基于用户网络嵌入的民宿房源推荐方法

doi:10.11772/j.issn.1001-9081.2019040721

计算机应用 ›› 2019, Vol. 39 ›› Issue (11): 3398-3402.DOI: 10.11772/j.issn.1001-9081.2019040721

• 应用前沿、交叉与综合 • 上一篇下一篇

基于用户网络嵌入的民宿房源推荐方法

刘彤¹, 曾诚^1,2, 何鹏^1,2

1. 湖北大学计算机与信息工程学院, 武汉 430062;
2. 湖北省教育信息化工程技术研究中心, 武汉 430062

收稿日期:2019-04-26 修回日期:2019-07-12 发布日期:2019-08-26 出版日期:2019-11-10
通讯作者: 曾诚
作者简介:刘彤(1993-),男,甘肃白银人,硕士研究生,主要研究方向:大数据处理、推荐算法;曾诚(1976-),男,湖北武汉人,教授,博士,CCF会员,主要研究方向:大数据处理、领域软件工程、服务推荐;何鹏(1988-),男,湖北武汉人,副教授,博士,主要研究方向:大数据处理、软件度量、复杂网络。
基金资助:
国家自然科学基金青年项目（61902114）；湖北省技术创新重大专项（2016CFB309）。

Housing recommendation method based on user network embedding

LIU Tong¹, ZENG Cheng^1,2, HE Peng^1,2

1. School of Computer Science and Information Engineering, Hubei University, Wuhan Hubei 430062, China;
2. Hubei Engineering Research Center for Education Informationization, Wuhan Hubei 430062, China

Received:2019-04-26 Revised:2019-07-12 Online:2019-08-26 Published:2019-11-10
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61902114), the Hubei Province Major Technological Innovation Project (2016CFB309).

摘要/Abstract

摘要： 随着民宿行业的迅速发展，在线民宿订房系统开始流行起来。让用户在海量房源信息中快速找到所需房源是订房系统中待解决的问题。针对房源推荐中用户冷启动与数据稀疏性的问题，提出基于网络嵌入法的房源个性化推荐（UNER）方法。首先通过用户在系统中的历史行为数据及标签信息构建两类用户网络；然后基于网络嵌入法将网络映射至低维向量空间中，得到用户节点的向量表示并通过用户向量计算用户相似度矩阵；最后依据该矩阵为用户进行房源推荐。实验数据来源于贵州"水东乡舍"民宿订房系统。实验结果表明，相对于基于用户的协同过滤算法，所提方法的综合评价指标（F1）提升了20个百分点，平均正确率（MAP）提升11个百分点，体现出该方法的优越性。

关键词: 网络嵌入, 房源推荐, 协同过滤, 用户行为

Abstract: With the rapid development of the hotel industry, the online hotel reservation system has become popular. How to let users quickly find the housing they need from massive housing information is the problem to be solved in the reservation system. Aiming at the cold start and data sparseness of users in the housing recommendation, the User Network Embedding Recommendation (UNER) method based on the network embedding method was proposed. Firstly, two kinds of user networks were constructed by the user's historical behavior data and tag information in the system. Then, the network was mapped into the low-dimensional vector space based on the network embedding method, and the vector representation of the user node was obtained and the user similarity matrix was calculated by the user vector. Finally, according to the matrix, the housing recommendation was performed for the user. The experimental data come from the hotel reservation system of "Shuidongxiangshe" in Guizhou. The experimental results show that compared with the user-based collaborative filtering algorithm, the proposed method has the comprehensive evaluation index (F1) increased by 20 percentage points and the Mean Average Precision (MAP) increased by 11 percentage points, reflecting the superiority of the method.

Key words: network embedding, housing recommendation, collaborative filtering, user behavior

中图分类号:

TP311.51

刘彤, 曾诚, 何鹏. 基于用户网络嵌入的民宿房源推荐方法[J]. 计算机应用, 2019, 39(11): 3398-3402.

LIU Tong, ZENG Cheng, HE Peng. Housing recommendation method based on user network embedding[J]. Journal of Computer Applications, 2019, 39(11): 3398-3402.

