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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…
Out-of-Town Recommendation with Travel Intention Modeling
Haoran Xin, Xinjiang Lu, Tong Xu +4
Out-of-town recommendation is designed for those users who leave their home-town areas and visit the areas they have never been to before. It is challenging to recommend Point-of-I…
Spatial Object Recommendation with Hints: When Spatial Granularity Matters
Hui Luo, Jingbo Zhou, Zhifeng Bao +5
Existing spatial object recommendation algorithms generally treat objects identically when ranking them. However, spatial objects often cover different levels of spatial granularit…
Collaborative Company Profiling: Insights from an Employee's Perspective
Hao Lin, Hengshu Zhu, Yuan Zuo +3
Company profiling is an analytical process to build an indepth understanding of company's fundamental characteristics. It serves as an effective way to gain vital information of th…
Structural Analysis of User Choices for Mobile App Recommendation
Bin Liu, Yao Wu, Neil Zhenqiang Gong +3
Advances in smartphone technology have promoted the rapid development of mobile apps. However, the availability of a huge number of mobile apps in application stores has imposed th…