WalkRanker: A unified pairwise ranking model with multiple relations for item recommendation

Lu Yu, Chuxu Zhang, Shichao Pei, Guolei Sun, Xiangliang Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

36 Scopus citations


Top-N item recommendation techniques, e.g., pairwise models, learn the rank of users' preferred items through separating items into positive samples if user-item interactions exist, and negative samples otherwise. This separation results in an important issue: the extreme imbalance between positive and negative samples, because the number of items with user actions is much less than those without actions. The problem is even worse for “cold-start” users. In addition, existing learning models only consider the observed user-item proximity, while neglecting other useful relations, such as the unobserved but potentially helpful user-item relations, and high-order proximity in user-user, item-item relations. In this paper, we aim at incorporating multiple types of user-item relations into a unified pairwise ranking model towards approximately optimizing ranking metrics mean average precision (MAP), and mean reciprocal rank (MRR). Instead of taking statical separation of positive and negative sets, we employ a random walk approach to dynamically draw positive samples from short random walk sequences, and a rank-aware negative sampling method to draw negative samples for efficiently learning the proposed pairwise ranking model. The proposed method is compared with several state-of-the-art baselines on two large and sparse datasets. Experimental results show that our proposed model outperforms the other baselines with average 4% at different top-N metrics, in particular for cold-start users with 6% on average.
Original languageEnglish (US)
Title of host publication32nd AAAI Conference on Artificial Intelligence, AAAI 2018
PublisherAAAI press
Number of pages8
ISBN (Print)9781577358008
StatePublished - Jan 1 2018

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): Award No. 2639
Acknowledgements: This work is supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. 2639.


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