Transfer learning aims at improving the performance of target learners on target domains by
transferring the knowledge contained in different but related source domains. In this way, the …

Traditional recommendation systems are faced with two long-standing obstacles, namely
data sparsity and cold-start problems, which promote the emergence and development of …

Supervised machine learning techniques have already been widely studied and applied to
various real-world applications. However, most existing supervised algorithms work well …

The proliferation of e-commerce sites and online social media has allowed users to provide
preference feedback and maintain profiles in multiple systems, reflecting a variety of their …

Cross domain recommender systems (CDRS) can assist recommendations in a target
domain based on knowledge learned from a source domain. CDRS consists of three …

One-class collaborative filtering or collaborative ranking with implicit feedback has been
steadily receiving more attention, mostly due to the “oneclass” characteristics of data in …

Intelligent recommendation technology has been playing an increasingly important role in
various industry applications such as e-commerce product promotion and Internet …

Implicit feedbacks have recently received much attention in recommendation communities
due to their close relationship with real industry problem settings. However, most works only …

This study presents a data-driven method to monitor customer complaints for efficient service
quality management. Recognising the value of customer reviews as a pool of'voice of the …

Friend recommendation is an important recommender application in social media. Major
social websites such as Twitter and Facebook are all capable of recommending friends to …