Personalized recommendation: an enhanced hybrid collaborative filtering

Abstract

Commonly used similarity-based algorithms in memory-based collaborative filtering may provide unreliable and misleading results. In a cold start situation, users may find the most similar neighbors by relying on an insufficient number of ratings, resulting in low-quality recommendations. Such poor recommendations can also result from similarity metrics as they are incapable of capturing similarities among uncommon items. For example, when identical items between two users are popular items, and both users rated them with high scores, their different preferences toward other items are hidden from similarity metrics. In this paper, we propose a method that estimates the final ratings based on a combination of multiple ratings supplied by various similarity measures. Our experiments show that this combination benefits from the diversity within similarities and offers high-quality personalized suggestions to the target user.