Building a successful recommender system depends on understanding both the dimensions
of people's preferences as well as their dynamics. In certain domains, such as fashion …

Modern recommender systems model people and items by discovering orteasing apart'the
underlying dimensions that encode the properties of items and users' preferences toward …

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 …

In recent years, interest in recommender research has shifted from explicit feedback towards
implicit feedback data. A diversity of complex models has been proposed for a wide variety …

The objective of a recommender system is to provide customers with personalized
recommendations while selecting an item among a set of products (movies, books, etc.). The …

Group recommendation has attracted significant research efforts for its importance in
benefiting a group of users. This paper investigates the Group Recommendation problem …

The recommender systems aim to predict potential demands of users by analyzing their
preferences and provide personalized recommendation services. User preferences can be …

In information retrieval, query auto completion (QAC), also known as typeahead [**ao et al.,
2013, Cai et al., 2014b] and auto-complete suggestion [Jain and Mishne, 2010], refers to the …

With the rapid proliferation of images on e-commerce platforms today, embracing and
integrating versatile information sources have become increasingly important in …

With the explosive growth of online social networks, it is now well understood that social
information is highly helpful to recommender systems. Social recommendation methods are …