Integrating users’ long-term and short-term interests with knowledge graph to improve restaurant recommendation

User preferences are typically analyzed by recommendation systems for recommending items. However, in most existing restaurant recommendation methods, recommendation similarity (e.g., reviews, sentiment) is considered only from the perspective of users, whereas restaurant attributes and potential connections between restaurants are ignored. Furthermore, the dining interests of users are diverse and not fixed. For improved recommendations, the changes of user interests should be considered. To address these problems, this study proposed a recommendation approach that combines knowledge graphs and long-term and short-term interests of users. In the approach, user preferences were analyzed from the attributes of restaurant using the semantic structure of knowledge graphs to obtain long-term user representations. Next, we filtered recent historical restaurants based on time series to obtain short-term user representations through the gated recurrent unit. To integrate long- and short-term interests, a novel gated structure was designed to obtain dynamic interest weights. Extensive experiments on a real-world dataset revealed that the proposed approach outperformed baseline methods.