74,277 research outputs found
Early evaluation of Unistats: user experiences
This paper sets out the findings of the user evaluation of Unistats.UK Higher Education Funding Bodie
Factors Influencing the Quality of the User Experience in Ubiquitous Recommender Systems
The use of mobile devices and the rapid growth of the internet and networking
infrastructure has brought the necessity of using Ubiquitous recommender
systems. However in mobile devices there are different factors that need to be
considered in order to get more useful recommendations and increase the quality
of the user experience. This paper gives an overview of the factors related to
the quality and proposes a new hybrid recommendation model.Comment: The final publication is available at www.springerlink.com
Distributed, Ambient, and Pervasive Interactions Lecture Notes in Computer
Science Volume 8530, 2014, pp 369-37
CHORUS Deliverable 4.4: Report of the 2nd CHORUS Conference
The Second CHORUS Conference and third Yahoo! Research Workshop on the Future of Web Search was held during April 4-5, 2008, in Granvalira, Andorra to discuss future directions in multi-medial information access and other specialised topics in the near future of retrieval. Attendance was at capacity, with 97 participants from 11 countries and 3 continents
A personalized and context-aware news offer for mobile devices
For classical domains, such as movies, recommender systems have proven their usefulness. But recommending news is more challenging due to the short life span of news content and the demand for up-to-date recommendations. This paper presents a news recommendation service with a content-based algorithm that uses features of a search engine for content processing and indexing, and a collaborative filtering algorithm for serendipity. The extension towards a context-aware algorithm is made to assess the information value of context in a mobile environment through a user study. Analyzing interaction behavior and feedback of users on three recommendation approaches shows that interaction with the content is crucial input for user modeling. Context-aware recommendations using time and device type as context data outperform traditional recommendations with an accuracy gain dependent on the contextual situation. These findings demonstrate that the user experience of news services can be improved by a personalized context-aware news offer
Second Screen User Profiling and Multi-level Smart Recommendations in the context of Social TVs
In the context of Social TV, the increasing popularity of first and second
screen users, interacting and posting content online, illustrates new business
opportunities and related technical challenges, in order to enrich user
experience on such environments. SAM (Socializing Around Media) project uses
Social Media-connected infrastructure to deal with the aforementioned
challenges, providing intelligent user context management models and mechanisms
capturing social patterns, to apply collaborative filtering techniques and
personalized recommendations towards this direction. This paper presents the
Context Management mechanism of SAM, running in a Social TV environment to
provide smart recommendations for first and second screen content. Work
presented is evaluated using real movie rating dataset found online, to
validate the SAM's approach in terms of effectiveness as well as efficiency.Comment: In: Wu TT., Gennari R., Huang YM., Xie H., Cao Y. (eds) Emerging
Technologies for Education. SETE 201
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