Learning disentangled representations for user intentions from multi-feedback (ie, positive
and negative feedback) can enhance the accuracy and explainability of recommendation …

Modern recommenders usually consider both collaborative features from user behavior data
(eg, clicks) and content information about the users and items (eg, user ages or item images) …

For better user satisfaction and business effectiveness, more and more attention has been
paid to the sequence-based recommendation system, which is used to infer the evolution of …

Recommendation is crucial in both academia and industry, and various techniques are
proposed such as content-based collaborative filtering, matrix factorization, logistic …

User behavior data in recommender systems are driven by the complex interactions of many
latent factors behind the users' decision making processes. The factors are highly entangled …

There exist complex interactions among a large number of latent factors behind the decision
making processes of different individuals, which drive the various user behavior patterns in …

There exist complex patterns behind the decision-making processes of different individuals
across different environments. For instance, in a social recommender system, various user …

In many recommender systems, users express item opinions through two kinds of behaviors:
giving preferences and writing detailed reviews. As both kinds of behaviors reflect users' …

Personalized recommendation has become increasingly pervasive nowadays. Users
receive recommendations on products, movies, point-of-interests and other online services …

The ubiquity of implicit feedback makes them the default choice to build online
recommender systems. While the large volume of implicit feedback alleviates the data …