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The pink curve shows that accuracy monotonically improves with rising k values, as root mean squared error falls from 0.9139 for k = 250 to 0.9002 for k = ∞. We repeated the experiments without using the implicit feedback, that is, dropping the cij parameters from our model

Factorization meets the neighborhood: a multifaceted collaborative filtering model

KDD, pp.426-434, (2008)

Cited by: 3552|Views391
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Abstract

Recommender systems provide users with personalized suggestions for products or services. These systems often rely on Collaborating Filtering (CF), where past transactions are analyzed in order to establish connections between users and products. The two more successful approaches to CF are latent factor models, which directly profile bot...More

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Introduction
  • Matching consumers with most appropriate products is not trivial, yet it is a key in enhancing user satisfaction and loyalty.
  • This emphasizes the prominence of recommender systems, which provide personalized recommendations for products that suit a user’s taste [1].
  • Recommender systems are often based on Collaborative Filtering (CF) [10], which relies only on past user behavior—e.g., their previous transactions or product ratings—and does not require the creation of explicit profiles.
  • CF attracted much of attention in the past decade, resulting in significant progress and being adopted by some successful commercial systems, including Amazon [15], TiVo and Netflix
Highlights
  • Recommender systems are often based on Collaborative Filtering (CF) [10], which relies only on past user behavior—e.g., their previous transactions or product ratings—and does not require the creation of explicit profiles
  • Experimental results on the Netflix data with the new neighborhood model are presented in Fig. 1
  • The pink curve shows that accuracy monotonically improves with rising k values, as root mean squared error (RMSE) falls from 0.9139 for k = 250 to 0.9002 for k = ∞. (Notice that since the Netflix data contains 17,770 movies, k = ∞ is equivalent to k =17,770, where all item-item relations are explored.) We repeated the experiments without using the implicit feedback, that is, dropping the cij parameters from our model
  • The new neighborhood model enables us to derive, for the first time, an integrated model that combines the neighborhood and the latent factor models. This is helpful for improving system performance, as the neighborhood and latent factor models address the data at different levels and complement each other
Results
  • Experimental results on the

