Content-based movie recommending using a Triple Wing Harmonium model

A content-based movie recommender by using a Triple Wing Harmonium (TWH) model is proposed. TWH integrates text metadata into a low dimensional semantic space. movie synopsis, actor list and user comments are considered as the text metadata. A new TWH model is developed by projecting these multiple textual features into low dimensional latent topics. We have used a contrastive divergence algorithm for efficient learning and inference. Experimental results show that the proposed method performs better than the state-of-the-art content-based algorithms for movie recommendation.