Name
Title | ||
|---|---|---|
Improving neighborhood based Collaborative Filtering via integrated folksonomy information |
Abstract | ||
|---|---|---|
Personalized recommender systems which can provide people with suggestions according to individual interests usually rely on Collaborative Filtering (CF). The neighborhood based model (NBM) is a common choice when implementing such recommenders due to the intuitive nature; however, the recommendation accuracy is a major concern. Current NBM based recommenders mostly address the accuracy issue based on the rating data alone, whereas research on hybrid recommender systems suggests that users enjoy specifying feedback about items across multiple dimensions. In this work we aim to improve the accuracy of NBM via integrating the folksonomy information. To achieve this objective, we first propose the folksonomy network (FN) to analyze the item relevance described by the folksonomy data. We subsequently integrate the obtained folksonomy information into the global-optimization based NBM for making multi-source based recommendations. Experiments on the MovieLens dataset suggest positive results, which prove the efficiency of our strategy. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1016/j.patrec.2011.10.016 | Pattern Recognition Letters |
Keywords | Field | DocType |
accuracy issue,rating data,folksonomy network,current nbm,improving neighborhood,collaborative filtering,hybrid recommender system,recommendation accuracy,folksonomy data,personalized recommender system,folksonomy information,integrated folksonomy information | Recommender system,Collaborative filtering,Information retrieval,Computer science,MovieLens,Folksonomy,Multiple time dimensions | Journal |
Volume | Issue | ISSN |
33 | 3 | 0167-8655 |
Citations | PageRank | References |
4 | 0.39 | 26 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xin Luo | 1 | 239 | 17.86 |
| Yuanxin Ouyang | 2 | 121 | 21.57 |
| Zhang Xiong | 3 | 1069 | 102.45 |