PHOAKS: a system for sharing recommendations
Communications of the ACM
Adaptive interfaces for ubiquitous web access
Communications of the ACM - The Adaptive Web
MovieLens unplugged: experiences with an occasionally connected recommender system
Proceedings of the 8th international conference on Intelligent user interfaces
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
IEEE Transactions on Knowledge and Data Engineering
Using SVD and demographic data for the enhancement of generalized Collaborative Filtering
Information Sciences: an International Journal
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
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Recommender Systems are used for generating recommendations for users with respect to various products and applications. Currently, recommender systems are widely used in e- commerce applications to suggest the appropriate products and services to the users. Sequential information plays an important role for deciding the interests of the user. The proposed system happens to be a collaborative-model based recommendation system and considers the sequential information present in web logs for generation of the recommendations. The model is a combination of clustering, classification and recommendation engine. Clustering has been performed to group users on the basis of sequential and content similarity present in their web page visit sequences. Each cluster represents an interest area or category. Singular value decomposition (SVD) has been used for classification and generating the recommendations for new users.