Name
Abstract | ||
|---|---|---|
We address the efficiency problem of Collaborative Filtering ( CF) in the context of large user and item spaces. A promising solution is to hash users and items with binary codes, and then make recommendation in a Hamming space. However, existing CF hashing methods mainly concentrate on modeling the user-item affinity, yet ignore the user-user and item-item affinities. Such manner results in a large encoding loss and deteriorates the recommendation accuracy subsequently. Towards this end, we propose a Binary Collaborative Filtering Ensemble ( BCFE) framework which ensembles three popularly used CF methods to preserve the user-item, user-user and item-item affinities in the Hamming space simultaneously. In order to avoid a time-consuming computation of the user-user and item-item affinity matrices, an anchor approximation solution is employed by BCFE through subspace clustering. Specifically, we devise a Discretization-like Bit-wise Gradient Descend ( DBGD) optimization algorithm that incorporates the binary quantization into the learning stage and updates binary codes in a bit-by-bit way. Such a discretization-like algorithm can yield more high-quality binary codes comparing with the popular "two-stage" CF hashing schemes, and is much simpler than the rigorous discrete optimization. Extensive experiments on three real-world datasets show that our BCFE approach significantly outperforms state-of-the-art CF hashing techniques. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-319-97304-3_76 | PRICAI 2018: TRENDS IN ARTIFICIAL INTELLIGENCE, PT I |
Keywords | Field | DocType |
Recommender systems, Collaborative filtering, Anchor representation, Learning to hash | Recommender system,Collaborative filtering,Pattern recognition,Discrete optimization,Computer science,Binary code,Algorithm,Artificial intelligence,Hash function,Hamming space,Quantization (signal processing),Binary number | Conference |
Volume | ISSN | Citations |
11012 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 11 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yujia Zhang | 1 | 31 | 9.22 |
| Jun Wu | 2 | 125 | 15.66 |
| Hai-Shuai Wang | 3 | 62 | 13.11 |