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Regular version of the site

Book chapter

Similarity Aggregation for Collaborative Filtering

Ignatov D. I., Sarwar S. M., Hasan M., Billal M.

In this paper we show how several similarity measures can be combined for finding similarity between a pair of users for performing Collaborative Filtering in Recommender Systems. Through aggregation of several measures we find super similar and super dissimilar user pairs and assign a different similarity value for these types of pairs. We also introduce another type of similarity relationship which we call medium similar user pairs and use traditional JMSD for assigning similarity values for them. By experimentation with real data we show that our method for finding similarity by aggregation performs better than each of the similarity metrics. Moreover, as we apply all the traditional metrics in the same setting, we can assess their relative performance

In book

Similarity Aggregation for Collaborative Filtering
Vol. 542: Series: Communications in Computer and Information Science. Switzerland: Springer, 2015.