Addressing Cold Start Problem in Recommendation Systems with Collaborative filtering and Reverse Collaborative Filtering
DOI:
https://doi.org/10.26438/ijcse/v6i4.211214Keywords:
Personalization, Profiles, Recommendation Systems, Cold Start ProblemAbstract
Today Recommender system predicts the future preferences of the user based on the user’s profile. A number of approaches have been taken to address the issue of recommendations, be it user based filtering methods, item-based filtering methods etc. The popular is Collaborative filtering technique used by some renowned companies like Amazon, YouTube and others. But the problem that still holds is the cold start problem and the amount of time and accuracy that is associated with these algorithms. A recent improvement suggested is the Reverse Collaborative filtering for the accuracy and pre-processing time. This paper implements and compares collaborative and reverse collaborative filtering solutions to address the cold start problem.
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