2 citations · 3 across the 3 of their papers we have counts for
5 papers
Detect Professional Malicious User with Metric Learning in Recommender Systems
Yuanbo Xu, Yongjian Yang, En Wang +2
In e-commerce, online retailers are usually suffering from professional malicious users (PMUs), who utilize negative reviews and low ratings to their consumed products on purpose t…
A Unified Collaborative Representation Learning for Neural-Network based Recommender Systems
Yuanbo Xu, En Wang, Yongjian Yang +1
Most NN-RSs focus on accuracy by building representations from the direct user-item interactions (e.g., user-item rating matrix), while ignoring the underlying relatedness between…
Generating Self-Serendipity Preference in Recommender Systems for Addressing Cold Start Problems
Yuanbo Xu, Yongjian Yang, En Wang
Classical accuracy-oriented Recommender Systems (RSs) typically face the cold-start problem and the filter-bubble problem when users suffer the familiar, repeated, and even predict…
Cell Selection with Deep Reinforcement Learning in Sparse Mobile Crowdsensing
Leye Wang, Wenbin Liu, Daqing Zhang +3
Sparse Mobile CrowdSensing (MCS) is a novel MCS paradigm where data inference is incorporated into the MCS process for reducing sensing costs while its quality is guaranteed. Since…
Urban contact structures for epidemic simulations: Correcting biases in data-driven approaches
Zhanwei Du, Chao Gao, Yuan Bai +2
Epidemics are emergent phenomena depending on the epidemiological characteristics of pathogens and the interaction and movement of people. Public transit systems have provided much…