Recommender systems have been widely applied to several domains and applications to
assist decision making by recommending items tailored to user preferences. One of the …

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

Sales diversity is considered a key feature of Recommender Systems from a business
perspective. Sales diversity is also linked with the long-tail novelty of recommendations, a …

In this paper, we study the problem of retrieving a ranked list of top-N items to a target user in
recommender systems. We first develop a novel preference model by distinguishing different …

It is traditionally a challenge for home buyers to understand, compare and contrast the
investment values of real estates. While a number of estate appraisal methods have been …

Recommender systems are intelligent programs to suggest relevant contents to users
according to their interests which are widely expressed as numerical ratings. Collaborative …

With the advent and popularity of social network, more and more people like to share their
experience in social network. However, network information is growing exponentially which …

The two main tasks in the Recommender Systems domain are the ranking and rating
prediction tasks. The rating prediction task aims at predicting to what extent a user would like …

Data sparsity, that is a common problem in neighbor-based collaborative filtering domain,
usually complicates the process of item recommendation. This problem is more serious in …

Trust-aware recommender systems are advanced approaches which have been developed
based on social information to provide relevant suggestions to users. These systems can …