"Recommender Systems have become essential personalized navigational tools for users
to wade through the plethora of online content as they allow users to
discover relevant information that they would have never known it
existed. In recent years, the importance of this information discovery
process as opposed to explicit (keyword-based) search has been emphasized.
Current research in Recommender Systems, while taking into account the
relation between user and item, often ignores the ``context'' of the
recommendation. We define as ``context'' any environmental, temporal
or otherwise variable that influences a decision a user might make.
Early work on Context-Aware Recommender Systems (CARS) has found that
contextual factors do influence the recommendation needs of users.
However, the role that each of the contextual variables (e.g. time,
location, activity, emotional state, social network, etc.) plays on
the user's needs is still not clearly defined.
The main aim of this proposal is to build a compact Context-Aware
Recommender System (CARS) for mobile and desktop computing devices.
The research methodology of this proposal is structured in 3 research
1) Understanding contextual information in Recommender Systems
Where data will be mined in order to uncover underlying
patterns in the influence of context on users' preferences.
2) Building Context-aware Recommendation models
Which involves using state of the art Machine Learning to build
models and algorithms for CARS
3) Building a prototype and deployment
Which involves building and deploying a prototype based on the
developed algorithms and conducting a user study
Modern Machine Learning algorithms have been shown to perform well in
Recommendation Tasks and this proposal has a strong algorithmic and
methods focus but also aims at knowledge discovery both through Data
Mining and Human Computer Interaction techniques."
Fields of science
Call for proposal
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