Name
Abstract | ||
|---|---|---|
In many recommendation applications, like music and movies recommendation, describing the features of items heavily relies on user-generated contents, especially social tags. They suffer from serious problems including redundancy and self-contradiction. Direct exploitation of them in a recommender system leads to reduced performance. However, few systems have taken this problem into consideration. In this paper, we propose a novel framework named as prior knowledge based context aware recommender (PKCAR). We incorporate Dirichlet Forrest priors to encode prior knowledge about item features into our model to deal with the redundancy, and self-contradiction problems. We also develop an algorithm which automatically mine prior knowledge using co-occurrence, lexical and semantic features. We evaluate our framework on two datasets from different domains. Experimental results show that our approach performs better than systems without leveraging prior knowledge about item features. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-46675-0_55 | NEURAL INFORMATION PROCESSING, ICONIP 2016, PT III |
Keywords | Field | DocType |
Context-aware recommendation,Topic modeling,KBTM,Semi-supervised learning,Collaborative filtering | Recommender system,Semi-supervised learning,Collaborative filtering,Computer science,Redundancy (engineering),Artificial intelligence,Topic model,Social tags,Machine learning | Conference |
Volume | ISSN | ISBN |
9949 | 0302-9743 | 978-3-319-46675-0; 978-3-319-46674-3 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zheng Hai-Tao | 1 | 142 | 24.39 |
| Mao Xiaoxi | 2 | 0 | 3.38 |