Demo: Image Recommendation with User Intent on a Mobile
Authors: 
Xiaoming Dai, Qing Wang, Tianzhang Xing, Feng Chen, Xiaojiang Chen, Dingyi FangAuthors Info & Claims
Abstract
The mobile devices photographing has greatly enriched people’s interest, social and entertainment. The improvement of mobile devices processing chip and storage make the quality and quantity of pictures in mobile increased rapidly. Although mobile images can be simply categorized in existing work, there is no recommendation[5] list based on user’s mind. This situation will bring great burden and poor experience to users when selecting image. In this paper, we proposed an plug-in system, named IRI(IRI, Image Recommendation with User Intent), to create a recommendation list which follows user’s mind. In IRI, the user intent[1] can be sensed employing the text input. The multi-layer semantic relation library is used for assessing correlation between the image and sensed intent. We implement the IRI on mobile phone and test the accuracy of the intent sensing and energy consumption. The experimental results demonstrate the effectiveness and superiority of the IRI.
References
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R. Agrawal, A. Habeeb, and C. H. Hsueh. Learning user intent from action sequences on interactive systems. 2017.
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K. Bringmann, A. Gronlund, and K. G. Larsen. A dichotomy for regular expression membership testing. In 2017 IEEE 58th Annual Symposium on Foundations of Computer Science (FOCS), volume 00, pages 307– 318, Oct. 2017.
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F. Gao, D. Tao, X. Gao, and X. Li. Learning to rank for blind image quality assessment. IEEE Transactions on Neural Networks and Learning Systems, 26(10):2275–2290, 2015.
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A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam. Mobilenets: Efficient convolutional neural networks for mobile vision applications. CoRR, abs/1704.04861, 2017.
[5]
D. Sejal, V. Rashmi, K. R. Venugopal, S. S. Iyengar, and L. M. Patnaik. Image recommendation based on keyword relevance using absorbing markov chain and image features. International Journal of Multimedia Information Retrieval, 5(3):1–15, 2016.
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Y. Wang, H. Yin, and M. He. Improvement of textrank based on cooccurrence word pairs and context information. In M. Qiu, editor, Smart Computing and Communication, pages 226–235, Cham, 2018. Springer International Publishing.
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Information
Published In
February 2019
436 pages
ISBN:9780994988638
- General Chairs:
- Yunhao Liu,
- Guoliang Xing,
- Program Chairs:
- Yuan He,
- Gian Pietro Picco
Sponsors
- EWSN: International Conference on Embedded Wireless Systems and Networks
In-Cooperation
Publisher
Junction Publishing
United States
Publication History
Published: 15 March 2019
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Overall Acceptance Rate 81 of 195 submissions, 42%
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