Abstract The use of Online Social Networks has been rapidly increased over the last years.
In particular, Social Media Networks allow people to communicate, share, comment and …

For online product recommendation engines, learning high-quality product embedding that
captures various aspects of the product is critical to improving the accuracy of user rating …

Existing review-aware recommendation methods represent users (or items) through the
concatenation of the reviews written by (or for) them, and depend entirely on convolutional …

Recommender systems for online broadcasting become important as the number of
channels has been increasing. In online broadcasting, to provide accurate recommendation …

Motivated by the success of deep learning techniques, there are numerous deep learning
models developed for recommender systems. User-oriented autoencoder (UAE) and item …

The popularity of live streaming has led to the explosive growth in new video contents and
social communities on emerging platforms such as Facebook Live and Twitch. Viewers on …

Recently, listwise collaborative filtering (CF) algorithms are attracting increasing interest due
to their efficiency and prediction quality. Different from rating-oriented (pointwise) CF, they …

Traditional recommender systems assume that all the users are independent, and they
usually face the cold start and data sparse problems. To alleviate these problems, social …

We study the problem of active friending in online social networks. Given an initiator who
want to friend a target person on a social network, we propose a strategy to support active …

Social media recommendation differs from traditional recommendation approaches as it
needs considering not only the content information and users' similarities, but also users' …