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Subject:
From:
Tammie T Dudley <[log in to unmask]>
Reply To:
PhD Student <[log in to unmask]>
Date:
Mon, 20 Mar 2017 12:50:18 +0000
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Dear Student
It is my pleasure to invite you for research seminar, to be given by our Assistant/Associate Professor candidate, Dr. Bin Liu. Please, help me to spread the word and send it to your students and collaborators. The candidate requested that we do not advertise his visit on the Web, so please keep it under Goggle's radars when advertising. [😊]
Here are the details:
Name of Speaker:  Dr. Bin Liu
Place: CS Conference Room, 755 in 25 PP Building
Time: 11:00 am – noon on Thursday, March 23rd
Title: Personalized Recommendations in Mobile Business Environments
Abstract:  Recent years have witnessed a rapid adoption of smart mobile devices and their increased pervasiveness into people’s daily life. As a result of this quick development, the demand for better mobile services is increasing with an even faster speed. Recommender systems become essential to deliver the right services to the right mobile users. For instance, Point of Interest (POI) recommendation enables us to recommend the right places to the right users based on their preferences. In this talk, I will discuss several unique challenges for recommendation in mobile business environments, and then introduce how we use advanced data mining techniques to address these challenges. First, many mobile services are location-dependent. I will show how we can effectively model a user’s spatial choice behavior through the example of point of interest recommendation. Along this line, we have proposed a geographical probabilistic factor model framework, which strategically captures user mobility and geographical influence, to model user spatial choice behavior. Extensive experiments demonstrate the effectiveness of the proposed approach. Second, services are usually organized into hierarchy structure such as category hierarchy. I will then introduce a structural user choice model (SUCM) to learn fine-grained user choice patterns by exploiting hierarchy structure. Evaluation on an app adoption data demonstrates that our approach can better capture user choice patterns and thus improve recommendation performance. Finally, privacy becomes a big issue for mobile service adoption. Through an example of app recommendation, I will briefly introduce how recommendation can be improved by considering users’ privacy preferences.
Bio: Bin Liu received his Ph.D. in Management (Information Systems) from Rutgers Business School, Rutgers University in 2016. He is currently a Postdoctoral Researcher at Thomas J. Watson Research Center. He is interested in data mining/machine learning, and their intersection with recommender systems, healthcare, location/mobile, and security/privacy in social information systems. He has published in refereed journals and conference proceedings, such as IEEE Transactions on Knowledge and Data Engineering (TKDE), ACM Transactions on Knowledge Discovery from Data (TKDD), ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), ACM International Conference on Web Search and Data Mining (WSDM), SIAM International Conference on Data Mining (SDM), IEEE Conference on Data Mining (ICDM), ACM International Conference on Information and Knowledge Management (CIKM), and USENIX Security Symposium (USENIX Security).

See you all at the seminar! [😊]
Rafal


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