The creation of digital marketing has enabled companies to adopt personalized item
recommendations for their customers. This process keeps them ahead of the competition …

With the increase in the use of mobile devices having location-related capabilities, the use of
Location-Based Social Networks (LBSN) has also increased, allowing users to share …

Recommendation systems play a significant role in alleviating information overload in the
digital world. They provide suggestions to users based on past symmetric activities or …

Collaborative filtering (CF) is one of the most practical approaches on recommendation
systems by predicting users' preferences for items based on the user-item interaction …

Popularity of Location-based Social Networks (LBSNs) provides an opportunity to collect
massive multi-modal datasets that contain geographical information, as well as time and …

Recommender systems aim to capture user preferences and provide accurate
recommendations to users accordingly. For each user, there usually exist others with similar …

Collaborative filtering is a popular recommendation algorithm. It predicts user's interests
according to the ratings or behaviour of other users in the system. However, the …

With the increasing number of micro-video community applications, social media platforms
have started to attract more people through their built-in micro-video features. More and …