CARS
Context-Aware Recommender Systems (CARS)
| Coordinatore | TELEFONICA INVESTIGACION Y DESARROLLO SA
Organization address
address: RONDA DE LA COMUNICACION S/N DISTRITO C EDIFICIO OESTE I contact info |
| Nazionalità Coordinatore | Spain [ES] |
| Totale costo | 166˙180 € |
| EC contributo | 166˙180 € |
| Programma | FP7-PEOPLE
Specific programme "People" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013) |
| Code Call | FP7-PEOPLE-2010-IEF |
| Funding Scheme | MC-IEF |
| Anno di inizio | 2011 |
| Periodo (anno-mese-giorno) | 2011-12-01 - 2013-11-30 |
Partecipanti
| # | ||||
|---|---|---|---|---|
| 1 |
TELEFONICA INVESTIGACION Y DESARROLLO SA
Organization address
address: RONDA DE LA COMUNICACION S/N DISTRITO C EDIFICIO OESTE I contact info |
ES (MADRID) | coordinator | 166˙180.80 |
Mappa
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Obiettivo del progetto (Objective)
'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 objectives: 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.'
Introduzione (Teaser)
Online retail and similar systems often make inappropriate recommendations. An EU project introduced systems that use contextual relationship algorithms to better determine a user's likely preferences.
Descrizione progetto (Article)
Systems used for online shopping commonly make suggestions to users about other products. Yet the recommendations are often not fully relevant, necessitating a system that utilises all available customer information to make more targeted suggestions.
The EU-funded 'Context-aware recommender systems (CARS)' (CARS) project aimed to build such a recommendation system for mobile and desktop devices. The system takes advantage of the full contextual relationship between user and item. The consortium's activities began at the end of 2011 and lasted two years.
Achievements included developing five novel algorithms for collaborative filtering, which outperform competitors. The algorithms were integrated into an application (app) for Android mobile devices, called Frappe, which recommends other apps the user might like. The app uses contextual information from the mobile phone sensors to find similar products that may meet the apparent needs of the user. The project evaluated the app's usefulness and impact.
Project results were published as conference and workshop papers, two of which received Best Paper awards.
The CARS project advanced the field of context-based recommendation systems. As a result, new systems will provide more relevant suggestions, potentially leading to further purchases.