    Netflix data with the new neighborhood model are presented in Fig. 1.
  • The results depicted by the yellow curve show a significant decline in estimation accuracy, which widens as k grows.
  • This demonstrates the value of incorporating implicit feedback into the model.
  • First is a correlation-based neighborhood model (following (3)), which is the most popular CF method in the literature
  • The authors denote this model as CorNgbr.
  • The authors' new model delivers more accurate results even when compared with WgtNgbr, as long as the value of k is at least 500
Conclusion
  • This work proposed improvements to two of the most popular approaches to Collaborative Filtering.
  • The authors suggested a new neighborhood based model, which unlike previous neighborhood methods, is based on formally optimizing a global cost function.
  • This leads to improved prediction accuracy, while maintaining merits of the neighborhood approach such as explainability of predictions and ability to handle new users without re-training the model.
Tables
  • Table1: Comparison of SVD-based models: prediction accucuracy is improved by combining the complementing neighborracy is measured by RMSE on the Netflix test set for varying hood and latent factor models. Increasing the number of facnumber of factors (f ). Asymmetric-SVD offers practical adtors contributes to accuracy, but also adds to running time
  • Table2: Performance of the integrated model. Prediction ac-
Download tables as Excel
Funding
  • Evaluated our algorithms on the Netflix data of more than 100 million movie ratings performed by anonymous Netflix customers
Reference
  • G. Adomavicius and A. Tuzhilin, “Towards the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions”, IEEE Transactions on Knowledge and Data Engineering 17 (2005), 634–749.
    Google ScholarLocate open access versionFindings
  • R. Bell and Y. Koren, “Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights”, IEEE International Conference on Data Mining (ICDM’07), pp. 43–52, 2007.
    Google ScholarLocate open access versionFindings
  • R. Bell and Y. Koren, “Lessons from the Netflix Prize Challenge”, SIGKDD Explorations 9 (2007), 75–79.
    Google ScholarLocate open access versionFindings
  • R. M. Bell, Y. Koren and C. Volinsky, “Modeling Relationships at Multiple Scales to Improve Accuracy of Large Recommender Systems”, Proc. 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2007.
    Google ScholarLocate open access versionFindings
  • J. Bennet and S. Lanning, “The Netflix Prize”, KDD Cup and Workshop, 2007. www.netflixprize.com.
    Locate open access versionFindings
  • J. Canny, “Collaborative Filtering with Privacy via Factor Analysis”, Proc. 25th ACM SIGIR Conf.on Research and Development in Information Retrieval (SIGIRŠ02), pp. 238–245, 2002.
    Google ScholarLocate open access versionFindings
  • D. Blei, A. Ng, and M. Jordan, “Latent Dirichlet Allocation”, Journal of Machine Learning Research 3 (2003), 993–1022.
    Google ScholarLocate open access versionFindings
  • S. Deerwester, S. Dumais, G. W. Furnas, T. K. Landauer and R. Harshman, “Indexing by Latent Semantic Analysis”, Journal of the Society for Information Science 41 (1990), 391–407.
    Google ScholarLocate open access versionFindings
  • S. Funk, “Netflix Update: Try This At Home”, http://sifter.org/̃simon/journal/20061211.html, 2006.
    Findings
  • D. Goldberg, D. Nichols, B. M. Oki and D. Terry, “Using Collaborative Filtering to Weave an Information Tapestry”, Communications of the ACM 35 (1992), 61–70.
    Google ScholarLocate open access versionFindings
  • J. L. Herlocker, J. A. Konstan and J. Riedl,, “Explaining Collaborative Filtering Recommendations”, Proc. ACM conference on Computer Supported Cooperative Work, pp. 241–250, 2000.
    Google ScholarLocate open access versionFindings
  • J. L. Herlocker, J. A. Konstan, A. Borchers and John Riedl, “An Algorithmic Framework for Performing Collaborative Filtering”, Proc. 22nd ACM SIGIR Conference on Information Retrieval, pp. 230–237, 1999.
    Google ScholarLocate open access versionFindings
  • T. Hofmann, “Latent Semantic Models for Collaborative Filtering”, ACM Transactions on Information Systems 22 (2004), 89–115.
    Google ScholarLocate open access versionFindings
  • D. Kim and B. Yum, “Collaborative Filtering Based on Iterative Principal Component Analysis”, Expert Systems with Applications 28 (2005), 823–830.
    Google ScholarLocate open access versionFindings
  • G. Linden, B. Smith and J. York, “Amazon.com Recommendations: Item-to-item Collaborative Filtering”, IEEE Internet Computing 7 (2003), 76–80.
    Google ScholarLocate open access versionFindings
  • D.W. Oard and J. Kim, “Implicit Feedback for Recommender Systems”, Proc. 5th DELOS Workshop on Filtering and Collaborative Filtering, pp. 31–36, 1998.
    Google ScholarLocate open access versionFindings
  • A. Paterek, “Improving Regularized Singular Value Decomposition for Collaborative Filtering”, Proc. KDD Cup and Workshop, 2007.
    Google ScholarLocate open access versionFindings
  • R. Salakhutdinov, A. Mnih and G. Hinton, “Restricted Boltzmann Machines for Collaborative Filtering”, Proc. 24th Annual International Conference on Machine Learning, pp. 791–798, 2007.
    Google ScholarLocate open access versionFindings
  • R. Salakhutdinov and A. Mnih, “Probabilistic Matrix Factorization”, Advances in Neural Information Processing Systems 20 (NIPS’07), pp. 1257–1264, 2008.
    Google ScholarLocate open access versionFindings
  • B. M. Sarwar, G. Karypis, J. A. Konstan, and J. Riedl, “Application of Dimensionality Reduction in Recommender System – A Case Study”, WEBKDD’2000.
    Google ScholarFindings
  • B. Sarwar, G. Karypis, J. Konstan and J. Riedl, “Item-based Collaborative Filtering Recommendation Algorithms”, Proc. 10th International Conference on the World Wide Web, pp. 285-295, 2001.
    Google ScholarLocate open access versionFindings
  • G. Takacs, I. Pilaszy, B. Nemeth and D. Tikk, “Major Components of the Gravity Recommendation System”, SIGKDD Explorations 9 (2007), 80–84.
    Google ScholarLocate open access versionFindings
  • N. Tintarev and J. Masthoff, “A Survey of Explanations in Recommender Systems”, ICDE’07 Workshop on Recommender Systems and Intelligent User Interfaces, 2007.
    Google ScholarLocate open access versionFindings
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