参考文献

[1] DESHPANDE M, KARRYPIS G. Item-based Top-N recommendation algorithms[J]. ACM Transactions on Information Systems, 2004, 22(1):143-177.
[2] JUNG K Y, HWANG H J, KANG U G. Constructing full matrix through Naive Bayesian for collaborative filtering[C]//Proceedings of the 2006 International Conference on Intelligent Computing, LNCS 4114. Berlin:Springer, 2006:1210-1215.
[3] GOLDBERG D, NICHOLS D, OKI B M, et al. Using collaborative filtering to weave an information tapestry[J]. Communications of the ACM, 1992, 35(12):61-70.
[4] BREESE B J S, HECKERMAN D, KADIE C. Empirical analysis of predictive algorithms for collaborative filtering[C]//Proceedings of the 4th Conference on Uncertainty in Artificial Intelligence, 1998:43-52.
[5] 邓爱林,朱扬勇,施伯乐. 基于项目评分预测的协同过滤推荐算法[J]. 软件学报, 2003, 14(9):1621-1628. (DENG A L, ZHU Y Y, SHI B L. A collaborative filtering recommendation algorithm based on item rating prediction[J]. Journal of Software, 2003, 14(9):1621-1628.)
[6] 邵煜,谢颖华. 协同过滤算法中冷启动问题研究[J]. 计算机系统应用, 2019, 28(2):246-252. (SHAO Y, XIE Y H. Research on cold-start problem of collaborative filtering algorithm[J]. Computer Systems & Applications, 2019, 28(2):246-252.)
[7] LE T M V, LAUW H W. Probabilistic latent document network embedding[C]//Proceedings of the 2014 IEEE International Conference on Data Mining. Piscataway:IEEE, 2014:270-279.
[8] BOURIGAULT S, LAGNIER C, LAMPRIER S, et al. Learning social network embeddings for predicting information diffusion[C]//Proceedings of the 2014 IEEE International Conference on Data Mining. Piscataway:IEEE, 2014:393-402.
[9] PEROZZI B, AL-RFOU R, SKIENA S. DeepWalk:online learning of social representations[C]//Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York:ACM, 2014:701-710.
[10] XIE M, YIN H, XU F, et al. Graph-based metric embedding for next POI recommendation[C]//Proceedings of the 2016 International Conference on Web Information Systems Engineering, LNCS 10042. Berlin:Springer, 2016:207-222.
[11] XIE M, YIN H, WANG H, et al. Learning graph-based POI embedding for location-based recommendation[C]//Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. New York:ACM, 2016:15-24.
[12] DING R, CHEN Z. RecNet:a deep neural network for personalized POI recommendation in location-based social networks[J]. International Journal of Geographical Information Science, 2018, 32(8):1631-1648.
[13] HE X, LIAO L, ZHANG H, et al. Neural collaborative filtering[J]. Proceedings of the 26th International Conference on World Wide Web. Republic and Canton of Geneva, Switzerland:International World Wide Web Conferences Steering Committee, 2017:173-182.
[14] WANG D, CUI P, ZHU W. Structural deep network embedding[C]//Proceedings of the 22nd ACM SIGKDD International Conference. New York:ACM, 2016:1225-1234.
[15] MIKOLOV T, SUTSKEVER I, CHEN K, et al. Distributed representations of words and phrases and their compositionality[C]//Advances in Neural Information Processing Systems 26. Cambridge, MA:MIT Press, 2013:3111-3119.
[16] LUO C, ZHAN J, WANG L, et al. Cosine normalization:using cosine similarity instead of dot product in neural networks[EB/OL].[2019-03-20]. https://arxiv.org/pdf/1702.05870.pdf.
[17] 高阳,余建伟. 判断矩阵标度扩展法在不同标度下的比较[J]. 统计与决策, 2007(20):152-154. (GAO Y, YU J W. Comparison of judgment matrix scale expansion method under different scales[J]. Statistics and Decision, 2007(20):152-154.)

基于用户网络嵌入的民宿房源推荐方法

Housing recommendation method based on user network embedding

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