41,280 research outputs found
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
Travel recommendations in a mobile tourist information system
An advanced mobile tourist information system delivers information about sights and events on a tourists travel route. The system should be personalized in its interaction with the tourist. Data that can be used for personalization are: the tourists interest profile, an analysis of their travel history, and the tourists feedback about sights. Existing mobile information systems for tourists do not tailor their information delivery to the tourists interests. In this paper, we propose the use of personalised recommendations that consider all of the personal information a tourist provides. We adopt and modify techniques from recommended systems to the new application area of mobile tourist information. We propose a number of methods for personalised recommendations; and select a subset of these for implementation. This paper then presents the implemented recommended component of our TIP system for mobile tourist informatio
Context-aware Sequential Recommendation
Since sequential information plays an important role in modeling user
behaviors, various sequential recommendation methods have been proposed.
Methods based on Markov assumption are widely-used, but independently combine
several most recent components. Recently, Recurrent Neural Networks (RNN) based
methods have been successfully applied in several sequential modeling tasks.
However, for real-world applications, these methods have difficulty in modeling
the contextual information, which has been proved to be very important for
behavior modeling. In this paper, we propose a novel model, named Context-Aware
Recurrent Neural Networks (CA-RNN). Instead of using the constant input matrix
and transition matrix in conventional RNN models, CA-RNN employs adaptive
context-specific input matrices and adaptive context-specific transition
matrices. The adaptive context-specific input matrices capture external
situations where user behaviors happen, such as time, location, weather and so
on. And the adaptive context-specific transition matrices capture how lengths
of time intervals between adjacent behaviors in historical sequences affect the
transition of global sequential features. Experimental results show that the
proposed CA-RNN model yields significant improvements over state-of-the-art
sequential recommendation methods and context-aware recommendation methods on
two public datasets, i.e., the Taobao dataset and the Movielens-1M dataset.Comment: IEEE International Conference on Data Mining (ICDM) 2016, to apea
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