Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
A Framework for Collaborative, Content-Based and Demographic Filtering
Artificial Intelligence Review - Special issue on data mining on the Internet
Data mining: concepts and techniques
Data mining: concepts and techniques
Explaining collaborative filtering recommendations
CSCW '00 Proceedings of the 2000 ACM conference on Computer supported cooperative work
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
Eigentaste: A Constant Time Collaborative Filtering Algorithm
Information Retrieval
Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Sparsity, scalability, and distribution in recommender systems
Sparsity, scalability, and distribution in recommender systems
Clustering Approach for Hybrid Recommender System
WI '03 Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence
ACM Transactions on Information Systems (TOIS)
Integrating AHP and data mining for product recommendation based on customer lifetime value
Information and Management
A probabilistic music recommender considering user opinions and audio features
Information Processing and Management: an International Journal - Special issue: AIRS2005: Information retrieval research in Asia
Expert Systems with Applications: An International Journal
A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem
Information Sciences: an International Journal
Wiki-News Interface Agent Based on AIS Methods
KES-AMSTA '07 Proceedings of the 1st KES International Symposium on Agent and Multi-Agent Systems: Technologies and Applications
Mining changes in customer buying behavior for collaborative recommendations
Expert Systems with Applications: An International Journal
Collaborative filtering by personality diagnosis: a hybrid memory- and model-based approach
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Extended latent class models for collaborative recommendation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A hybrid approach for learning concept hierarchy from Malay text using artificial immune network
Natural Computing: an international journal
An improved privacy-preserving DWT-based collaborative filtering scheme
Expert Systems with Applications: An International Journal
Collaborative Filtering with a User-Item Matrix Reduction Technique
International Journal of Electronic Commerce
A literature review and classification of recommender systems research
Expert Systems with Applications: An International Journal
A comparison of clustering-based privacy-preserving collaborative filtering schemes
Applied Soft Computing
Knowledge-Based Systems
A scalable privacy-preserving recommendation scheme via bisecting k-means clustering
Information Processing and Management: an International Journal
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A system is seriously required for helping users to find their path on the shopping and entertainment web sites where the amounts of on-line information vastly increase. Therefore, recommender systems, new type of internet based software tool, appeared, and became an appealing subject for researchers. Collaborative filtering (CF) technique based on user is the one of the method widely used by recommender systems but they have some problems for waiting to be developed solutions that are more efficient. One of these mainly problems is data sparsity. While the number of products is increase, the ratio of common rated products is decrease so calculating the computations of neighbourhood become difficult. The other one is scalability which is the performance problem of the existing algorithms on the datasets has large amounts of information. In this article, we tackle these two questions: (1) how the data sparsity can be reduced ? (2) How to make recommendation algorithms more scalable? We present an approach to addressing the both of these problems at the same time by using a new CF model, constructed based on the Artificial Immune Network Algorithm (aiNet). It is chosen because aiNet is capable of reducing sparsity and providing the scalability of dataset via describing data structure, including their spatial distribution and cluster inter-relations. The new user-item ratings dataset reduced by applying aiNet (aiNetDS) given more stable results and produced predictions more quickly than the raw user-item ratings dataset (rawDS). Besides, the effects of using clustering for forming the neighbourhoods to the system performance are investigated. For this, both of these dataset are clustered by using k-means algorithm and then these cluster partitions are used as neighbourhoods. As a result, it has been shown that the clustered aiNetDS is given more accurate and quick results than the others are.