Augmented context-based recommendation service framework using knowledge over the Linked Open Data cloud |
| |
Affiliation: | 1. Department of Information Management, National Taiwan University of Science and Technology, Taipei, Taiwan;2. Institute for Information Industry, Taipei, Taiwan;3. Institute of Statistical, Academia Sinica, Taipei, Taiwan |
| |
Abstract: | This research proposes ACARDS (Augmented-Context bAsed RecommenDation Service) framework that is able to utilize knowledge over the Linked Open Data (LOD) cloud to recommend context-based services to users. To improve the level of user satisfaction with the result of the recommendation, the ACARDS framework implements a novel recommendation algorithm that can utilize the knowledge over the LOD cloud. In addition, the noble algorithm is able to use new concepts like the enriched tags and the augmented tags that originate from the hashtags on the SNSs materials. These tags are utilized to recommend the most appropriate services in the user’s context, which can change dynamically. Last but not least, the ACARDS framework implements the context-based reshaping algorithm on the augmented tag cloud. In the reshaping process, the ACARDS framework can recommend the highly receptive services in the users’ context and their preferences. To evaluate the performance of the ACARDS framework, we conduct four kinds of experiments using the Instagram materials and the LOD cloud. As a result, we proved that the ACARDS framework contributes to increasing the query efficiency by reducing the search space and improving the user satisfaction on the recommended services. |
| |
Keywords: | Context-aware recommendation algorithm LOD cloud Service composition Ontology Tag cloud |
本文献已被 ScienceDirect 等数据库收录! |
|