RECOMMENDATION OF MOVIES UTILIZING REAL TIME USER INTEREST MODEL

This large volume of information requires techniques or tools for efficient extraction of required information. In this paper we proposed a new technique for the recommendation of movies utilizing real time user interest model. We have also evaluated slope one and its variants, weighted slope one and bipolar slope one, which are currently popular recommendation algorithm used by most of the memory based recommendation system. But due to various limitations like sparsity, cold start, of these algorithm limits the accuracy and performance of the predictions and hence quality of recommendations. The algorithm proposed here improved the existing slope one algorithm and increased the efficiency to a great extent. It's also very scalable; take less memory space as it reduces item search scope by grouping users according to user similarities based on real time genre rating information. Results prove that R-slope one algorithm gives better performance over other algorithm.