Dynamic Intention-Aware Recommendation System

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 beŠer facilitate users to €nd 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 Œings, it is capable of delivering more satisfactory recommendations by leveraging rich online and o„ine user data; (2) it embraces the bene€ts of embedding heterogeneous source information and shared representations of multiple domains to provide accurate and e‚ective recommendations comprehensively; (3) it recommends products or services proactively and timely by capturing the dynamic inƒuences, which can signi€cantly reduce user involvements and e‚orts.