Enhancing Accuracy of Recommender System through Adaptive Similarity Measures Based on Hybrid Features

Abstract

Collaborative Filtering techniques offer recommendations to users by leveraging on the preferences of like-minded users. They thus rely highly on similarity measures to determine proximity between users. However, most of the previously proposed similarity measures are heuristics based and are not guaranteed to work well under all data environments. We propose a method employing Genetic algorithm to learn user similarity based on comparison of individual hybrid user features. The user similarity is determined for each feature by learning a feature similarity function. The rating for each item is then predicted as an aggregate of estimates garnered from predictors based on each attribute. Our method differs from previous attempts at learning similarity, as the features considered for comparison take into account not only user preferences but also the item contents and user demographic data. The proposed method is shown to outperform existing filtering methods based on user-defined similarity measures.