Name
Abstract | ||
|---|---|---|
Learning informative representations of users and items from the historical interactions is crucial to collaborative filtering (CF). Existing CF approaches usually model interactions solely within the Euclidean space. However, the sophisticated user-item interactions inherently present highly non-Euclidean anatomy with various types of geometric patterns (i.e., tree-likeness and cyclic structures). The Euclidean-based models may be inadequate to fully uncover the intent factors beneath such hybrid-geometry interactions. To remedy this deficiency, in this paper, we study the novel problem of Geometric Disentangled Collaborative Filtering (GDCF), which aims to reveal and disentangle the latent intent factors across multiple geometric spaces. A novel generative GDCF model is proposed to learn geometric disentangled representations by inferring the high-level concepts associated with user intentions and various geometries. Empirically, our proposal is extensively evaluated over five real-world datasets, and the experimental results demonstrate the superiority of GDCF. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1145/3477495.3531982 | SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval |
Keywords | DocType | Citations |
Collaborative Filtering, Disentangled Representation Learning, Non-Euclidean Geometry | Conference | 0 |
PageRank | References | Authors |
0.34 | 5 | 10 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yiding Zhang | 1 | 3 | 1.43 |
| Chaozhuo Li | 2 | 0 | 2.37 |
| Xing Xie | 3 | 9105 | 527.49 |
| Xiao Wang | 4 | 445 | 29.80 |
| Chuan Shi | 5 | 1137 | 80.79 |
| Yuming Liu | 6 | 0 | 1.01 |
| Hao Sun | 7 | 0 | 2.03 |
| Liangjie Zhang | 8 | 2 | 1.13 |
| Weiwei Deng | 9 | 0 | 1.01 |
| Qi Zhang | 10 | 931 | 179.66 |