Basser Seminar Series

Exploiting content, structure, and communities in recommender systems

Speaker: Dr Julian McAuley
Stanford University

Time: THURSDAY 24 October 2013, 4:00-5:00pm

Location: The University of Sydney, School of IT Building, Lecture Theatre (Room 123), Level 1

**Note different day

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Abstract

Recommender systems have transformed the way users discover and evaluate products on the web. In order to recommend products (or webpages, ads, etc.) to users we must uncover the implicit tastes of each user as well as the properties of each product. In addition to users' ratings, there are many sources of structure and side-information that may help with this task. For example, how can the text of users' reviews be exploited to better understand why users rated products the way they did? How can we model users' expertise in settings where users have 'acquired tastes'? How does the title of a product, or the community in which it is marketed, influence its chance of being adopted? In this talk I'll present a series of papers that try to answer such questions. To do so, we'll introduce large-scale datasets and models from traditional review communities (such as Yelp and Amazon), to niche communities (such as RateBeer and CellarTracker), and social media communities (such as Reddit).

Speaker's biography

Julian McAuley is currently a postdoctoral scholar at Stanford University, where he works with Jure Leskovec on modeling the structure and dynamics of social networks. His current work on recommender systems is concerned with modeling user behaviour in online communities, especially in terms to their linguistic and temporal dimensions. Previously, Julian received his PhD from the ANU under Tiberio Caetano, with whom he worked on inference and learning in structured output spaces. His work has recently been featured in Time, Forbes, New Scientist, and Wired, among others.