Name
Abstract | ||
|---|---|---|
Collaborative filtering (CF) is a major technique in recommender systems to help users find their potentially desired items. Since the data sparsity problem is quite commonly encountered in real-world scenarios, Cross-Domain Collaborative Filtering (CDCF) hence is becoming an emerging research topic in recent years. However, due to the lack of sufficient dense explicit feedbacks and even no feedback available in users' uninvolved domains, current CDCF approaches may not perform satisfactorily in user preference prediction. In this paper, we propose a generalized Cross Domain Triadic Factorization (CDTF) model over the triadic relation user-item-domain, which can better capture the interactions between domain-specific user factors and item factors. In particular, we devise two CDTF algorithms to leverage user explicit and implicit feedbacks respectively, along with a genetic algorithm based weight parameters tuning algorithm to trade off influence among domains optimally. Finally, we conduct experiments to evaluate our models and compare with other state-of-the-art models by using two real world datasets. The results show the superiority of our models against other comparative models. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2488388.2488441 | WWW |
Keywords | Field | DocType |
sufficient dense explicit feedbacks,comparative model,genetic algorithm,cdtf algorithm,domains optimally,cross-domain collaborative filtering,domain-specific user factor,current cdcf approach,user preference prediction,implicit feedbacks,cross-domain triadic factorization,personalized recommendation,performance,algorithms,recommender system | Recommender system,Data mining,World Wide Web,Collaborative filtering,Computer science,Factorization,Artificial intelligence,Genetic algorithm,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4503-2035-1 | 78 | 1.97 |
References | Authors | |
18 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Liang Hu | 1 | 166 | 15.64 |
| Jian Cao | 2 | 274 | 19.90 |
| Guandong Xu | 3 | 640 | 75.03 |
| Longbing Cao | 4 | 2212 | 185.04 |
| Zhiping Gu | 5 | 132 | 9.49 |
| Can Zhu | 6 | 78 | 1.97 |