Name
Abstract | ||
|---|---|---|
Cross-domain recommender systems are usually able to suggest items, which are not in the same domain, where users provided ratings. For this reason, cross-domain recommendation has attracted more and more attention in recent years. However, most studies propose to make cross-domain recommendation in the scenario, where there are common ratings between different domains. The scenario without common ratings is seldom considered. In this paper, we propose a novel method to solve the cross-domain recommendation problem in such a scenario. We first apply trust relations to the cross-domain scenario for predicting coarse ratings pertaining to cross-domain items. Then, we build a new rating matrix, including known ratings and predicted ratings of items from different domains, and transform a user-item matrix into an item item association matrix. Finally, we compute the similarities of items belonging to different domains and use item-based collaborative filtering to generate recommendations. Through relevant experiments on a real-world data set, we compare our method to a trust-aware recommendation method and demonstrate its effectiveness in terms of prediction accuracy, recall, and coverage. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/ACCESS.2016.2566658 | IEEE ACCESS |
Keywords | Field | DocType |
Cross-domain recommendation,trust network,recommender system,item-item similarity,usage context | Recommender system,Collaborative filtering,Information retrieval,Computer science,Matrix (mathematics),Context model,Recall | Journal |
Volume | ISSN | Citations |
4 | 2169-3536 | 3 |
PageRank | References | Authors |
0.37 | 22 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhenzhen Xu | 1 | 80 | 11.66 |
| Fuli Zhang | 2 | 4 | 0.78 |
| Wei Wang 0077 | 3 | 3 | 0.71 |
| Haifeng Liu | 4 | 6 | 1.22 |
| Xiangjie Kong | 5 | 425 | 46.56 |