基于事件本体的新闻个性化推荐
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摘要
针对传统推荐系统中存在的冷启动、数据稀疏、语义缺乏、推荐精度较低等问题,提出一种基于事件本体的推荐算法。结合新闻的分类结构和新闻语料构建事件本体,对用户浏览的新闻进行要素抽取并构建用户兴趣模型。基于事件本体的分类结构计算新闻事件之间的相似度,通过用户兴趣模型计算用户兴趣相似度,根据事件本体非层次结构的语义半径寻找相关新闻事件。综合事件本体相似度、用户兴趣相似度和非层次结构相似度3个方面得出新闻个性化推荐结果。实验结果表明,该算法的推荐结果优于协同过滤推荐算法和基于内容的推荐算法。
Aiming at the problems existing in the traditional recommendation system,such as cold start,sparse data,lack of semantics and relatively low recommendation accuracy,a recommendation algorithm based on event ontology is proposed.By combining the news classification structure and news corpus to build event ontology,the elements of news browsed by users are extracted and user's interest model is constructed.The similarity between the news events is calculated based on the event ontology structure,the user interest similarity is calculated through user's interest model,and relevant news events are found according to the semantic radius of the non-hierarchical structure of event ontology.Synthesize the news ontology similarity,the user's interest similarity,and the non-hierarchical structure similarity to realize a comprehensive news personalized recommendation.Experimental results show that the proposed algorithm has better recommendation than the collaborative filtering recommendation algorithm and the recommendation algorithm based on content.
Aiming at the problems existing in the traditional recommendation system,such as cold start,sparse data,lack of semantics and relatively low recommendation accuracy,a recommendation algorithm based on event ontology is proposed.By combining the news classification structure and news corpus to build event ontology,the elements of news browsed by users are extracted and user's interest model is constructed.The similarity between the news events is calculated based on the event ontology structure,the user interest similarity is calculated through user's interest model,and relevant news events are found according to the semantic radius of the non-hierarchical structure of event ontology.Synthesize the news ontology similarity,the user's interest similarity,and the non-hierarchical structure similarity to realize a comprehensive news personalized recommendation.Experimental results show that the proposed algorithm has better recommendation than the collaborative filtering recommendation algorithm and the recommendation algorithm based on content.
引文
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[15] 仲兆满,刘宗田,李存华.事件本体模型及事件类排序[J].北京大学学报(自然科学版),2013,49(2):234-240.
[16] 刘炜,丁宁,杨竣辉,等.针对地点污染突发事件领域的事件本体模式[J].计算机科学与探索,2016,10(4):466-480.
[17] 刘炜,王旭,张雨嘉,等.一种面向突发事件的文本语料自动标注方法[J].中文信息学报,2017,31(2):76-85.
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[20] PAZZANI J M,BILLSUS D.Content-based recom-mendation systems[M].Berlin,Germany:Springer,2007:325-341.
[2] LI Lei,ZHENG Li,YANG Fan,et al.Modeling and broadening temporal user interest in personalized news recommendation[J].Expert Systems with Applications,2014,41(7):3168-3177.
[3] GARCIN F,ZHOU K,FALTINGS B,et al.Personalized news recommendation based on collaborative filtering[C]//Proceedings of 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology.Washington D.C.,USA:IEEE Computer Society,2012:437-441.
[4] TANG Xiangyu,ZHOU Jie.Dynamic personalized recommendation on sparse data[J].IEEE Transactions on Knowledge and Data Engineering,2013,25(12):2895-2899.
[5] ANIDORIFON L,SANTOSGAGO J,CAEIRORODRIGUEZ M,et al.Recommender systems[J].Communications of the ACM,2015,40(3):56-58.
[6] MUSTO C,SEMERARO G,GEMMIS M D,et al.Learning word embeddings from wikipedia for content-based recommender systems[C]//Proceedings of European Conference on Information Retrieval.Berlin,Germany:Springer,2016:729-734.
[7] KOMPAN M,BIELIKOVA M.Content-based news recommendation[C]//Proceedings of E-commerce and Web Technologies,International Conference.Berlin,Germany:Springer,2010:61-72.
[8] LI Lihong,CHU Wei,LANGFORD J,et al.A contextual-bandit approach to personalized news article recommenda-tion[C]//Proceedings of the 19th International Conference on World Wide Web.New York,USA:ACM Press,2010:661-670.
[9] 陈一峰,赵恒凯,余小清,等.基于本体的用户兴趣模型构建研究[J].计算机工程,2010,36(21):46-48.
[10] FRIKHA M,MHIRI M,GARGOURI F.A user interest ontology based on trusted friends preferences for personalized recommendation[C]//Proceedings of European Mediterranean and Middle Eastern Conference.Berlin,Germany:Springer,2017:54-68.
[11] 吴正洋,汤庸,方家,等.一种基于本体语义相似度的协同过滤推荐方法[J].计算机科学.2015,42(9):204-207.
[12] GIRI G L,DEEPAK G,MANJULA S H,et al.OntoYield:a semantic approach for context-based ontology recom-mendation based on structure preservation[C]//Proceedings of International Conference on Computational Intelligence and Data Engineering.Berlin,Germany:Springer,2018:256-277.
[13] 刘宗田,黄美丽,周文,等.面向事件的本体探究[J].计算机科学,2009,36(11):189-192.
[14] STUDER R,BENJAMINS V R,FENSEL D.Knowledge engineering:principles and methods[J].Data and Knowledge Engineering,1998,25(1):167-197.
[15] 仲兆满,刘宗田,李存华.事件本体模型及事件类排序[J].北京大学学报(自然科学版),2013,49(2):234-240.
[16] 刘炜,丁宁,杨竣辉,等.针对地点污染突发事件领域的事件本体模式[J].计算机科学与探索,2016,10(4):466-480.
[17] 刘炜,王旭,张雨嘉,等.一种面向突发事件的文本语料自动标注方法[J].中文信息学报,2017,31(2):76-85.
[18] KNIJNENBURG B P,KOBSA A.Making decisions about privacy:information disclosure in context-aware recommender systems[J].ACM Transactions on Interactive Intelligent Systems,2013,3(3):1-23.
[19] BERRSE J S,HECKERMAN D,KADIE C,et al.Empirical analysis of predictive algorithms for collaborative filtering[J].Uncertainty in Artificial Intelligence,2013,98(7):43-52.
[20] PAZZANI J M,BILLSUS D.Content-based recom-mendation systems[M].Berlin,Germany:Springer,2007:325-341.