DocumentCode :
3579708
Title :
Enhancing Context-Aware Recommendation via a Unified Graph Model
Author :
Hao Wu ; Xiaoxin Liu ; Yijian Pei ; Bo Li
Author_Institution :
Sch. of Inf. Sci. & Eng., Yunnan Univ., Kunming, China
fYear :
2014
Firstpage :
76
Lastpage :
79
Abstract :
In order to assist the development and use of context-aware recommendation capabilities, we propose a unified graph model which incorporates contextual information in an advantageous way. Basic RWR and Context RWR are specifically designed to estimate the relevance between the target user and the items against the contextual graph. Also, we propose two post-processing strategies to filter recommendation results given contextual conditions. Being dependent on experimental results on two datasets, all proposed methods are effective to enhance the quality of context-aware recommendations.
Keywords :
graph theory; recommender systems; ubiquitous computing; BasicRWR; ContextRWR; context-aware recommendation capabilities; context-aware recommendation enhancement; context-aware recommendations; contextual graph; filter recommendation; post-processing strategies; unified graph model; Collaboration; Context; Context modeling; Predictive models; Recommender systems; Semantics; context-aware recommendation; graph model; pervasive computing; post-filtering; random walks;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Identification, Information and Knowledge in the Internet of Things (IIKI), 2014 International Conference on
Type :
conf
DOI :
10.1109/IIKI.2014.23
Filename :
7064002
Link To Document :
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