Graph data is an important data type for representing the relationships between individuals,
and many research works are conducted based on graph data. In the real-world, graph data …

The different news clicked by users reflects the diverse interests of users. Most of the existing
news recommendation methods do not consider the interaction with candidate news in the …

With the explosion of information, recommendation systems have become important for
users to find their interested information. Existing recommendation methods mainly utilize …

Cross-domain recommender systems could potentially improve the recommendation
performance by means of transferring abundant knowledge from the auxiliary domain to the …

With the application of collaborative filtering and deep neural network in news
recommendation, it becomes more feasible and easier to capture users' preferences of news …

Recently, a new type of social network, the event-based social network (EBSN), has become
popular. Typical EBSN platforms include Meetup, Plancast, Doublan, etc. In an EBSN …

Recommender systems filter out information that meets user interests. However, users may
be tired of the recommendations that are too similar to the content they have been exposed …

Using graph neural networks to model recommendation scenarios can effectively capture
high-order relationship features between objects, thereby helping the model better handle …

TV program recommendation is very important to avoid confusing users with large amounts
of information. The existing methods are mainly based on collaborative filtering to utilize the …

In the literature, most previous studies on English implicit inter-sentence relation recognition
only focused on semantic interactions, which could not exploit the syntactic interactive …