Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
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
Artificial Intelligence Review - Special issue on lazy learning
Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis
Artificial Intelligence Review
ACM Computing Surveys (CSUR)
Clustering Approach for Hybrid Recommender System
WI '03 Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
PocketLens: Toward a personal recommender system
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
CinemaScreen Recommender Agent: Combining Collaborative and Content-Based Filtering
IEEE Intelligent Systems
IEEE Transactions on Knowledge and Data Engineering
Similarity Measure and Instance Selection for Collaborative Filtering
International Journal of Electronic Commerce
A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem
Information Sciences: an International Journal
Fuzzy-genetic approach to recommender systems based on a novel hybrid user model
Expert Systems with Applications: An International Journal
Collaborative filtering adapted to recommender systems of e-learning
Knowledge-Based Systems
A multi-disciplinar recommender system to advice research resources in University Digital Libraries
Expert Systems with Applications: An International Journal
Improving memory-based collaborative filtering via similarity updating and prediction modulation
Information Sciences: an International Journal
A study of particle swarm optimization particle trajectories
Information Sciences: an International Journal
Data clustering: 50 years beyond K-means
Pattern Recognition Letters
Collaborative filtering based on an iterative prediction method to alleviate the sparsity problem
Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services
A new collaborative filtering metric that improves the behavior of recommender systems
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
IEEE Transactions on Pattern Analysis and Machine Intelligence
Expert Systems with Applications: An International Journal
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Data sparsity issues in the collaborative filtering framework
WebKDD'05 Proceedings of the 7th international conference on Knowledge Discovery on the Web: advances in Web Mining and Web Usage Analysis
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients
IEEE Transactions on Evolutionary Computation
Least squares quantization in PCM
IEEE Transactions on Information Theory
Swarming to rank for recommender systems
Proceedings of the sixth ACM conference on Recommender systems
Intelligent Techniques in Recommendation Systems: Contextual Advancements and New Methods
Intelligent Techniques in Recommendation Systems: Contextual Advancements and New Methods
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Recommendation system has been a rhetoric area and a topic of rigorous research owing to its application in various domains, from academics to industries through e-commerce. Recommendation system is useful in reducing information overload and improving decision making for customers in any arena. Recommending products to attract customers and meet their needs have become an important aspect in this competitive environment. Although there are many approaches to recommend items, collaborative filtering has emerged as an efficient mechanism to perform the same. Added to it there are many evolutionary methods that could be incorporated to achieve better results in terms of accuracy of prediction, handling sparsity as well as cold start problems. In this paper, we have used unsupervised learning to address the problem of scalability. The recommendation engine reduces calculation time by matching the interest profile of the user to its partitioned and even smaller training samples. Additionally, we have explored the aspect of finding global neighbours through transitive similarities and incorporating particle swarm optimization (PSO) to assign weights to various alpha estimates (including the proposed @a"7) that alleviate sparsity problem. Our experimental study reveals that the particle swarm optimized alpha estimate has significantly increased the accuracy of prediction over the traditional methods of collaborative filtering and fixed alpha scheme.