ISSN 0253-2778

CN 34-1054/N

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Collaborative filtering recommendation algorithm based on semantic similarity

  • 1.

    Computer Practice Teaching Center, Jiangxi University of Finance and Economics,Nanchang 330013, China

  • 2.

    School of Humanities, Jiangxi University of Finance and Economic, Nanchang 330013, China

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2019.10.009
  • Received Date: 15 May 2019
  • Accepted Date: 28 September 2019
  • Rev Recd Date: 28 September 2019
  • Publish Date: 31 October 2019
    Abstract

  • Abstract

    To solve the problem that collaborative filtering recommendation algorithm does not consider the semantic relationship between recommendation objects，an improved collaborative filtering recommendation algorithm based on semantic similarity of recommendation objects is proposed. First,the semantic information of the recommended object is embedded into a low dimensional semantic space by using the knowledge map representation learning algorithm;then the semantic similarity between the recommended objects is calculated and integrated into the similarity calculation of collaborative filtering recommendation algorithm, thus compensating for the shortcoming that the collaborative filtering recommendation algorithm does not consider the semantic knowledge of the recommendation object. The experimental results show that the improved algorithm has higher accuracy, recall and coverage than the traditional collaborative filtering recommendation algorithm.

    Abstract

    To solve the problem that collaborative filtering recommendation algorithm does not consider the semantic relationship between recommendation objects，an improved collaborative filtering recommendation algorithm based on semantic similarity of recommendation objects is proposed. First,the semantic information of the recommended object is embedded into a low dimensional semantic space by using the knowledge map representation learning algorithm;then the semantic similarity between the recommended objects is calculated and integrated into the similarity calculation of collaborative filtering recommendation algorithm, thus compensating for the shortcoming that the collaborative filtering recommendation algorithm does not consider the semantic knowledge of the recommendation object. The experimental results show that the improved algorithm has higher accuracy, recall and coverage than the traditional collaborative filtering recommendation algorithm.
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    • References(19)
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    杨武,唐瑞,卢玲.基于内容的推荐与协同过滤融合的新闻推荐方法[J].计算机应用,2016,36(2):414-418.
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    郭云飞,方耀宁,扈红超.基于Logistic函数的社会化矩阵分解推荐算法[J].北京理工大学学报,2016, 36(1):70-74.
    GUO Yunfei, FANG Yaoning, HU Hongchao. A social matrix factorization recommender algorithm based on logistic function[J]. Transactions of Beijing Institute of Technology, 2016, 36(1):70-74.
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    张宜浩,朱小飞,徐传运,等.基于用户评论的深度情感分析和多视图协同融合的混合推荐方法[J].计算机学报,2019, 42(6): 1316-1333.
    ZHANG Yihao, ZHU Xiaofei, XU Chuanyun, et al. Hybrid recommendation approach based on deep sentiment analysis of user reviews and multi-view collaborative fusion[J]. Chinese Journal of Computers, 2019, 42(6):1316-1333.
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    ZHOU T Q, CHEN L N, SHEN J. Movie recommendation system employing the user-based CF in Cloud computing[C]// IEEE International Conference on Computational Science and Engineering. Guangzhou, China: IEEE, 2017:46-50.
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    ZHANG H Y, GANCHEV I, NIKOLOV N S, et al. A trust-enriched approach for item-based collaborative filtering recommendations[C]// IEEE, International Conference on Intelligent Computer Communication and Processing. Cluj-Napoca, Romania:IEEE, 2016:65-68.
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    肖文强,姚世军,吴善明.一种改进的top-N协同过滤推荐算法[J].计算机应用研究,2018,35(1):105-108,112.
    XIAO Wenqiang, YAO Shijun, WU Shanming. Improved top-N collaborative filtering recommendation algorithm[J].Application Research of Computers,2018,35(1):105-108,112.
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    LIU Qiao, LI Yang, DUAN Hong, et al. Knowledge graph construction techniques[J]. Journal of Computer Research and Development,2016,53(3):582-600.
    [15]
    MIKOLOV T, SUTSKEVER I, CHEN K, et al. Distributed representations of words and phrases and their compositionality[C]// International Conference on Neural Information Processing Systems. Daegu, Korea: Curran Associates Inc.2013:3111-3119.
    [16]
    FAN M, ZHOU Q, ZHENG T F, et al. Distributed representation learning for knowledge graphs with entity descriptions[J]. Pattern Recognition Letters, 2017,93(1):31-37.
    [17]
    BENGIO Y, COURVILLE A, VINCENT P. Representation learning: A review and new perspectives[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2013, 35(8):1798-1828.
