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A design framework for recommender system by incorporating sequential information
, Pradeep Kumar, Bharat Bhasker
Published in ACM Press
Pages: 387 - 390

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. extcopyright 2011 ACM.

About the journal
JournalData powered by TypesetProceedings of the 13th International Conference on Information Integration and Web-based Applications and Services - iiWAS '11
PublisherData powered by TypesetACM Press
Open AccessNo