Now showing 1 - 4 of 4
  • 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.
      575Scopus© Citations 15
  • 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.
      1099Scopus© Citations 23
  • 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.
      2091Scopus© Citations 76
  • Publication
    Recommending twitter users to follow using content and collaborative filtering approaches
    Recently the world of the web has become more social and more real-time. Facebook and Twitter are perhaps the exemplars of a new generation of social, real-time web services and we believe these types of service provide a fertile ground for recommender systems research. In this paper we focus on one of the key features of the social web, namely the creation of relationships between users. Like recent research, we view this as an important recommendation problem for a given user, UT which other users might be recommended as followers/followees but unlike other researchers we attempt to harness the real-time web as the basis for profiling and recommendation. To this end we evaluate a range of different profiling and recommendation strategies, based on a large dataset of Twitter users and their tweets, to demonstrate the potential for effective and efficient followee recommendation.
      9829Scopus© Citations 391