Personalized recommendation by integrating a neural topic model and Bayesian personalized ranking

Traditional recommendation systems typically sort items and provide users with top items by analyzing user–item interactions. Interactions vary from person to person because they are determined by personal intents and other environmental factors. However, these intents are implicit and difficult to capture because experimental data often contain plain user–item interactions. In this study, we proposed an attentive neural topic model (ANTM) to determine user latent intents and distinguish individual preferences. We first used the neural topic model in the natural language processing domain to discover user latent intents by encoding user–item interactions and jointly learned the model and variational parameters during inference. In addition, because of differences in user latent intents, we applied an attention mechanism to intents to obtain individual preferences. The representation of user features enriched by individual latent intents was then used to replace plain user profiles to provide personalized recommendations. Experimental results demonstrated that the proposed ANTM outperformed the best baseline algorithm by 1.09%–17.25% and 0.66%–10.38% in terms of the hit rate for recommending the top-5 and top-10 items, respectively. Moreover, its improvements over the best baseline algorithm were 0.69%–35.48% and 0.54%–15.48% in terms of normalized discounted cumulative gain in recommending the top-5 and top-10 items, respectively.