Paper Info
Title
Classical music for rock fans?: novel recommendations for expanding user interests
Abstract
Most recommender algorithms produce types similar to those the active user has accessed before. This is because they measure user similarity only from the co-rating behaviors against items and compute recommendations by analyzing the items possessed by the users most similar to the active user. In this paper, we define item novelty as the smallest distance from the class the user accessed before to the class that includes target items over the taxonomy. Then, we try to accurately recommend highly novel items to the user. First, our method measures user similarity by employing items rated by users and a taxonomy of items. It can accurately identify many items that may suit the user. Second, it creates a graph whose nodes are users; weighted edges are set between users according to their similarity. It analyzes the user graph and extracts users that are related on the graph though the similarity between the active user and each of those users is not high. The users so extracted are likely to have highly novel items for the active user. An evaluation conducted on several datasets finds that our method accurately identifies items with higher novelty than previous methods.
Year
DOI
Venue
2010
10.1145/1871437.1871558
CIKM
Keywords
Field
DocType
item novelty,user similarity,novel recommendation,user graph,active user,extracts user,user interest,rock fan,previous method,co-rating behavior,classical music,method measures user similarity,novel item,higher novelty,collaborative filtering
Data mining,Graph,Novelty detection,Collaborative filtering,Classical music,Information retrieval,Computer science,User modeling,Novelty
Conference
Citations 
PageRank 
References 
25
0.86
20
Authors
6
Name
Order
Citations
PageRank
Makoto Nakatsuji122513.16
Yasuhiro Fujiwara229225.43
Akimichi Tanaka3474.90
Toshio Uchiyama422127.76
Ko Fujimura543524.83
Ishida, Toru63021490.20