Alleviating New User Cold-Start in User-Based Collaborative Filtering via Bipartite Network
Bibliographic Data
| ID | 22107396 |
|---|---|
| Authors | Zhipeng Zhang (0000-0001-8201-258X, Liaoning Normal University), Mianxiong Dong (0000-0002-2788-3451, Muroran Institute of Technology), Kaoru Ota (0000-0002-3382-1652, Muroran Institute of Technology), Yasuo Kudo (0009-0009-2968-1448, Muroran Institute of Technology) |
| Year | 2020 |
| Volume | 7 |
| Issue | 3 |
| Pages | 672-685 |
| Publication date | 2020-06-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | IEEE Transactions on Computational Social Systems (JOURNAL) |
| Journal identifiers | ISSN: 2329-924X • E-ISSN: 2373-7476 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) (PUBLISHER) |
| DOI | 10.1109/tcss.2020.2971942 |
| OpenAlex | W3015045735 |
| ISBN | 9781728163215 |
| Language | EN |
| Citations received | 5 |
| References cited | 46 |
The recommender system (RS) can help us extract valuable data from a huge amount of raw information. User-based collaborative filtering (UBCF) is widely employed in practical RSs due to its outstanding performance. However, the traditional UBCF is subject to the new user cold-start issue because a new user is often extreme lack of available rating information. In this article, we develop a novel approach that incorporates a bipartite network into UBCF for enhancing the recommendation quality of new users. First, through the statistic and analysis of new users' rating characteristics, we collect niche items and map the corresponding rating matrix to a weighted bipartite network. Furthermore, a new weighted bipartite modularity index merging normalized rating information is present to conduct the community partition that realizes coclustering of users and items. Finally, for each individual clustering that is much smaller than the original rating matrix, a localized low-rank matrix factorization is executed to predict rating scores for unrated items. Items with the highest predicted rating scores are recommended to a new user. Experimental results from two real-world data sets suggest that without requiring additional complex information, the proposed approach is superior in terms of both recommendation accuracy and diversity and can alleviate the new user cold-start issue of UBCF effectively
Bipartite graph · Cluster analysis · Cold start (automotive) · Collaborative filtering · Data mining · Graph · Information overload · Information retrieval · Machine learning · Matrix decomposition · Recommender system · RSS · World Wide Web · Advanced Clustering Algorithms Research · Computer Science · Image and Video Quality Assessment · Mathematics · Recommender Systems and Techniques · Artificial Intelligence · Theoretical Computer Science
| Citations per year | 1 |
|---|---|
| Citation span | 2021 - 2025 (5) |
| Highly cited | No |