Paper Info
Title
Exploiting Latent Relations Between Users and Items for Collaborative Filtering.
Abstract
As one of the most important techniques in recommender systems, collaborative filtering (CF) generates the recommendations or predictions based on the observed preferences. Most traditional recommender systems fail to discover the latent associations between the same or similar items with different names, which is called synonymy problem. With the rapid increasing number of users and items, the user-item rating data is extremely sparse. Based on the limited number of user ratings, we cannot capture enough information from the user history using the traditional CF techniques, which could reduce the effectiveness of the recommender systems. In this paper, we propose a novel model User-Relation-Item Model (URIM) for CF, which exploits the latent relationship between different user interest domains and item types. By introducing a component named user-item-relation matrix, which reflects the latent major association patterns behind users and items, URIM tackles the synonymy problem, and therefore achieves a significant performance improvement. We compared our method with several state-of-the-art recommendation algorithms on two real-world datasets. Experimental results validate the effectiveness of our model in terms of prediction accuracy (RMSE) and top-N recommendation quality (Recall and Precision). More specifically, URIM reduces the RMSE by nearly 10% and 5% on the two datasets, respectively.
Year
DOI
Venue
2015
10.1007/978-3-319-26555-1_41
ICONIP
Keywords
Field
DocType
Recommender system,Collaborative filtering,Batch gradient descent,Top-N recommendation
Recommender system,Gradient descent,Collaborative filtering,Information retrieval,Pattern recognition,Computer science,Artificial intelligence,Machine learning
Conference
Volume
ISSN
Citations 
9491
0302-9743
0
PageRank 
References 
Authors
0.34
11
3
Name
Order
Citations
PageRank
Zhou Yingmin111.37
Song Binheng272.14
Zheng Hai-Tao314224.39