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Rethinking Correlation-based Item-Item Similarities for Recommender Systems

Published: 07 July 2022 Publication History

Abstract

This paper studies correlation-based item-item similarity measures for recommendation systems. While current research on recommender systems is directed toward deep learning-based approaches, nearest neighbor methods have been still used extensively in commercial recommender systems due to their simplicity. A crucial step in item-based nearest neighbor methods is to compute similarities between items, which are generally estimated through correlation measures like Pearson. The purpose of this paper is to re-investigate the effectiveness of correlation-based nearest neighbor methods on several benchmark datasets that have been used for recommendation evaluation in recent years. This paper also provides a more effective estimation method for correlation measures than the classical Pearson correlation coefficient and shows that this leads to significant improvements in recommendation performance.

References

[1]
Deepak Agarwal, Liang Zhang, and Rahul Mazumder. 2011. Modeling item-item similarities for personalized recommendations on Yahoo! front page. The Annals of applied statistics (2011), 1839--1875.
[2]
James Bennett, Stan Lanning, et al. 2007. The Netflix Prize. In Proceedings of KDD cup and workshop, Vol. 2007. New York, NY, USA., 35.
[3]
Thierry Bertin-Mahieux, Daniel P. W. Ellis, Brian Whitman, and Paul Lamere. 2011. The Million Song Dataset. In Proceedings of the 12th International Society for Music Information Retrieval Conference, ISMIR 2011, Miami, Florida, USA, October 24--28, 2011, Anssi Klapuri and Colby Leider (Eds.). 591--596.
[4]
Mukund Deshpande and George Karypis. 2004. Item-based top-n recommendation algorithms. ACM Transactions on Information Systems (TOIS) 22, 1 (2004), 143--177.
[5]
F Maxwell Harper and Joseph A Konstan. 2015. The Movielens Datasets: History and Context. ACM transactions on interactive intelligent systems (tiis) 5, 4 (2015), 1--19.
[6]
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural collaborative filtering. In Proceedings of the 26th international conference on world wide web. 173--182.
[7]
Yifan Hu, Yehuda Koren, and Chris Volinsky. 2008. Collaborative filtering for implicit feedback datasets. In 2008 Eighth IEEE international conference on data mining. Ieee, 263--272.
[8]
Maurice George Kendall et al. 1946. The advanced theory of statistics. The advanced theory of statistics. 2nd Ed (1946).
[9]
Halime Khojamli and Jafar Razmara. 2021. Survey of similarity functions on neighborhood-based collaborative filtering. Expert Systems with Applications 185 (2021), 115482.
[10]
Peter Knees, Dominik Schnitzer, and Arthur Flexer. 2014. Improving neighborhood-based collaborative filtering by reducing hubness. In Proceedings of International Conference on Multimedia Retrieval. 161--168.
[11]
Yehuda Koren. 2008. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. 426--434.
[12]
Yehuda Koren, Robert Bell, and Chris Volinsky. 2009. Matrix factorization techniques for recommender systems. Computer 42, 8 (2009), 30--37.
[13]
Dawen Liang, Rahul G Krishnan, Matthew D Hoffman, and Tony Jebara. 2018. Variational autoencoders for collaborative filtering. In Proceedings of the 2018 world wide web conference. 689--698.
[14]
Greg Linden, Brent Smith, and Jeremy York. 2003. Amazon. com recommendations: Item-to-item collaborative filtering. IEEE Internet computing 7, 1 (2003), 76--80.
[15]
Sam Lobel, Chunyuan Li, Jianfeng Gao, and Lawrence Carin. 2019. RaCT: Toward Amortized Ranking-Critical Training For Collaborative Filtering. In International Conference on Learning Representations.
[16]
Xin Luo, Mengchu Zhou, Yunni Xia, and Qingsheng Zhu. 2014. An efficient non-negative matrix-factorization-based approach to collaborative filtering for recommender systems. IEEE Transactions on Industrial Informatics 10, 2 (2014), 1273--1284.
[17]
Hao Ma, Irwin King, and Michael R Lyu. 2007. Effective missing data prediction for collaborative filtering. In Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval. 39--46.
[18]
Xia Ning and George Karypis. 2011. Slim: Sparse linear methods for top-n recommender systems. In 2011 IEEE 11th international conference on data mining. IEEE, 497--506.
[19]
Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. 2001. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th international conference on World Wide Web. 285--295.
[20]
Harald Steck. 2019. Embarrassingly shallow autoencoders for sparse data. In The World Wide Web Conference. 3251--3257.
[21]
Marwan Torki. 2018. A document descriptor using covariance of word vectors. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). 527--532.
[22]
Koen Verstrepen and Bart Goethals. 2014. Unifying nearest neighbors collaborative filtering. In Proceedings of the 8th ACM Conference on Recommender systems. 177--184.
[23]
Joe Whittaker. 1990. Graphical Models in Applied Multivariate Statistics. Wiley, New York, NY.
[24]
Yao Wu, Christopher DuBois, Alice X Zheng, and Martin Ester. 2016. Collaborative denoising auto-encoders for top-n recommender systems. In Proceedings of the ninth ACM international conference on web search and data mining. 153--162.
[25]
Yin Zheng, Bangsheng Tang, Wenkui Ding, and Hanning Zhou. 2016. A neural autoregressive approach to collaborative filtering. In International Conference on Machine Learning. PMLR, 764--773.

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  • (2023)Boosting the Item-Based Collaborative Filtering Model with Novel Similarity MeasuresInternational Journal of Computational Intelligence Systems10.1007/s44196-023-00299-216:1Online publication date: 29-Jul-2023

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cover image ACM Conferences
SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
July 2022
3569 pages
ISBN:9781450387323
DOI:10.1145/3477495
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Published: 07 July 2022

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Author Tags

  1. collaborative filtering
  2. correlation
  3. item-item similarity
  4. recommendation system

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  • JSPS KAKENHI

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  • (2023)Boosting the Item-Based Collaborative Filtering Model with Novel Similarity MeasuresInternational Journal of Computational Intelligence Systems10.1007/s44196-023-00299-216:1Online publication date: 29-Jul-2023

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