An Ontology-based Context-aware Recommender System for Campus Scenic Spots
Date Issued
2009
Date
2009
Author(s)
Chen, Hsiao-Wei
Abstract
In this paper, we propose a context-aware campus spots recommender system which detects the changes in the environment, and provides a list of spots to user whose preference cannot be retrieved at the trip beginning, on the basis of user’s responses of what had visited in the trip, what time it is, whether it rains, who company with the visitor, and attractions information. The traditional recommender systems overlooked that a decison making differs in different context (location, time, or weather). In order to get high quality recommendations, in the general recommender system user has to rate a sufficient number of items; However, this system learns user’s preferences during the trip and recommends spots that user are interested in, even if this is his first time contact with the system. The system uses three main technologies: knowledge conceptualization, contextawareness ability, dynamic recommneder algorithm. Ontology is used to represent a set of concepts within the tourist domain. A spatial ontology organize spots information, and conceptualize geographic knowledge of NTU campus, such as Fu Bell is a spot, Royal Palm Blvd. is a road, and Fu Bell is on Royal Palm Blvd. is a geographic knowledge. The temporal concepts are built in ontology which could infer high-level information with the raw data. For example, 9:00 AMis a time stamp, by inference the system obtain that 9:00 AM is in the Morning and it belongs to eating time. Contextawareness ability copes with the changes in the enviroment. In short, the visitors could experience recommendations depending on their personal data and the environment conditions. With up-to-date user’s responses in the trip, the system dynamicly provides recommendations which vary with different time and weather condtion.
Subjects
Context-aware computing
recommender systems
multidimensional recommender systems
collaborative filtering
rating estimation
ontology
user modeling agent
location profiles
Type
thesis
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