Abstract
Collaborative filtering is a popular approach in recommender Systems that helps users in identifying the items they may like in a wagon of items. Finding similarity among users with the available item ratings so as to predict rating(s) for unseen item(s) based on the preferences of likeminded users for the current user is a challenging problem. Traditional measures like Cosine similarity and Pearson correlation’s correlation exhibit some drawbacks in similarity calculation. This paper presents a new similarity measure which improves the performance of Recommender System. Experimental results on MovieLens dataset show that our proposed distance measure improves the quality of prediction. We present clustering results as an extension to validate the effectiveness of our proposed method.
Highlights
In the age of digital erudition, glut of massive data is generated in every field of science and Technology due to availability of automated tools and techniques for data generation and data collection
We propose a new similarity measure which performs better than Cosine similarity and Pearson correlation
Cosine similarity performed better than Pearson correlation when top-5 neighbors is considered on ua dataset (Fig:3 & Fig:4)but Pearson correlation performed better when neighborhood size is increased
Summary
In the age of digital erudition, glut of massive data is generated in every field of science and Technology due to availability of automated tools and techniques for data generation and data collection. Recommender systems call for Intelligent Information Retrieval Techniques to provide a solution to the problem of triumphant information search by applying the practice of knowledge detection in the available colossal data to provide individual personalized recommendations. Recommender systems can be described as services for suggesting a list of products to people who might tend to like the same. The extent of liking of a product by a user is termed as rating in Recommender systems. Recommender systems are usually classified into the following categories, based on how recommendations are made [1]
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