University of Cambridge > > Computer Laboratory Systems Research Group Seminar > Towards the Profiling of Twitter Users for Topic-Based Filtering + Collaborative Filtering For Recommendation

Towards the Profiling of Twitter Users for Topic-Based Filtering + Collaborative Filtering For Recommendation

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Talk1 – Towards the Profiling of Twitter Users for Topic-Based Filtering: Towards the Profiling of Twitter Users for Topic-Based Filtering: Real-time information streams such as Twitter have become a common way for users to discover new information. For most users this means curating a set of other users to follow. However, at the moment the following granularity of Twitter is restricted to the level of individual users. Our research has highlighted that many following relationships are motivated by a subset of interests that are shared by the users in question.

For example, user A might follow user B because of their technology related tweets, but shares little or no interest in their other tweets. As a result this all-or-nothing following relationship can quickly overwhelm users’ timelines with extraneous information. To improve this situation we propose a user profiling approach based on the topical categorisation of users’ posted URLs. These topics can then be used to filter information streams so that they focus on more relevant information from the people they follow, based on their core interests. In particular, we have built a system called CatStream that provides for a more fine-grained way to follow users on specific topics and filter our timelines accordingly. We present the results of a live-user study that shows how filtered timelines offer a better way to organise and filter their information streams.

Sandra Garcia Esparza graduated in Computer Science at Universitat Ramon Llull (Barcelona) in 2008. In 2009 she graduated from Trinity College Dublin with a MSc in Networks and Distributed Systems. She is currently in Dublin working on her PhD in CLARITY : Centre for Sensor Web Technologies (University College Dublin) in the area of Recommender Systems. Her PhD is about harnessing real-time data such as Twitter data to provide more personalised user experiences, including product recommendations and stream filtering.

Talk2 – Collaborative Filtering For Recommendation: In Online Social Networks In the past recommender systems have relied heavily on the availability of ratings data as the raw material for recommendation. Moreover, popular collaborative filtering approaches generate recommendations by drawing on the interests of users who share similar ratings patterns. This is set to change because the unbundling of social networks (via open APIs), providing a richer world of recommendation data. For example, we now have access to a richer source of ratings and preference data, across many item types. In addition, we also have access to mature social graphs, which means we can explore different ways of creating recommendations, often based on explicit social links and friendships. In this paper we evaluate a conventional collaborative filtering framework in the context of this richer source of social data and clarify some important new opportunities for improved recommendation performance.

Steven Bourke is currently a 3rd year PhD student working in the area of recommender systems. Previously he completed a MSc in Computer Science in Trinity College Dublin and a BEng in Software Engineering in the University of Wales. His research interest lay within the realm of social recommender systems and intelligent user interfaces which can make use of social data. He is currently being supervised by Professor Barry Smyth and based in CLARITY : Centre for Sensor Web Technologies, a research centre in University College Dublin.

This talk is part of the Computer Laboratory Systems Research Group Seminar series.

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