    [18]
    刘知远,孙茂松,林衍凯,等.知识表示学习研究进展[J].计算机研究与发展,2016, 53(2):247-261.
    LIU Zhiyuan, SUN Maosong, LIN Yankai, et al. Knowledge representation learning: A review[J]. Journal of Computer Research and Development,2016, 53(2):247-261.
    [19]
    BORDES A , USUNIER N , GARCIA-DURAN A , et al. Translating embeddings for modeling multi-relational data[C]// International Conference on Neural Information Processing Systems. Daegu, Korea:Curran Associates Inc. 2013: 2787-2795.)
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    [1]
    魏慧娟,戴牡红,宁勇余.基于最近邻居聚类的协同过滤推荐算法[J].中国科学技术大学学报,2016, 46(9):736-742.
    WEI Huijuan, DAI Muhong, NING Yongyu. Collaborative filtering recommendation algorithm based on nearest neighbor clustering[J]. Journal of University of Science and Technology of China, 2016, 46(9): 736-742.
    [2]
    陈洁敏,汤庸,李建国,等.个性化推荐算法研究[J].华南师范大学学报,2014,46(5):8-15.
    CHEN Jiemin,TANG Yong, LI Jianguo, et al. Survey of personalized recommendation algorithm[J]. Journal of South China Normal University, 2014, 46(5):8-15.
    [3]
    WU M L, CHANG C H,LIU R Z.Integrating content-based filtering with collaborative filtering using co-clustering with augmented matrices[J].Expert Systems with Applications,2014, 41(6):2754-2761.
    [4]
    冷亚军,陆青,梁昌勇.协同过滤推荐技术综述[J].模式识别与人工智能,2014,27(8):720-734.
    LENG Yajun, LU Qing, LIANG Changyong. Survey of recommendation based on collaborative filting[J]. Pattern Recognition and Artificial Intelligence, 2014,27(8):720-734.
    [5]
    杨武,唐瑞,卢玲.基于内容的推荐与协同过滤融合的新闻推荐方法[J].计算机应用,2016,36(2):414-418.
    YANG Wu, TANG Rui, LU Ling. News recommendation method by fusion of content-based recommendation and collaborative filtering[J]. Journal of Computer Application, 2016,36(2):414-418.
    [6]
    郭云飞,方耀宁,扈红超.基于Logistic函数的社会化矩阵分解推荐算法[J].北京理工大学学报,2016, 36(1):70-74.
    GUO Yunfei, FANG Yaoning, HU Hongchao. A social matrix factorization recommender algorithm based on logistic function[J]. Transactions of Beijing Institute of Technology, 2016, 36(1):70-74.
    [7]
    张宜浩,朱小飞,徐传运,等.基于用户评论的深度情感分析和多视图协同融合的混合推荐方法[J].计算机学报,2019, 42(6): 1316-1333.
    ZHANG Yihao, ZHU Xiaofei, XU Chuanyun, et al. Hybrid recommendation approach based on deep sentiment analysis of user reviews and multi-view collaborative fusion[J]. Chinese Journal of Computers, 2019, 42(6):1316-1333.
    [8]
    王巍巍,王志刚,潘亮铭,等.双语影视知识图谱的构建研究[J].北京大学学报,2016, 52(01):25-34.
    WANG Weiwei, WANG Zhigang, PAN Liangming, et al. Research on the construction of bilingual movie knowledge graph[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2016, 52(01):25-34.
    [9]
    吴玺煜,陈启买,刘海,等.基于知识图谱表示学习的协同过滤推荐算法[J].计算机工程,2018,44(02):226-232, 263.
    WU Xiyu, CHEN Qimai, LIU Hai, et al. Collaborative filtering recommendation algorithm based on representation learning of knowledge graph[J]. Computer Engineering, 2018, 44(02):226-232, 263.
    [10]
    BOBADILLA J. Collaborative filtering based on significances[J]. Information Sciences,2012, 185(1):1-17.
    [11]
    ZHOU T Q, CHEN L N, SHEN J. Movie recommendation system employing the user-based CF in Cloud computing[C]// IEEE International Conference on Computational Science and Engineering. Guangzhou, China: IEEE, 2017:46-50.
    [12]
    ZHANG H Y, GANCHEV I, NIKOLOV N S, et al. A trust-enriched approach for item-based collaborative filtering recommendations[C]// IEEE, International Conference on Intelligent Computer Communication and Processing. Cluj-Napoca, Romania:IEEE, 2016:65-68.
    [13]
    肖文强,姚世军,吴善明.一种改进的top-N协同过滤推荐算法[J].计算机应用研究,2018,35(1):105-108,112.
    XIAO Wenqiang, YAO Shijun, WU Shanming. Improved top-N collaborative filtering recommendation algorithm[J].Application Research of Computers,2018,35(1):105-108,112.
    [14]
    刘峤,李杨,段宏,等.知识图谱构建技术综述[J].计算机研究与发展,2016,53(3):582-600.
    LIU Qiao, LI Yang, DUAN Hong, et al. Knowledge graph construction techniques[J]. Journal of Computer Research and Development,2016,53(3):582-600.
    [15]
    MIKOLOV T, SUTSKEVER I, CHEN K, et al. Distributed representations of words and phrases and their compositionality[C]// International Conference on Neural Information Processing Systems. Daegu, Korea: Curran Associates Inc.2013:3111-3119.
    [16]
    FAN M, ZHOU Q, ZHENG T F, et al. Distributed representation learning for knowledge graphs with entity descriptions[J]. Pattern Recognition Letters, 2017,93(1):31-37.
    [17]
    BENGIO Y, COURVILLE A, VINCENT P. Representation learning: A review and new perspectives[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2013, 35(8):1798-1828.
    [18]
    刘知远,孙茂松,林衍凯,等.知识表示学习研究进展[J].计算机研究与发展,2016, 53(2):247-261.
    LIU Zhiyuan, SUN Maosong, LIN Yankai, et al. Knowledge representation learning: A review[J]. Journal of Computer Research and Development,2016, 53(2):247-261.
    [19]
    BORDES A , USUNIER N , GARCIA-DURAN A , et al. Translating embeddings for modeling multi-relational data[C]// International Conference on Neural Information Processing Systems. Daegu, Korea:Curran Associates Inc. 2013: 2787-2795.)
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