An Algorithmic Approach for Recommendation of Movie Under a New User Cold Start Problem

One of the most popular approach for personalized recommendations is the Collaborative filtering methods. The key point of this method is to find out similar users by calculating the similarities among them. The product is recommended to a user based on user-user similarity. For finding similarities, the measures like Cosine, Pearson correlation coefficient, Proximity-Impact-Popularity (PIP) measure, and Proximity-Significance-Singularity (PSS) measure can be used. The main issue with the recommendation system is the new user cold start problem where less ratings are available with the user. The user-user similarity matrix obtained with the help of above mentioned measures in terms of cold start problem is not more accurate. In this paper, we show that the hybrid measure is giving more accurate result then the other similarity measures. In this paper a detailed algorithm for the recommendation of a movie to a new cold start user for the hybrid measure is given. The experiments are done with Movie Lens dataset and the results are displayed in the form of user-user similarity metrics in order to obtain the user-user similarity matrix for the hybrid measure.