Abstract:

In order to address the  cold start problem in the recommendation system, based on the collaborative theme regression (CTR) model, we introduce the stacked denoising autoencoder (SDAE) deep learning network to deeply learn the implicit representation of user auxiliary information, and establish a SDAE-CTR model. The model utilizes a two-layered SDAE network, takes the implicit representation obtained in the encoding process and the approximate representation obtained in the decoding process as the two network outputs , and determines the optimal implicit representation by minimizing the difference between the user auxiliary information and the approximate representation. The model utilizes a user-item scoring matrix (cold start condition without scoring), item content information and user auxiliary information to predict user ratings for unrated items. We compared the SDFM-CTR to the CTR model in recommendation accuracy, novelty, and recommendation effect under the user coldstart condition on the LastFM, Book Crossing, and Movie Lens datasets. The results show that the SDAE-CTR is better than the latter under the conditions of cold start or noncold start, the degree of novelty is theoretically within a reasonable range, and its overall performance is better.

Key words: