Event Recommendation Strategy Combining User Long-Short Term Interest and vent Influence

The problem of information overload caused by the rapid development of event-based social network is the main challenge currently. Those technologies such as deep learning can mine potential relationship from a large amount of data to effectively cope with this problem. At the same time, some studies show that user interests have different characteristic patterns in long-term time series and short-term time series. In-depth mining of users’ time series characteristics and interests can effectively provide users with personalized event recommendation information. Based on this, a recommendation strategy is proposed, which combines long-short term user interests with event influences. It obtains the long-short term user interests by graph neural network and long-short term memory network together with attention mechanism. At the same time, it constructs the influence on the target user for candidate events, and predicts the participation probability of the target user based on the long-short term interests and event influences. Finally, TOP-K events are recommended to the users according to the sorted participation probability. The experimental results show that the proposed recommendation model has been improved on multiple indicators. Its recommendation performance is better than the several existing compared models, and it has a good recommendation effect.