关键词: 哈希学习, 推荐系统, 相似性, 用户偏好, 偏置

Abstract:

Hashing techniques can effectively solve the storage and retrieval efficiency problems faced by Recommender Systems（RSs）. However, one issue of applying hashing to RSs is that RSs focus on modeling user’s preference over items rather than their similarities concerned by hashing. Therefore, an improved hashing for efficient recommendation method is proposed. The mean of each user and item relative scoring system is considered as a bias. The rating is mapped to the similarity interval by subtracting the bias term. For preserving the similarity mentioned above, two methods are proposed to decompose the similarity matrix to obtain user and item binary codes. Extensive experiments performed on three real-world benchmarks show that the method outperforms the state-of-the-art methods.

Key words: hashing, recommender systems, similarities, user’s preference, bias