Dynamic Circle Recommendation: A Probabilistic Model

[[abstract]]This paper presents a novel framework for dynamic circle recommendation for a query user at a given time point from historical communication logs. We identify the fundamental factors that govern interactions and aim to automatically form dynamic circle for scenarios, such as, who should I dial to in the early morning? whose mail would I reply first at midnight? We develop a time-sensitive probabilistic model (TCircleRank) that not only captures temporal tendencies between the query user and candidate friends but also blends frequency and recency into group formation. We also utilize the model to support two types of dynamic circle recommendation: Seedset Generation: single-interaction suggestion and Circle Suggestion: multiple interactions suggestion. We further present approaches to infer relevant time interval in determining circles for a query user at a given time. Experimental results on Enron dataset, Call Detail Records and Reality Mining Data prove the effectiveness of dynamic circle recommendation using TCircleRank.[[incitationindex]]EI[[conferencetype]]國際[[conferencedate]]20140513~20140516[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Tainan, Taiwa

Probabilistic Personalized Recommendation Models For Heterogeneous Social Data

Content recommendation has risen to a new dimension with the advent of platforms like Twitter, Facebook, FriendFeed, Dailybooth, and Instagram. Although this uproar of data has provided us with a goldmine of real-world information, the problem of information overload has become a major barrier in developing predictive models. Therefore, the objective of this The- sis is to propose various recommendation, prediction and information retrieval models that are capable of leveraging such vast heterogeneous content. More specifically, this Thesis focuses on proposing models based on probabilistic generative frameworks for the following tasks: (a) recommending backers and projects in Kickstarter crowdfunding domain and (b) point of interest recommendation in Foursquare. Through comprehensive set of experiments over a variety of datasets, we show that our models are capable of providing practically useful results for recommendation and information retrieval tasks