Name
Title | ||
|---|---|---|
HCFRec: Hash Collaborative Filtering via Normalized Flow with Structural Consensus for Efficient Recommendation. |
Abstract | ||
|---|---|---|
The ever-increasing data scale of user-item interactions makes it challenging for an effective and efficient recommender system. Recently, hash-based collaborative filtering (Hash-CF) approaches employ efficient Hamming distance of learned binary representations of users and items to accelerate recommendations. However, Hash-CF often faces two challenging problems, i.e., optimization on discrete representations and preserving semantic information in learned representations. To address the above two challenges, we propose HCFRec, a novel Hash-CF approach for effective and efficient recommendations. Specifically, HCFRec not only innovatively introduces normalized flow to learn the optimal hash code by efficiently fit a proposed approximate mixture multivariate normal distribution, a continuous but approximately discrete distribution, but also deploys a cluster consistency preserving mechanism to preserve the semantic structure in representations for more accurate recommendations. Extensive experiments conducted on six real-world datasets demonstrate the superiority of our HCFRec compared to the state-of-art methods in terms of effectiveness and efficiency. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.24963/ijcai.2022/315 | European Conference on Artificial Intelligence |
Keywords | DocType | Citations |
Data Mining: Recommender Systems | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fan Wang | 1 | 0 | 0.34 |
| Weiming Liu | 2 | 0 | 0.34 |
| Chaochao Chen | 3 | 115 | 19.04 |
| Mengying Zhu | 4 | 0 | 1.01 |
| Xiaolin Zheng | 5 | 300 | 36.99 |