电子学报 ›› 2020, Vol. 48 ›› Issue (9): 1735-1740.DOI: 10.3969/j.issn.0372-2112.2020.09.010

• 学术论文 • 上一篇    下一篇

直播电视推荐系统的评分预测算法研究

郭景峰1,2,3, 朱晓松1,2,3, 李爽4,5   

  1. 1. 燕山大学信息科学与工程学院, 河北秦皇岛 066004;
    2. 河北省计算机虚拟技术与系统集成重点实验室, 河北秦皇岛 066004;
    3. 河北省文化旅游大数据技术创新中心, 河北承德 067000;
    4. 天津大学建筑学院, 天津 300072;
    5. 河北环境工程学院生态系, 河北秦皇岛 066102
  • 收稿日期:2019-11-21 修回日期:2020-05-09 出版日期:2020-09-25 发布日期:2020-09-25
  • 通讯作者: 朱晓松
  • 作者简介:郭景峰 男,1962年2月出生于黑龙江哈尔滨.现为燕山大学信息科学与工程学院教授、博士生导师.在国内外发表学术论文130余篇.E-mail:jfguo@ysu.edu.cn
    李爽 女,1982年7月出生于河北唐山.现为河北环境工程学院副教授,天津大学建筑学院博士生,主要研究方向为数字景观、机器学习.E-mail:89732212@qq.com
  • 基金资助:
    国家自然科学基金(No.61472340);河北省重点研发计划项目(No.20310301D)

Research on Rating Prediction Algorithm of Live TV Recommender Systems

GUO Jing-feng1,2,3, ZHU Xiao-song1,2,3, LI Shuang4,5   

  1. 1. College of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China;
    2. The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Qinhuangdao, Hebei 066004, China;
    3. The Technology Innovation Center of Cultural Tourism Big Data of Hebei Province, Chengde, Hebei 067000, China;
    4. School of Architecture, Tianjin University, Tianjin 300072, China;
    5. Faculty of Ecology, Environmental Management College of China, Qinhuangdao, Hebei 066102, China
  • Received:2019-11-21 Revised:2020-05-09 Online:2020-09-25 Published:2020-09-25

摘要: 伴随电视频道的不断增加,推荐系统在直播电视领域应用成为研究热点.然而,直播电视独特的播放和收视方式使得传统的VOD(Video On Demand)推荐系统无法直接应用,已有的推荐频道的方法不关注正在播出的节目状态从而影响了推荐准确率,而推荐节目的方法难以应对节目冷启动.为此,本文提出了一种融合频道推荐和节目推荐的评分预测算法OFAP(Over the First by Adding Preference).首先,利用聚类方法对每个用户实现差异性的收视时段划分,构建他们的频道-时段偏好矩阵和预推荐评分权重矩阵;其次,提出一个评分替代策略使得已有的推荐节目的算法能够应对节目冷启动,从而实现预推荐;最后,通过融合用户偏好、预推荐评分权重与预推荐结果,构建评分预测函数,将预推荐算法的评分预测结果作为评分预测函数的训练样本.实验表明,采用Precision@N和Recall@N作为评价标准,本文所提方法OFAP明显优于对比算法.

关键词: 直播电视, 推荐系统, 冷启动, 时间相关, 预推荐, 协同过滤, 电视频道

Abstract: With the increase of TV channels,the application of recommender systems in the field of live TV has become a research hotspot.However,a traditional VOD (Video on Demand) recommender system is unable to be directly applied in live TV because of its special way of broadcasting and watching,and the existing methods of recommending channels do not pay attention to status of TV shows being broadcasted,which affects recommendation accuracy,and the methods of recommending programs are difficult to handle cold start of TV shows.Therefore,this paper proposes a rating prediction algorithm by fusing TV channel recommendation method and TV program recommendation method OFAP (Over the First by Adding Preference).Firstly,we construct different channel-time preference matrix and rating weight matrix of pre-recommendation for each user by clustering their viewing logs.Secondly,we propose a rating strategy to alleviate the cold-start problem of TV programs for existing program recommendation algorithms,and we adopt one of them to perform pre-recommendation.Finally,we combine user's preference,rating weight and rating of pre-recommendation to construct a prediction function,which is trained with the results of pre-recommendation.Experiments on industrial datasets show that the proposed model OFAP significantly outperforms baseline algorithms when Precision@N and Recall@N are adopted as criterias.

Key words: live TV, recommender system, cold start, time-based, pre-recommendation, collaborative filtering, TV channel

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