I’m feeling LoCo: A Location Based Context Aware Recommendation System
Research in ubiquitous location recommendation systems has focused on automatically inferring a user’s preferences while little attention has been devoted to the recommendation algorithms. Location recommendation systems with a focus on recommendation algorithms generally require the user to complete complicated and time consuming surveys and rarely consider the user’s current context. The purpose of this investigation is to design a more complete ubiquitous location based recommendation algorithm that by inferring user’s preferences and considering time geography and similarity measurements automatically, betters the user experience. Our system learns user preferences by mining a person’s social network profile. The physical constraints are delimited by a user’s location, and form of transportation, which is automatically detected through the use of a decision tree followed by a discrete Hidden Markov Model. We defined a decision-making model, which considers the learned preferences, physical constraints and how the individual is currently feeling. Our recommendation algorithm is based on a text classification problem. The detection of the form of transportation and the user interface was implemented on the Nokia N900 phone, the recommendation algorithm was implemented on a server which communicates with the phone. The novelty of our approach relies on the fusion of information inferred from a user’s social network profile and his/her mobile phone’s sensors for place discovery. Our system is named: I’m feeling LoCo.
KeywordsPersonalization Recommendation Systems Pervasive computing Human Computer Interaction Context Aware Recommendation Engines Automatic Travel Guides
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