关键词: 信息推荐, 堆叠降噪自编码器, 精确欧氏局部敏感哈希, 数据降维, 冷启动

Abstract: There are several problems in the recommendation method based on k nearest neighbors, such as high dimensionality of rating features, slow speed of searching nearest neighbors and cold start problem of ratings. To solve these problems, a recommendation method based on k nearest neighbors using data dimensionality reduction and Exact Euclidean Locality-Sensitive Hashing (E²LSH) was proposed. Firstly, the rating data, the user attribute data and the item category data were integrated as the input data to train the Stack Denoising Auto-encoder (SDA) neutral network, of which the last hidden layer values were used as the feature coding of the input data to complete data dimensionality reduction. Then, the index of the reduced dimension data was built by the Exact Euclidean Local-Sensitive Hash algorithm, and the target users or the target items were retrieved to get their similar nearest neighbors. Finally, the similarities between the target and the neighbors were calculated, and the target user's similarity-weighted prediction rating for the target item was obtained. The experimental results on standard data sets show that the mean square error of the proposed method is reduced by an average of about 7.2% compared with the recommendation method based on Locality-Sensitive Hashing (LSH-ICF), and the average run time of the proposed method is the same as LSH-ICF. It shows that the proposed method alleviates the rating cold start problem on the premiss of keeping the efficiency of LSH-ICF.

Key words: information recommendation, Stack Denoising Auto-encoder (SDA), Exact Euclidean Locality-Sensitive Hashing (E²LSH), data dimensionality reduction, cold start