Sense-Based Topic Word Embedding Model for Item Recommendation

As a useful way to help users filter information and save time, item recommendation intends to recommend new items to users who tend to be interested. As the most common format related to items in online social networks, short texts have always been disregarded by previous research on item recommendation. The sparse features and insufficient information in short texts render the extraction of features from short texts difficult. To address the problems of short text feature extraction and item recommendation, we introduce a sense-based word embedding method to enrich word features and aid in item topic extraction. The sense is used to demonstrate the semantic meaning of a word since each word contains different senses, and each sense can be used to describe various words. Thus, the sense is befitting to enrich the information of short texts and address the problem of polysemy. By combining topic distribution, social relationships, and users' interests and interactions, we propose a time-aware probabilistic model to profile a user's preference score on items. By predicting the user preference score at a future time interval, the items with top scores can be regarded as the recommended items. Different experiments on real-world datasets are deployed to prove the feasibility and efficiency of sense-based short text topic assignment and item recommendation in multiple languages. The prediction results also demonstrate that the model substantially outperforms the compared methods.