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Peifeng Yin
Researcher at IBM
Publications - 48
Citations - 2286
Peifeng Yin is an academic researcher from IBM. The author has contributed to research in topics: Cloud computing & Chaotic. The author has an hindex of 13, co-authored 47 publications receiving 2091 citations. Previous affiliations of Peifeng Yin include Pennsylvania State University.
Papers
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Proceedings ArticleDOI
Exploiting geographical influence for collaborative point-of-interest recommendation
TL;DR: This paper argues that the geographical influence among POIs plays an important role in user check-in behaviors and model it by power law distribution, and develops a collaborative recommendation algorithm based on geographical influence based on naive Bayesian.
Proceedings ArticleDOI
Location recommendation for location-based social networks
TL;DR: A friend-based collaborative filtering approach for location recommendation based on collaborative ratings of places made by social friends is developed, and a variant of FCF technique, namely Geo-Measured FCF (GM-FCF), based on heuristics derived from observed geospatial characteristics in the Foursquare dataset is proposed.
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
App recommendation: a contest between satisfaction and temptation
TL;DR: This work proposes an Actual- Tempting model that captures factors that invoke a user to replace an old app with a new app and shows that the AT model performs significantly better than the conventional recommendation techniques such as collaborative filtering and content-based recommendation.
Proceedings ArticleDOI
Silence is also evidence: interpreting dwell time for recommendation from psychological perspective
TL;DR: This work aims to enrich the user-vote matrix by converting the dwell time on items into users' ``pseudo votes'' and then help improve recommendation performance, and shows that the traditional rate-based recommendation's performance is greatly improved with the support of VV model.