Paper Info
Title
Discrete Listwise Personalized Ranking for Fast Top-N Recommendation with Implicit Feedback.
Abstract
We address the efficiency problem of personalized ranking from implicit feedback by hashing users and items with binary codes, so that top-N recommendation can be fast executed in a Hamming space by bit operations. However, current hashing methods for top-N recommendation fail to align their learning objectives (such as pointwise or pairwise loss) with the benchmark metrics for ranking quality (e.g. Average Precision, AP), resulting in sub-optimal accuracy. To this end, we propose a Discrete Listwise Personalized Ranking (DLPR) model that optimizes AP under discrete constraints for fast and accurate top-N recommendation. To resolve the challenging DLPR problem, we devise an efficient algorithm that can directly learn binary codes in a relaxed continuous solution space. Specifically, theoretical analysis shows that the optimal solution to the relaxed continuous optimization problem is exactly the same as that of the original discrete DLPR problem. Through extensive experiments on two real-world datasets, we show that DLPR consistently surpasses state-of-the-art hashing methods for top-N recommendation.
Year
DOI
Venue
2022
10.24963/ijcai.2022/300
International Joint Conference on Artificial Intelligence
Keywords
DocType
Citations 
Data Mining: Recommender Systems
Conference
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Fangyuan Luo100.68
Jun Wu212515.66
Tao Wang315629.90