Collaborative Filtering Techniques for Recency Frequency Monetary Analysis

Recommendation engines act as a key for the e-commerce industries today. The immense growth of information on the internet and the number of users accessing the internet everyday increases the intricacy for recommendation engines. Despite all this, they should deliver the best recommendations to ensure customer satisfaction. For recommending items many algorithms have being developed that can address large-scale problems easily. Collaborative filtering has proved to be one of the best among all algorithms used, providing great achievements to e-commerce retailers today in terms of their versatility, the recommendations it provides, and the revenue generated. In this paper, a collaborative user-item filtering approach has been discussed elaborating about user-user and item-item filtering algorithms in detail.