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380 results on '"Hong Wenxing"'

2. Financial FAQ Question-Answering System Based on Question Semantic Similarity

Author

Hong, Wenxing, Li, Jun, Li, Shuyan, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Cao, Cungeng, editor, Chen, Huajun, editor, Zhao, Liang, editor, Arshad, Junaid, editor, Asyhari, Taufiq, editor, and Wang, Yonghao, editor

Published
2024
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11. Capacity Estimation of Time-Triggered Ethernet Network Based on Complex Network Theory

Author

Wang, Qing, Teng, Liping, Hong, Wenxing, Wu, Huimin, Li, Beichen, Liu, Guihua, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Tang, Yong, editor, Zu, Qiaohong, editor, and Rodríguez García, José G., editor

Published
2019
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20. Taxonomy-Oriented Recommendation towards Recommendation with Stage

Author

Li, Lei, Hong, Wenxing, Li, Tao, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Sheng, Quan Z., editor, Wang, Guoren, editor, Jensen, Christian S., editor, and Xu, Guandong, editor

Published
2012
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30. CPIN:Comprehensive present-interest network for CTR prediction

Author

Hong, Wenxing, Xiong, Ziang, You, Jinjie, Wu, Xiaolin, Xia, Min, Hong, Wenxing, Xiong, Ziang, You, Jinjie, Wu, Xiaolin, and Xia, Min

Abstract

Personalized recommendation is a popular research direction in both industry and academia. Some research on recommender systems utilizes the users’ interaction history on items to represent the users’ interests, which has achieved remarkable success. Users’ interests in the real world are dynamically changing and have a strong correlation with the interaction sequence. However, sometimes users’ interests are less relevant to the order of the current interaction sequence, but are more relevant to certain items in the user interaction history. In this paper, a novel deep neural network model is proposed to deal with this situation. The developed model consists of two parts: the present interest relevant to the order of the interaction sequence and the comprehensive interest relevant to some items in the interaction sequence. An ancillary multi-layer perceptron (MLP) is constructed to improve the training of our model. Experiments on public and industrial datasets are conducted. The experimental results show that our proposed model outperforms the state-of-the-art models which demonstrates the effectiveness of the ancillary MLP.

Published
2021

47. iHR+: A mobile reciprocal job recommender system

Author

Hui Yin, Hong Wenxing, Qi Jianwei, and Chen Yiwei

Subjects
World Wide Web, Seekers, ComputingMilieux_THECOMPUTINGPROFESSION, Reciprocity (social psychology), Computer science, Reciprocity (network science), Recommender system, Reciprocal
Abstract

The concept of reciprocal recommender is introduced to extend the traditional recommendation algorithm in a people-to-people recommender system where people are both the subjects and objects of the recommendation. In this paper, we designed and implemented a mobile reciprocal job recommender system, named iHR+. We first divided both the features of job seekers and jobs into two categories, one for self-description of a job seeker or a job, and the other presents the preferences of theirs. The bi-directional preference based on cross-similarity was then calculated to get a reciprocal recommendation.

Published
2015
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