Kevin McCarthy

Using twitter to recommend real-time topical news

Recommending news stories to users, based on their preferences, has long been a favourite domain for recommender systems research. In this paper, we describe a novel approach to news recommendation that harnesses real-time micro-blogging activity, from a service such as Twitter, as the basis for promoting news stories from a users favourite RSS feeds. A preliminary evaluation is carried out on an implementation of this technique that shows promising results.

Incremental critiquing

Conversational recommender systems guide users through a product space, alternatively making concrete product suggestions and eliciting the users feedback. Critiquing is a common form of user feedback, where users provide limited feedback at the feature-level by constraining a features value-space. For example, a user may request a cheaper product, thus critiquing the price feature. Usually, when critiquing is used in conversational recommender systems, there is little or no attempt to monitor successive critiques within a given recommendation session. In our experience this can lead to inefficiencies on the part of the recommender system, and confusion on the part of the user. In this paper we describe an approach to critiquing that attempts to consider a users critiquing history, as well as their current critique, when making new recommendations. We provide experimental evidence to show that this has the potential to significantly improve recommendation efficiency.

Group recommender systems: a critiquing based approach

Group recommender systems introduce a whole set of new challenges for recommender systems research. The notion of generating a set of recommendations that will satisfy a group of users, with potentially competing interests, is challenging in itself. In addition to this we must consider how to record and combine the preferences of many different users as they engage in simultaneous recommendation dialogs. In this paper we introduce a group recommender system that is designed to provide assistance to a group of friends trying to plan a skiing vacation.

Experiments in dynamic critiquing

Conversational recommender systems are commonly used to help users to navigate through complex product-spaces by alternatively making product suggestions and soliciting user feedback in order to guide subsequent suggestions. Recently, there has been a surge of interest in developing effective interfaces that support user interaction in domains of limited user expertise. Critiquing has proven to be a popular and successful user feedback mechanism in this regard, but is typically limited to the modification of single features. We review a novel approach to critiquing, dynamic critiquing, that allows users to modify multiple features simultaneously by choosing from a range of so-called compound critiques that are automatically proposed based on their current position within the product-space. In addition, we introduce the results of an important new live-user study that evaluates the practical benefits of dynamic critiquing.

Terms of a feather: content-based news recommendation and discovery using twitter

User-generated content has dominated the webs recent growth and today the so-called real-time web provides us with unprecedented access to the real-time opinions, views, and ratings of millions of users. For example, Twitters 200m+ users are generating in the region of 1000+ tweets per second. In this work, we propose that this data can be harnessed as a useful source of recommendation knowledge. We describe a social news service called Buzzer that is capable of adapting to the conversations that are taking place on Twitter to ranking personal RSS subscriptions. This is achieved by a content-based approach of mining trending terms from both the public Twitter timeline and from the timeline of tweets published by a users own Twitter friend subscriptions. We also present results of a live-user evaluation which demonstrates how these ranking strategies can add better item filtering and discovery value to conventional recency-based RSS ranking techniques.

On the Dynamic Generation of Compound Critiques in Conversational Recommender Systems

Conversational recommender systems help to guide users through a product-space towards a particular product that meets their specific requirements. During the course of a “conversation” with the user the recommender system will suggest certain products and use feedback from the user to refine future suggestions. Critiquing has proven to be a powerful and popular form of feedback. Critiques allow the user to express a preference over part of the feature-space; for example, in a vacation/travel recommender a user might indicate that they are looking for a “less expensive” vacation than the one suggested, thereby critiquing the price feature. Usually the set of critiques that the user can chose from is fixed as part of the basic recommender interface. In this paper we will propose a more dynamic critiquing approach where high-quality critiques are automatically generated during each recommendation cycle from the remaining product-cases. We show that these dynamic critiques can lead to more efficient recommendation performance by helping the user to more rapidly focus in on the right region of the product-space.

The needs of the many: a case-based group recommender system

While much of the research in the area of recommender systems has focused on making recommendations to the individual, many recommendation scenarios involve groups of inter-related users. In this paper we consider the challenges presented by the latter scenario. We introduce a (case-based) group recommender designed to meet these challenges through a variety of recommendation features, including the generation of reactive and proactive suggestions based on user feedback in the form of critiques, and demonstrate its effectiveness through a live-user case-study.

Finding Useful Users on Twitter: Twittomender the Followee Recommender

This paper examines an application for finding pertinent friends followees on Twitter. Whilst Twitter provides a great basis for receiving information, we believe a potential downfall lies in the lack of an effective way in which users of Twitter can find other Twitter users to follow. We apply several recommendation techniques to build a followee recommender for Twitter. We evaluate a variety of different recommendation strategies, using real-user data, to demonstrate the potential for this recommender system to correctly identify and promote interesting users who are worth following.

Personalized and automatic social summarization of events in video

Social services like Twitter are increasingly used to provide a conversational backdrop to real-world events in real-time. Sporting events are a good example of this and this year, millions of users tweeted their comments as they watched the World Cup matches from around the world. In this paper, we look at using these time-stamped opinions as the basis for generating video highlights for these soccer matches. We introduce the PASSEV system and describe and evaluate two basic summarization approaches.

Opinionated Product Recommendation

In this paper we describe a novel approach to case-based product recommendation. It is novel because it does not leverage the usual static, feature-based, purely similarity-driven approaches of traditional case-based recommenders. Instead we harness experiential cases, which are automatically mined from user generated reviews, and we use these as the basis for a form of recommendation that emphasises similarity and sentiment. We test our approach in a realistic product recommendation setting by using live-product data and user reviews.