ITM Web Conf.
Volume 12, 2017The 4th Annual International Conference on Information Technology and Applications (ITA 2017)
|Number of page(s)
|Session 4: Information Theory and Information Systems
|05 September 2017
Collaborating Filtering Community Image Recommendation System Based on Scene
Academy of Computer Science and Technology,Beihang university, Beijing, China
With the advancement of smart city, the development of intelligent mobile terminal and wireless network, the traditional text information service no longer meet the needs of the community residents, community image service appeared as a new media service. “There are pictures of the truth” has become a community residents to understand and master the new dynamic community, image information service has become a new information service. However, there are two major problems in image information service. Firstly, the underlying eigenvalues extracted by current image feature extraction techniques are difficult for users to understand, and there is a semantic gap between the image content itself and the user’s understanding; secondly, in community life of the image data increasing quickly, it is difficult to find their own interested image data. Aiming at the two problems, this paper proposes a unified image semantic scene model to express the image content. On this basis, a collaborative filtering recommendation model of fusion scene semantics is proposed. In the recommendation model, a comprehensiveness and accuracy user interest model is proposed to improve the recommendation quality. The results of the present study have achieved good results in the pilot cities of Wenzhou and Yan'an, and it is applied normally.
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.