TY - GEN
T1 - GBPR: Group preference based Bayesian personalized ranking for one-class collaborative filtering
T2 - 23rd International Joint Conference on Artificial Intelligence, IJCAI 2013
AU - Pan, Weike
AU - CHEN, Li
N1 - Copyright:
Copyright 2014 Elsevier B.V., All rights reserved.
PY - 2013/8
Y1 - 2013/8
N2 - One-class collaborative filtering or collaborative ranking with implicit feedback has been steadily receiving more attention, mostly due to the "oneclass" characteristics of data in various services, e.g., "like" in Facebook and "bought" in Amazon. Previous works for solving this problem include pointwise regression methods based on absolute rating assumptions and pairwise ranking methods with relative score assumptions, where the latter was empirically found performing much better because it models users' ranking-related preferences more directly. However, the two fundamental assumptions made in the pairwise ranking methods, (1) individual pairwise preference over two items and (2) independence between two users, may not always hold. As a response, we propose a new and improved assumption, group Bayesian personalized ranking (GBPR), via introducing richer interactions among users. In particular, we introduce group preference, to relax the aforementioned individual and independence assumptions. We then design a novel algorithm correspondingly, which can recommend items more accurately as shown by various ranking-oriented evaluation metrics on four real-world datasets in our experiments.
AB - One-class collaborative filtering or collaborative ranking with implicit feedback has been steadily receiving more attention, mostly due to the "oneclass" characteristics of data in various services, e.g., "like" in Facebook and "bought" in Amazon. Previous works for solving this problem include pointwise regression methods based on absolute rating assumptions and pairwise ranking methods with relative score assumptions, where the latter was empirically found performing much better because it models users' ranking-related preferences more directly. However, the two fundamental assumptions made in the pairwise ranking methods, (1) individual pairwise preference over two items and (2) independence between two users, may not always hold. As a response, we propose a new and improved assumption, group Bayesian personalized ranking (GBPR), via introducing richer interactions among users. In particular, we introduce group preference, to relax the aforementioned individual and independence assumptions. We then design a novel algorithm correspondingly, which can recommend items more accurately as shown by various ranking-oriented evaluation metrics on four real-world datasets in our experiments.
UR - http://www.scopus.com/inward/record.url?scp=84896063244&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84896063244
SN - 9781577356332
T3 - IJCAI International Joint Conference on Artificial Intelligence
SP - 2691
EP - 2697
BT - IJCAI 2013 - Proceedings of the 23rd International Joint Conference on Artificial Intelligence
PB - AAAI press
Y2 - 3 August 2013 through 9 August 2013
ER -