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Measuring predictive capability in collaborative filtering

Published: 23 October 2009 Publication History

Abstract

This paper presents a new memory-based approach to Collaborative Filtering where the neighbors of the active user will be selected taking into account their predictive capability. Our hypothesis is that if a user was good at predicting the past ratings, then his/her predictions will be also helpful to recommend ratings in the future. The predictive capability of a user will be measured using two different criteria: The first one which is based on the likelihood of the active user's rating and the second one tries to minimize the error obtained using his/her predictions. We show our experimental results using standard data sets.

References

[1]
H.J. Ahn. A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem. Information Sciences 178:37--51, 2008.
[2]
S. Geisser (1993) Predictive Inference: An Introduction. New York: Chapman&Hall.
[3]
J. L. Herlocker, J. A. Konstan, A. Borchers, J. Riedl. An algorithmic framework for performing collaborative filtering. Proc. ACM SIGIR 99, pp. 230--237. 1999
[4]
T. Hofmann. Latent semantic models for collaborative filtering. ACM TOIS V22 (1) pp 89--115. 2004
[5]
G. Linden, B. Smith, J. York. Amazon.com Recommendations: Item-to-Item collaborative filtering IEEE Internet Computing, pp. 76--80. 2003.
[6]
P. Reskick, H.R. Varian. Recommender systems. Communications of the ACM, 40(3):56--58, 1997.
[7]
B. Sarwar, G. Karypis, J. Konstan, J. Reidl. Item based collaborative filtering recommendation algorithms, Proc.10th Int. Conference on WWW, p.285--295. 2001,
[8]
J.Wang, A.P. de Vries, M.Reinders Unified Relevance Models for Rating Prediction in Collaborative Filtering ACM TOIS, 26:3. Article 16. 2008
[9]
Yahoo! Webscope dataset ydata-ymovies-user-movie-ratings-v1.0 http://research.yahoo.com/Academic_Relations

Cited By

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  • (2020)Rule-Based Effective Collaborative Recommendation Using Unfavorable PreferenceIEEE Access10.1109/ACCESS.2020.30085148(128116-128123)Online publication date: 2020
  • (2014)The maximum imputation framework for neighborhood-based collaborative filteringSocial Network Analysis and Mining10.1007/s13278-014-0207-34:1Online publication date: 18-Jun-2014
  • (2014)Comparing the Predictive Capability of Social and Interest Affinity for RecommendationsWeb Information Systems Engineering – WISE 201410.1007/978-3-319-11749-2_22(276-292)Online publication date: 2014
  • Show More Cited By

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      cover image ACM Conferences
      RecSys '09: Proceedings of the third ACM conference on Recommender systems
      October 2009
      442 pages
      ISBN:9781605584355
      DOI:10.1145/1639714
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Publication History

      Published: 23 October 2009

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      Author Tags

      1. collaborative filtering
      2. probabilistic reasoning
      3. recommender system

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      RecSys '09
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      RecSys '09: Third ACM Conference on Recommender Systems
      October 23 - 25, 2009
      New York, New York, USA

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      Overall Acceptance Rate 254 of 1,295 submissions, 20%

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      Cited By

      View all
      • (2020)Rule-Based Effective Collaborative Recommendation Using Unfavorable PreferenceIEEE Access10.1109/ACCESS.2020.30085148(128116-128123)Online publication date: 2020
      • (2014)The maximum imputation framework for neighborhood-based collaborative filteringSocial Network Analysis and Mining10.1007/s13278-014-0207-34:1Online publication date: 18-Jun-2014
      • (2014)Comparing the Predictive Capability of Social and Interest Affinity for RecommendationsWeb Information Systems Engineering – WISE 201410.1007/978-3-319-11749-2_22(276-292)Online publication date: 2014
      • (2013)Lazy Collaborative Filtering for Data Sets With Missing ValuesIEEE Transactions on Cybernetics10.1109/TSMCB.2012.223141143:6(1822-1834)Online publication date: Dec-2013
      • (2012)The efficient imputation method for neighborhood-based collaborative filteringProceedings of the 21st ACM international conference on Information and knowledge management10.1145/2396761.2396849(684-693)Online publication date: 29-Oct-2012
      • (2012)Using past-prediction accuracy in recommender systemsInformation Sciences: an International Journal10.1016/j.ins.2012.02.033199(78-92)Online publication date: 1-Sep-2012
      • (2010)Social webconsciousnessProceedings of the 1st ACM international workshop on Connected multimedia10.1145/1877911.1877915(3-8)Online publication date: 29-Oct-2010

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