  Recommender systems have been actively and extensively studied over past
decades. In the meanwhile, the boom of Big Data is driving fundamental changes
in the development of recommender systems. In this paper, we propose a dynamic
intention-aware recommender system to better facilitate users to find desirable
products and services. Compare to prior work, our proposal possesses the
following advantages: (1) it takes user intentions and demands into account
through intention mining techniques. By unearthing user intentions from the
historical user-item interactions, and various user digital traces harvested
from social media and Internet of Things, it is capable of delivering more
satisfactory recommendations by leveraging rich online and offline user data;
(2) it embraces the benefits of embedding heterogeneous source information and
shared representations of multiple domains to provide accurate and effective
recommendations comprehensively; (3) it recommends products or services
proactively and timely by capturing the dynamic influences, which can
significantly reduce user involvements and efforts.
