Slope One Predictors for Online Rating-Based Collaborative Filtering

Lemire, Daniel et Maclachlan, Anna (2005). « Slope One Predictors for Online Rating-Based Collaborative Filtering », dans SDM'05 (Proceedings of the Fifth SIAM International Conference on Data Mining, Newport Beach, California, USA, April 22-24, 2005)

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Résumé

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.

Type:	Communication, article de congrès ou colloque
Mots-clés ou Sujets:	Item-Item Collaborative Filtering, e-Commerce, Item-to-Item Collaborative Filtering, Recommender Systems, Item-based Collaborative Filtering, Data Mining, Knowledge Discovery
Unité d'appartenance:	Télé-université > UER Science et Technologie
Déposé par:	Daniel Lemire
Date de dépôt:	05 juin 2007
Dernière modification:	01 nov. 2014 02:03
Adresse URL :	http://archipel.uqam.ca/id/eprint/317

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