Contextual information based recommender system using Singular Value Decomposition

The web contains a large collection of data, this is where the need for recommender system arises. A recommender system helps user to come to a decision quickly. In the conventional recommendation system only the reviewer's ratings are taken into consideration. However, contextual information pertaining to each user should be incorporated in the recommendation system, making the recommendation personalized. As some features can enhance the performance of a recommendation system and also certain irrelevant features might degrade it, feature selection becomes an essential aspect of context aware recommendation system. In our paper we have devised a novel approach which first selects relevant contextual variables based on the contextual information of the reviewers and their ratings for a class of entities, with naive Bayes classifier. Once the relevant contextual variables are extracted, Singular Value Decomposition (SVD) is applied for extracting most significant features corresponding to each entity. This information is used by the recommendation system in analyzing the contextual information of the user in recommending him entities that are of interest to him. The proposed method also determines the best contextual variable and feature space for each entity. This enables the context aware recommendation system more efficient and personalized. Moreover, with the proposed method an overall increase in F-score of 30% was obtained thereby improving the reliability of the recommender system.