Slope One Predictors for Online Rating-Based Collaborative Filtering

Abstract

Rating-based collaborative filtering is the process of predicting how a user would rate a given item from other user ratings. We propose three related slope one schemes with predictors of the form f(x) = x + b, which precompute the average difference between the ratings of one item and another for users who rated both. Slope one algorithms are easy to implement, efficient to query, reasonably accurate, and they support both online queries and dynamic updates, which makes them good candidates for real-world systems. The basic slope one scheme is suggested as a new reference scheme for collaborative filtering. By factoring in items that a user liked separately from items that a user disliked, we achieve results competitive with slower memory-based schemes over the standard benchmark EachMovie and Movielens data sets while better fulfilling the desiderata of CF applications.

Keywords

Item-Item Collaborative Filtering, e-Commerce, Item-to-Item Collaborative Filtering, Recommender Systems, Item-based Collaborative Filtering, Data Mining, Knowledge Discovery

Reference

Daniel Lemire, Anna Maclachlan, Slope One Predictors for Online Rating-Based Collaborative Filtering, In SIAM Data Mining (SDM'05), Newport Beach, California, April 21-23, 2005.

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Software and related work

Please see the Wikipedia entry on Slope One.

BibTeX

@inproceedings{LemireMaclachlan2005,
   author    = {Daniel Lemire and Anna Maclachlan},
   title     = {Slope One Predictors for Online Rating-Based Collaborative Filtering},
   booktitle = {Proceedings of SIAM Data Mining (SDM'05)},
   year      = {2005},
   url = {http://www.daniel-lemire.com/fr/documents/publications/lemiremaclachlan_sdm05.pdf}
}

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