Recommender systems play an important role in hel** online users find relevant
information by suggesting information of potential interest to them. Due to the potential value …

Context-aware recommender systems leverage the value of recommendations by exploiting
context information that affects user preferences and situations, with the goal of …

Online communities such as Facebook and Twitter are enormously popular and have
become an essential part of the daily life of many of their users. Through these platforms …

Social recommendation has emerged to leverage social connections among users for
predicting users' unknown preferences, which could alleviate the data sparsity issue in …

In the era of big data, recommender system (RS) has become an effective information
filtering tool that alleviates information overload for Web users. Collaborative filtering (CF) …

With the tremendous success of Graph Convolutional Networks (GCNs), they have been
widely applied to recommender systems and have shown promising performance. However …

Recommending products to users means estimating their preferences for certain items over
others. This can be cast either as a problem of estimating the rating that each user will give …

With the advent and popularity of social network, more and more users like to share their
experiences, such as ratings, reviews, and blogs. The new factors of social network like …

Action localization networks are often structured as a feature encoder sub-network and a
localization sub-network, where the feature encoder learns to transform an input video to …

This chapter provides an accessible introduction for point processes, and especially Hawkes
processes, for modeling discrete, inter-dependent events over continuous time. We start by …