GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Data mining: concepts and techniques
Data mining: concepts and techniques
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Eigentaste: A Constant Time Collaborative Filtering Algorithm
Information Retrieval
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Probabilistic Association Rules for Item-Based Recommender Systems
Proceedings of the 2008 conference on STAIRS 2008: Proceedings of the Fourth Starting AI Researchers' Symposium
Making use of associative classifiers in order to alleviate typical drawbacks in recommender systems
Expert Systems with Applications: An International Journal
A hybrid recommendation approach for a tourism system
Expert Systems with Applications: An International Journal
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Recommender systems make information filtering for user by predicting user’s preference to items. Collaborative filtering is the most popular technique in implementing a recommender system. Association rule mining is a powerful data mining method to search for interesting relationships between items by finding the items frequently appeared together in a transaction database. In this paper, we apply quantitative association rules to mining the relationships between items, and then utilize the relationships between items to alleviate the data sparsity problem in the neighborhood-based algorithms. The proposed method considers not only similarities between users, but also similarities between items. The experimental results on two publicly available datasets show that our algorithm outperforms the conventional Pearson method and adjusted cosine method.