Learning topics from short texts has become a critical and fundamental task for
understanding the widely-spread streaming social messages, eg, tweets, snippets and …

High-dimensional and sparse (HiDS) matrices generated by recommender systems contain
rich knowledge regarding various desired patterns like users' potential preferences and …

When users in online social networks make a decision, they are often affected by their
neighbors. Social recommendation models utilize social information to reveal the impact of …

With the accessibility to information, users often face the problem of selecting one item (a
product or a service) from a huge search space. This problem is known as information …

The explosive increasing of the social media data on the Web has created and promoted the
development of the social media big data mining area welcomed by researchers from both …

Emotional conversation generation has elicited a wide interest in both academia and
industry. However, existing emotional neural conversation systems tend to ignore the …

Recently, with the fast proliferation of smart phones, mobile phone has the powerful ability of
not only communication but also computation. Human beings are not only data consumers …

A non-negative latent factor analysis (NLFA)-based recommender can make precise
recommendations by correctly representing the non-negative characteristic of industrial …

Nowadays plenty of user-generated posts, eg, sina weibos, are published on the social
media. The posts contain the public's sentiments (ie, positive or negative) towards various …

Social media consists of various kinds of emotions and sentiments of its users in the form of
electronic media. To analyze the reactions or sentiments of the users on a certain post is …