## 1 Introduction  
Community is the basis for various recommendation applications, such as friend recommendation, precision marketing, and activity organization [1; 2; 3; 4]. Compared with community detection, community search can better explain the reasons for the formation of the community by personalized search according to the query criteria specified by the user. Therefore, community search has attracted extensive attention in academia and industry. Many query models and algorithms have emerged to serve various query scenarios. Unfortunately, the existing models [2; 3; 4; 5] do not fully describe users, especially their representative attributes (including text and spatial attributes). Take the geographic social network Foursquare as an example: it consists of millions of nodes, where each node represents a user, and an edge represents the friendship between two users. Users check in at different times and places. To make the query closer to reality, we should describe users based on multiple dimensions, such as spatial distance, social relations, interest attributes, and time. However, these models only judge the representative attributes of users according to the frequency of keywords. It is worth emphasizing that time plays an extremely important role in establishing social networks. The time change of users' check-in records can best reflect their real interest trends. Especially in the recommendation system, we are more concerned with the short-term interests of users. The more recent the attributes are, the stronger the ability to represent users' current interests, and the greater the weight that should be assigned. Therefore, selecting representative attributes only according to the frequency of keywords is inappropriate. To alleviate the query dilemma caused by incomplete query dimensions and harsh query conditions, we propose a new community search model--Time-Weighted Community Search Based on Interest (TWC). When a user wants to find friends with interests similar to their own recent interests to form a community, TWC can provide efficient queries. TWC comprehensively considers the impact of space, structure, text, and time on the community and requires as few parameters as possible to start the query. Specifically, given a geographic social network graph, a query vertex, and a positive integer, TWC aims to find a subgraph--that is, a connected k-core--with the highest attribute similarity score. TWC does not require users to specify query keywords, avoiding low-quality communities caused by inappropriate keywords. It extracts representative attributes from users' historical records and better explains the reasons for the formation of the community. At the same time, we calculate their weights based on the check-in time and frequency of the attributes. On this basis, a new node similarity scoring function and a subgraph scoring method are designed. In this article, we assume that the greater the attribute weight, the stronger the ability to represent users' current interests. Figure 1 shows an example of a social network diagram and the weighted attribute list of nodes. Our main contributions are as follows:  
1. Fully considering the characteristics of geographic social networks, we propose a new community search model, Time-Weighted Community Search Based on Interest (TWC), by considering the four dimensions of structural cohesiveness, spatial cohesiveness, interest, and time. 2. We design the attribute time-weighted decay function and then extract the user's time-weighted representative attributes, which express the user's interest trend clearly in the query window. In addition, we propose a new attribute similarity scoring function and a community scoring function. 3. In order to solve the TWC problem, we design a Local Extend algorithm from inside to outside and a Shrink algorithm from outside to inside to deal with different search scenarios. 4. We carry out many experiments on the real dataset.