NDLI: The application of visualization techniques in recommendation systems

Content Provider	IEEE Xplore Digital Library
Author	Qian, Dong Yang, Cheng Li, Chen
Copyright Year	2015
Description	Author affiliation: Science and Technology Department Communication, University of China, Beijing, China (Qian, Dong; Yang, Cheng; Li, Chen)
Abstract	The personalized recommendation system has been widely and maturely applied to various domains from social network to items recommendation such as videos, music, movies, books and online shopping. In the meantime, the information visualization technology based on big data has a substantial development. Considering the common based on big data, this paper discussed the connections of graph visualization and recommendation system from a new perspective. We propose three combinative points: Firstly, the Visual Analytics of social network can be combined with the recommendation computing; secondly, the graph drawing algorithms can be combined with the terminal presentation of recommended results. On the account of the evolutional nature of recommendation, the dynamic graphs would be the key area; thirdly, user interaction with the recommendation system can use the graph interaction strategies to achieve a more accurate and understandable result. In this paper, we discuss the visual techniques which can applied to recommendation system from a more general perspective. We also discuss some research challenges in the future.
Starting Page	656
Ending Page	660
File Size	529529
Page Count	5
File Format	PDF
e-ISBN	9781509000227
DOI	10.1109/BMEI.2015.7401585
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-10-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Filtering Recommendation Systems Heuristic algorithms Social network services Visual analytics Layout Dynamics Data visualization Graph Visualization Dynamic graph Interaction methods
Content Type	Text
Resource Type	Article

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