Intl. conference on Research and development in information retrieval - SIGIR '04, p. 337, New York, New York, USA, (2004). ACM Press. Google ScholarDigital Library
- YoungOk Kwon, 'Improving top-n recommendation techniques using rating variance', in Proc. of the 2008 ACM conference on Recommender systems, RecSys '08, pp. 307--310, New York, NY, USA, (2008). ACM. Google ScholarDigital Library
- Heng Luo, Changyong Niu, Ruimin Shen, and Carsten Ullrich, 'A collaborative filtering framework based on both local user similarity and global user similarity', Machine Learning, 72(3), 231--245, (July 2008). Google ScholarDigital Library
- MovieLens. MovieLens 10M/100k Data Set README. http://www.grouplens.org/system/files/README_10M100K.html (retrieved April, 2011).Google Scholar
- István Pilászy and Domonkos Tikk, 'Recommending new movies: even a few ratings are more valuable than metadata', in Proc. of the third ACM conference on Recommender systems, RecSys '09, pp. 93--100, New York, NY, USA, (2009). ACM. Google ScholarDigital Library
- A. Said, S. Berkovsky, and E. W. De Luca, 'Putting things in context: Challenge on context-aware movie recommendation', in Proc. of the Workshop on Context-Aware Movie Recommendation, CAMRa '10, New York, NY, USA, (2010). ACM. Google ScholarDigital Library
- Panagiotis Symeonidis, Alexandros Nanopoulos, and Yannis Manolopoulos, 'Feature-weighted user model for recommender systems', in Proc. of the 11th Intl. conference on User Modeling, UM '07, Berlin, Heidelberg, (2007). Springer-Verlag. Google ScholarDigital Library
- R. Wetzker, C. Zimmermann, and C. Bauckhage, 'Analyzing social bookmarking systems: A del.icio.us cookbook', in Mining Social Data (MSoDa) Workshop Proc. ECAI 2008, (July 2008).Google Scholar
- Kai Yu, Xiaowei Xu, Martin Ester, and Hans-Peter Kriegel, 'Feature Weighting and Instance Selection for Collaborative Filtering: An Information-Theoretic Approach', Knowledge and Information Systems, 5(2), 201--224, (April 2003). Google ScholarDigital Library
Index Terms
- Analyzing weighting schemes in collaborative filtering: cold start, post cold start and power users
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