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Abstract

Recommender systems these days have changed the way people search for new items, information, and even other person on social networking. Recommender systems look for pattern of behavior of users to know what that particular user will prefer from catalogue of items he has never experienced before. Evolution of the technology behind recommender systems has taken place from the past 20 years. This paper proposed a hybrid recommender system for research papers, which can be used as the alternative to the current systems. This system is hybridization of content filtering and collaborative filtering techniques. This system based on bottom up approach to classify research papers i.e. users can tag their papers with their own keywords. This system uses the content and collaborative filtering techniques along with author analysis, date analysis and title analysis. This system also optimized the retrieved results by using collaborative approaches like user to item techniques and item to item technique. Cold start problem for the new items also eliminated in proposed system.

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Keywords

Recommender system, collaborative tagging, content filtering, collaborative filtering, and cold start problem.