Name
Abstract | ||
|---|---|---|
Recommendation is an important application that is employed on the Web. In this paper, we propose a method for recommending items to a user by extending a probabilistic inference model in information retrieval. We regard the user's preference as the query, an item as a document, and explicit and implicit factors as index terms. Additional information sources can be added to the probabilistic inference model, particularly belief networks. The proposed method also uses the belief network model to recommend items by combining expert information. Experimental results on real-world data sets show that the proposed method can improve recommendation effectiveness. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/s10489-016-0783-1 | Appl. Intell. |
Keywords | Field | DocType |
Recommender systems,Probabilistic inference model,Belief network | Recommender system,Probabilistic inference,Divergence-from-randomness model,Data set,Information retrieval,Computer science,Bayesian network,Artificial intelligence,Probabilistic relevance model,Machine learning | Journal |
Volume | Issue | ISSN |
45 | 3 | 0924-669X |
Citations | PageRank | References |
3 | 0.36 | 23 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiajin Huang | 1 | 69 | 15.70 |
| Kunlei Zhu | 2 | 4 | 0.71 |
| Ning Zhong | 3 | 2907 | 300.63 |