WalkRanker: A Unified Pairwise Ranking Model With Multiple Relations for Item Recommendation

Authors

  • Lu Yu King Abdullah University of Science & Technology
  • Chuxu Zhang University of Notre Dame
  • Shichao Pei King Abdullah University of Science & Technology
  • Guolei Sun King Abdullah University of Science & Technology
  • Xiangliang Zhang King Abdullah University of Science & Technology

Keywords:

one-class collaborative filtering, pairwise ranking, top-N recommendation

Abstract

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.

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Published

2018-04-26

How to Cite

Yu, L., Zhang, C., Pei, S., Sun, G., & Zhang, X. (2018). WalkRanker: A Unified Pairwise Ranking Model With Multiple Relations for Item Recommendation. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11866

Issue

Section

Main Track: Machine Learning Applications