M
Mao Ye
Researcher at Pennsylvania State University
Publications - 34
Citations - 4944
Mao Ye is an academic researcher from Pennsylvania State University. The author has contributed to research in topics: Wireless sensor network & Social network. The author has an hindex of 19, co-authored 33 publications receiving 4624 citations. Previous affiliations of Mao Ye include Hewlett-Packard & Nanjing University.
Papers
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Proceedings ArticleDOI
Exploring social influence for recommendation: a generative model approach
TL;DR: Experimental results show that social influence captured based on the proposed probabilistic generative model, called social influenced selection (SIS), is effective for enhancing both item recommendation and group recommendation, essential for viral marketing, and useful for various user analysis.
Proceedings ArticleDOI
On the semantic annotation of places in location-based social networks
TL;DR: A semantic annotation technique for location-based social networks to automatically annotate all places with category tags which are a crucial prerequisite for location search, recommendation services, or data cleaning is developed.
Proceedings ArticleDOI
Location recommendation for out-of-town users in location-based social networks
TL;DR: This paper proposes a collaborative recommendation framework, called User Preference, Proximity and Social-Based Collaborative Filtering} (UPS-CF), to make location recommendation for mobile users in LBSNs and finds that preference derived from similar users is important for in-town users while social influence becomes more important for out-of- town users.
Proceedings ArticleDOI
Exploring personal impact for group recommendation
TL;DR: This paper analyzes the decision making process in a group to propose a personal impact topic (PIT) model for group recommendations, which effectively identifies the group preference profile for a given group by considering the personal preferences and personal impacts of group members.
Proceedings ArticleDOI
What you are is when you are: the temporal dimension of feature types in location-based social networks
TL;DR: This work extracts user check-ins from massive real-world data crawled from Location-based Social Networks to understand the temporal dimension of Points Of Interest.