کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
534181 | 870230 | 2012 | 8 صفحه PDF | دانلود رایگان |
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.
► We apply the FN model to analyze the item relevance in the folksonomy data.
► We integrate the obtained folksonomy item relevance into the global-optimization based NBM.
► The proposed model yields competitive results on the MovieLens 10 M/100 K dataset.
Journal: Pattern Recognition Letters - Volume 33, Issue 3, 1 February 2012, Pages 263–270