Automatic text processing
Information filtering and information retrieval: two sides of the same coin?
Communications of the ACM - Special issue on information filtering
Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Fab: content-based, collaborative recommendation
Communications of the ACM
Learning and Revising User Profiles: The Identification ofInteresting Web Sites
Machine Learning - Special issue on multistrategy learning
Modern Information Retrieval
Recommendation systems: a probabilistic analysis
Journal of Computer and System Sciences - Special issue on Internet algorithms
Eigentaste: A Constant Time Collaborative Filtering Algorithm
Information Retrieval
Collaborative Filtering Using Weighted Majority Prediction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Mining customer product ratings for personalized marketing
Decision Support Systems - Special issue: Web data mining
Probabilistic Memory-Based Collaborative Filtering
IEEE Transactions on Knowledge and Data Engineering
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
An MDP-Based Recommender System
The Journal of Machine Learning Research
CinemaScreen Recommender Agent: Combining Collaborative and Content-Based Filtering
IEEE Intelligent Systems
A user-oriented contents recommendation system in peer-to-peer architecture
Expert Systems with Applications: An International Journal
Developing recommender systems with the consideration of product profitability for sellers
Information Sciences: an International Journal
A time-based approach to effective recommender systems using implicit feedback
Expert Systems with Applications: An International Journal
Applications of wavelet data reduction in a recommender system
Expert Systems with Applications: An International Journal
Individual and group behavior-based customer profile model for personalized product recommendation
Expert Systems with Applications: An International Journal
Short communication: Recommendation based on rational inferences in collaborative filtering
Knowledge-Based Systems
An iterative semi-explicit rating method for building collaborative recommender systems
Expert Systems with Applications: An International Journal
Multidimensional credibility model for neighbor selection in collaborative recommendation
Expert Systems with Applications: An International Journal
Two-way cooperative prediction for collaborative filtering recommendations
Expert Systems with Applications: An International Journal
Handling sequential pattern decay: Developing a two-stage collaborative recommender system
Electronic Commerce Research and Applications
A hybrid recommendation technique based on product category attributes
Expert Systems with Applications: An International Journal
Scalable Collaborative Filtering Approaches for Large Recommender Systems
The Journal of Machine Learning Research
Personalized Recommendation over a Customer Network for Ubiquitous Shopping
IEEE Transactions on Services Computing
A hybrid of sequential rules and collaborative filtering for product recommendation
Information Sciences: an International Journal
Factor in the neighbors: Scalable and accurate collaborative filtering
ACM Transactions on Knowledge Discovery from Data (TKDD)
Collaborative filtering with temporal dynamics
Communications of the ACM
A new collaborative filtering metric that improves the behavior of recommender systems
Knowledge-Based Systems
A hybrid collaborative filtering recommendation mechanism for P2P networks
Future Generation Computer Systems
International Journal of Intelligent Systems
A careful assessment of recommendation algorithms related to dimension reduction techniques
Knowledge-Based Systems
Providing Justifications in Recommender Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Knowledge-Based Systems
Similarity of fuzzy triangular number based on indifference area and its application
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories
An entropy-based neighbor selection approach for collaborative filtering
Knowledge-Based Systems
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As one of the collaborative filtering (CF) techniques, memory-based CF technique which recommends items to users based on rating information of like-minded users (called neighbors) has been widely used and has also proven to be useful in many practices in the age of information overload. However, there is still considerable room for improving the quality of recommendation. Shortly, similarity functions in traditional CF compute a similarity between a target user and the other user without considering a target item. More specifically, they give an equal weight to each of the co-rated items rated by both users. Neighbors of a target user, therefore, are identical for all target items. However, a reasonable assumption is that the similarity between a target item and each of the co-rated items should be considered when finding neighbors of a target user. Additionally, a different set of neighbors should be selected for each different target item. Thus, the objective of this paper is to propose a new similarity function in order to select different neighbors for each different target item. In the new similarity function, the rating of a user on an item is weighted by the item similarity between the item and the target item. Experimental results from MovieLens dataset and Netflix dataset provide evidence that our recommender model considerably outperforms the traditional CF-based recommender model.