Trust-Aware Hybrid Collaborative Recommendation with Locality-Sensitive Hashing
Dejuan Li, James A. Esquivel
Abstract
This paper introduces a novel trust-aware hybrid recommendation framework that combines Locality-Sensitive Hashing (LSH) with the trust information in social networks, aiming to provide efficient and effective recommendations. Unlike traditional recommender systems which often overlook the critical influence of user trust, our proposed approach infuses trust metrics to better approximate user preferences. The LSH, with its intrinsic advantage in handling high-dimensional data and computational efficiency, is applied to expedite the process of finding similar items or users. We innovatively adapt LSH to form trust-aware buckets, encapsulating both trust and similarity information. These enhancements mitigate the sparsity and scalability issues usually found in existing recommender systems. Experimental results on a real-world dataset confirm the superiority of our approach in terms of recommendation quality and computational performance. The paper further discusses potential applications and future directions of the trust-aware hybrid recommendation with LSH.