A SURVEY OF RECOMMENDER SYSTEM TYPES AND ITS CLASSIFICATION

The current generation is finding it difficult to find the right information from the enormous amount of data they are presented with in the online platforms. It is hard to spent time online searching for information in such a scenario and it craves for the need of an information filtering system that could help them discover the information they seek. A research field that does this has emerged in the last few years called as recommender systems. A lot of extensive research is happening in the field which is trying to incorporate more attributes to give more precise and relevant personalised recommendations to a user. This paper is focused on reviewing some significant works in the three basic recommender system types including collaborative filtering, content based filtering and hybrid filtering. The paper also have identified and listed the major challenges faced by recommender systems. The main contribution of the paper is in proposing a novel hybrid recommender system which addresses the sparsity and serendipity drawback of recommender systems. The proposed method is expected to deliver more accurate, relevant and novel predictions.

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