Indian Journal of Science and Technology

Abstract

Objectives: Recommendation engine is a part of information filtering system that tries to predict the ‘preference’ or ‘rating’ of an item in the E-commerce. Recommendation engines have become extremely common in recent days to make an appropriate recommendation rapidly and effectively about any products on which the user is interested. Methods/ Statistical Analysis: One of popular information filtering systems in the recommendation engine is collaborative filtering where the predictions are made based on the usage patterns of the users who are similar to another user. The accuracy of a recommendation engine using collaborative filtering depends on the techniques used to measure the similarity between the user’s preferences. Therefore, in this paper we use two metrics to measure the similarity between the user’s preferences namely KL Divergences and Euclidean distance. The proposed algorithm works by first clustering the users using k means clustering by utilising the similarity metrics and then computing the global Markov matrix for that cluster. Next, the PageRank value for each user is computed and those values are combined with the global Markov matrix to find the recommendations. Findings: We consider the problem of collaborative filtering to recommend potential items of interest to a user already engaged in a session, using past session of the user and other users. Our algorithm leads to the personalized PageRank, where context is captured by the personalization vector. The results show that the collaborative filtering using Euclidean distance metrics for similarity measure performs well than the KL divergence. Application/ Improvements: The proposed recommendation engine can be used in a wide variety of applications such music, movies, books, news, research articles, social media, search queries, and products in general in order to provide a effective recommendation
Keywords: Collaborative Filtering, Euclidean Distance, Jaccard Metric, Recommendation Engine, Similarity Metrics