A Daily-Life Activity Assistant–Providing a Dynamic Recommender Service based on Multi-dimensional Filtering
Date Issued
2007
Date
2007
Author(s)
Wu, Yueh-Hsun
DOI
zh-TW
Abstract
Initial recommender system was used to solve the information overload problem. However, the traditional recommender system only uses the two dimensions "User" and "Content", and not considers the importance of contextual information. With the increasing complexity of recommendation contents, the impact on decision of user is also on the rise. Therefore, considering contextual information in recommender system has its existing necessities.
We propose an activity recommendation service that includes multi-dimensional contextual information. It forms a multi-dimension architecture by user profiles that considers contextual information, uses flexible concept hierarchy to improve the multi-dimensional similarity computation, and applies the above two solution into collaborative filtering algorithm to make a more personalization activity recommendation. Besides, we adopt a service-oriented architecture (SOA) to build our system in order to provide a portable service. Then, every user and developer can access the service easily and use the service to develop applications in any platform. Also, in our system experiment analysis, we run rationality verification and observe the recommender system phenomenon for the training data that system collected or generated and the testing data that getting from users.
Respecting the phenomenon of aging population, the need of homecare is gradually increasing. We can utilize this activity recommender system to help the family burden scheduling their daily-life activity better. Therefore, this research can be a potential application in homecare domain in the future.
Subjects
推薦系統
多維度
協同過濾
概念階層
Recommender System
Multi-dimension
Contextual Information
Collaborative Filtering
Concept Hierarchy
Type
other
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