A design framework for recommender system by incorporating sequential information
Rajhans Mishra, Pradeep Kumar, Bharat Bhasker · 2011
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.