Most existing collaborative filtering techniques have focused on modeling the binary relation
of users to items by extracting from user ratings. Aside from users' ratings, their affiliated …

Recommender systems have become an essential tool to help resolve the information
overload problem in recent decades. Traditional recommender systems, however, suffer …

In recent years, deep neural networks have yielded immense success on speech
recognition, computer vision and natural language processing. However, the exploration of …

Federated recommender system (FRS), which enables many local devices to train a shared
model jointly without transmitting local raw data, has become a prevalent recommendation …

Point-of-interest (POI) recommendation is an important service to Location-Based Social
Networks (LBSNs) that can benefit both users and businesses. In recent years, a number of …

With the advent of the era of big data, the recommendation system has become an effective
solution to the problem of information overload. This paper takes the literature data related to …

In general, recommendation can be viewed as a matching problem, ie, match proper items
for proper users. However, due to the huge semantic gap between users and items, it's …

Geographical characteristics derived from the historical check-in data have been reported
effective in improving location recommendation accuracy. However, previous studies mainly …

Recommending users with their preferred points-of-interest (POIs), eg, museums and
restaurants, has become an important feature for location-based social networks (LBSNs) …

With the increasing amount of information on the internet, recommendation system (RS) has
been utilized in a variety of fields as an efficient tool to overcome information overload. In …