A Recommended Method Based on Semantics and User Preferences in Network Live Television

ZHANG Xiang; YUAN Zheng; JIANG Dan; ZHU Ming

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A Recommended Method Based on Semantics and User Preferences in Network Live Television

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- A Recommended Method Based on Semantics and User Preferences in Network Live Television
- Vol. 33, Issue 12, Pages: 52-56(2016)
- 作者机构：
  
  1. 中国科学技术大学信息科学技术学院,安徽,合肥,230022
  2. 上海文广互动电视有限公司,上海,200072
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2016
- 稿件说明：
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张翔, 袁政, 蒋旦, 朱明. 基于语义和用户偏好的网络电视直播推荐方法[J]. 微电子学与计算机, 2016, 33(12): 52-56.

ZHANG Xiang, YUAN Zheng, JIANG Dan, et al. A Recommended Method Based on Semantics and User Preferences in Network Live Television[J]. 2016, 33(12): 52-56.
张翔, 袁政, 蒋旦, 朱明. 基于语义和用户偏好的网络电视直播推荐方法[J]. 微电子学与计算机, 2016, 33(12): 52-56. DOI：

ZHANG Xiang, YUAN Zheng, JIANG Dan, et al. A Recommended Method Based on Semantics and User Preferences in Network Live Television[J]. 2016, 33(12): 52-56. DOI：

摘要

提出一种基于语义和用户偏好的网络电视直播实时推荐方法.该方法首先基于用户的历史记录构建用户偏好模型

然后使用基于词向量的语义相似度计算方法

分别计算待推荐节目和用户记录或待推荐节目和用户当前观看节目间的相似度

再结合该相似度和用户偏好求取用户对待推荐节目的虚拟兴趣

最后选出虚拟兴趣较高的一组节目作为对用户的实时推荐.实验结果表明

此方法的命中率在实时预测推荐的场景下较对比方法提高了10%以上

且在实时节目推荐的场景下有更好的推荐效果.

Abstract

This paper proposes a real-time recommendation method based on semantics and user preference in Live TV. Firstly

it constructs a user preference model based on users watching history

and calculates the similarity between the to-be-recommended programs with users watching history or users watching programs respectively with the Semantic similarity calculation method based on word vector. Combined with user preference

the similarity is used to work out users virtual interest about the to-be-recommended programs. Finally

its the program which gets higher virtual interest that becomes users real-time recommendation result. Experimental results show that the recall rate of this method is improved by about ten percent than the existing methods when its used on the real-time predicted recommendation. And the quality of recommendation is improved significantly on real-time program recommendation.

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references

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Mikolov T, Chen K, Corrado G, et al. Efficient estimation of word representations in vector space[J]. Computer Science, 2013.

Bengio Y, Ducharme R, Vincent P, et al. A neural probabilistic language model[J]. The Journal of Machine Learning Research, 2003, 3(6): 1137-1155. DOI: 1137

田久乐, 赵蔚. 基于同义词词林的词语相似度计算方法[J]. 吉林大学学报(信息科学版), 2010, 28(6): 602-608. DOI: 602

刘群, 李素建. 基于《知网》的词汇语义相似度计算[J]. 中文计算语言学, 2002, 7(2): 59-76. DOI: 59

许海玲, 吴潇, 李晓东, 等. 互联网推荐系统比较研究[J]. 软件学报, 2009, 20(2): 350-362. DOI: 350

朱郁筱, 吕琳媛. 推荐系统评价指标综述[J]. 电子科技大学学报, 2012, 41(2): 163-175. DOI: 163

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