Now showing 1 - 10 of 22
  • Publication
    Experience-based critiquing : reusing critiquing experiences to improve conversational recommendation
    Product recommendation systems are now a key part of many e-commerce services and have proven to be a successful way to help users navigate complex product spaces. In this paper, we focus on critiquing-based recommenders, which permit users to tweak the features of recommended products in order to refine their needs and preferences. In this paper, we describe a novel approach to reusing past critiquing histories in order to improve overall recommendation efficiency.
      1006Scopus© Citations 42
  • Publication
    Towards a Novel and Timely Search and Discovery System Using the Real-Time Social Web
    The world of web search is changing. Mainstream search engines like Google and Bing are adding social signals to conventional query-based services while social networks like Twitter and Facebook are adding query-based search to sharing-based services. Our search and discovery system, Yokie, harnesses the wisdom of the crowd of communities of Twitter users to create indexes of proto-content (or recently shared content) that is typically not yet indexed by mainstream search engines. The system includes an architecture [13] for a range of contextual queries and ranking strategies beyond standard relevance. In this paper, we focus on evaluating Yokies ability to retrieve timely, relevant and exclusive results with which users interacted and found useful, compared to other standard web services.
  • Publication
    Towards an intelligent reviewer's assistant: recommending topics to help users to write better product reviews
    User opinions and reviews are an important part of the modern web and all major e-commerce sites typically provide their users with the ability to provide and access customer reviews across their product catalog. Indeed this has become a vital part of the service provided by sites like Amazon and TripAdvisor, so much so that many of us will routinely check appropriate product reviews before making a purchase decision, regardless of whether we intend to purchase online or not. The importance of reviews has highlighted the need to help users to produce better reviews and in this paper we describe the development and evaluation of a Reviewer's Assistant for this purpose. We describe a browser plugin that is designed to work with major sites like Amazon and to provide users with suggestions as they write their reviews. These suggestions take the form of topics (e.g. product features) that a reviewer may wish to write about and the suggestions automatically adapt as the user writes their review. We describe and evaluate a number of different algorithms to identify useful topics to recommend to the user and go on to describe the results of a preliminary live-user trial.
      483Scopus© Citations 14
  • Publication
    Terms of a feather : content-based news discovery and recommendation using Twitter
    User-generated content has dominated the web’s 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, Twitter’s 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 user’s 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.
      2196Scopus© Citations 80
  • Publication
    On using the real-time web for news recommendation & discovery
    In this work we propose that the high volumes of data on real-time networks like Twitter can be harnessed as a useful source of recommendation knowledge. We describe Buzzer, a news recommendation system that is capable of adapting to the conversations that are taking place on Twitter. Buzzer uses a content-based approach to ranking RSS news stories by mining trending terms from both the public Twitter timeline and from the timeline of tweets generated by a user’s own social graph (friends and followers). We also describe the result of a live-user trial which demonstrates how these ranking strategies can add value to conventional RSS ranking techniques, which are largely recency-based.
      1173Scopus© Citations 23
  • Publication
    Buzzer : online real-time topical news article and source recommender
    The significant growth of media and user-generated content online has allowed for the widespread adoption of recommender systems due to their proven ability to reduce the workload of a user and personalise content. In this paper, we describe our prototype system called Buzzer, which harnesses real-time micro-blogging activity, such as Twitter, as the basis for promoting personalised content, such as news articles, from RSS feeds. We also introduce several new features, that include a technique for recommending community articles from the pooled resources of all system users and also a mechanism for recommending source RSS feeds to which the user does not subscribe.
      3400Scopus© Citations 4
  • Publication
    Sentimental Product Recommendation
    This paper describes a novel approach to product recommendation that is based on opinionated product descriptions that are automatically mined from user-generated product reviews. We present a recommendation ranking strategy that combines similarity and sentiment to suggest products that are similar but superior to a query product according to the opinion of reviewers. We demonstrate the benefits of this approach across a variety of Amazon product domains.
      633Scopus© Citations 52
  • Publication
    SimpleFlow : enhancing gestural interaction with gesture prediction, abbreviation and autocompletion
    Gestural interfaces are now a familiar mode of user interaction and gestural input is an important part of the way that users can interact with such interfaces. However, entering gestures accurately and efficiently can be challenging. In this paper we present two styles of visual gesture autocompletion for 2D predictive gesture entry. Both styles enable users to abbreviate gestures. We experimentally evaluate and compare both styles of visual autocompletion against each other and against non-predictive gesture entry. The best perform- ing visual autocompletion is referred to as SimpleFlow. Our findings establish that users of SimpleFlow take significant advantage of gesture autocompletion by entering partial gestures rather than whole gestures. Compared to non- predictive gesture entry, users enter partial gestures that are 41% shorter than the complete gestures, while simultaneously improving the accuracy (+13%, from 68% to 81%) and speed (+10%) of their gesture input. The results provide insights into why SimpleFlow leads to significantly enhanced performance, while showing how predictive gestures with simple visual autocompletion impacts upon the gesture abbreviation, accuracy, speed and cognitive load of 2D predictive gesture entry.
      633Scopus© Citations 17
  • Publication
    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.
      1820Scopus© Citations 42
  • Publication
    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.
      724Scopus© Citations 42