| Computer Science and Artificial Intelligence |
|
|
|
|
| Intelligent commercial site recommendation with neural collaborative filtering |
Nuo LI( ),Bin GUO*(),Yan LIU,Yao JING,Zhi-wen YU |
| School of Computer Science, Northwestern Polytechnical University, Xi'an 710072, China |
|
|
|
Abstract A new neural collaborative filtering method, NCF-RS, was proposed based on the framework of neural collaborative filtering (NCF). In order to discover the relationship between store category and site, the linear relationship between store categories and sites was studied by matrix decomposition, the non-linear and in-depth relationship between store categories and sites was studied by deep learning multi-layer perceptron, then the two relationships were combined for the final results. Restaurants’ data and POI data in Beijing were used to evaluate the performance of NCF-RS. Results indicate that NCF-RS has better performance in intelligent commercial site recommendation than other advanced deep learning methods and collaborative filtering methods, and can take linear and nonlinear relationships into better consideration.
|
|
Received: 17 December 2018
Published: 12 September 2019
|
|
|
|
Corresponding Authors:
Bin GUO
E-mail: linuo@mail.nwpu.edu.cn;guob@nwpu.edu.cn
|
神经协同过滤智能商业选址方法
为了挖掘商店类别与地址之间的关系，基于神经协同过滤（NCF）框架提出商业选址神经协同过滤方法——NeuMF-RS. 采用嵌入方式分别得到商店类别和地址的表示，利用矩阵分解的思想学习商店类别与地址之间的线性关系；利用深度学习多层感知机学习商店类别与地址之间非线性、深层次的关系；结合这2种方法学习到的关系得到最终结果. 利用北京市的餐馆数据与POI数据来评价NeuMF-RS方法的性能，结果表明，NeuMF-RS相对于其他先进的深度学习方法和协同过滤方法在商业选址方面具有更好的性能，更能兼顾线性与非线性关系.
关键词：
商业选址,
推荐系统,
神经协同过滤(NCF),
多层感知机,
矩阵分解
|
|
| [1] |
LIAN J, ZHANG F, XIE X, et al. Restaurant survival analysis with heterogeneous information [C] // Proceedings of the 26th International Conference on World Wide Web Companion. Perth: IW3C2, 2017: 993-1002.
|
|
|
| [2] |
ROIG-TIERNO N, BAVIERA-PUIG A, BUITRAGO-VERA J, et al The retail site location decision process using GIS and the analytical hierarchy process[J]. Applied Geography, 2013, 40: 191- 198
doi: 10.1016/j.apgeog.2013.03.005
|
|
|
| [3] |
XU M, WANG T, WU Z, et al. Demand driven store site selection via multiple spatial-temporal data [C] // Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. Burlingame: ACM, 2016: 40.
|
|
|
| [4] |
KARAMSHUK D, NOULAS A, SCELLATO S, et al. Geo-spotting: mining online location-based services for optimal retail store placement [C] // Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. Chicago: ACM, 2013: 793-801.
|
|
|
| [5] |
CHEN L, ZHANG D, PAN G, et al. Bike sharing station placement leveraging heterogeneous urban open data [C] // Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing. Osaka: ACM, 2015: 571-575.
|
|
|
| [6] |
CUO B, LI J, ZHENG V W, et al CityTransfer: Transferring Inter-and Intra-City knowledge for chain store site recommendation based on multi-source urban data[J]. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2018, 1 (4): 135
|
|
|
| [7] |
ZHANG S,YAO L,SUN A X,et al Deep learning based recommender system: a survey and new perspectives[J]. ACM Computing Surveys (CSUR), 2019, 52 (1): 5
|
|
|
| [8] |
HE X, LIAO L, ZHANG H, et al. Neural collaborative filtering [C] // Proceedings of the 26th International Conference on World Wide Web Companion. Perth: International World Wide Web Conferences Steering Committee, 2017: 173-182.
|
|
|
| [9] |
CHIEN Y H, GEORGE E I. A Bayesian model for collaborative filtering [C] // AISTATS. [S.l.]: [s.n.], 1999.
|
|
|
| [10] |
BROWNING J, MILLER D J A maximum entropy approach for collaborative filtering[J]. Journal of VLSI Signal Processing Systems for Signal, Image and Video Technology, 2004, 37 (2/3): 199- 209
doi: 10.1023/B:VLSI.0000027485.11890.15
|
|
|
| [11] |
KOREN Y, BWLL R, VOLINSKY C Matrix factorization techniques for recommender systems[J]. Computer, 2009, 42 (8): 30- 37
doi: 10.1109/MC.2009.263
|
|
|
| [12] |
TOROSLU ? H. A singular value decomposition approach for recommendation systems [D]. Ankara: METU, 2010: 1-67.
|
|
|
| [13] |
SARWAR B, KARYPIS G, KONSTAN J, et al. Item-based collaborative filtering recommendation algorithms [C] // Proceedings of the 10th international conference on World Wide Web. Hong Kong: ACM, 2001: 285-295.
|
|
|
| [14] |
GETOOR L, SAHAMI M. Using probabilistic relational models for collaborative filtering [C] // Workshop on Web Usage Analysis and User Profiling. [S.l.]: WEBKDD, 1999: 1-6
|
|
|
| [15] |
SHI Y, LARSON M, HANJALIC A Collaborative filtering beyond the user-item matrix: A survey of the state of the art and future challenges[J]. ACM Computing Surveys (CSUR), 2014, 47 (1): 3
|
|
|
| [16] |
KINGMA D P, Ba J. Adam: A method for stochastic optimization [J]. arXiv preprint. arXiv: 1412.6980, 2014.
|
|
|
| [17] |
ELKAHKY A M, SONG Y, HE X. A multi-view deep learning approach for cross domain user modeling in recommendation systems [C] // Proceedings of the 24th International Conference on World Wide Web. Florence: IW3C2, 2015: 278-288.
|
|
|
| [18] |
WANG H, WANG N, YEUNG D Y. Collaborative deep learning for recommender systems [C] // Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Sydney: ACM, 2015: 1235-1244.
|
|
|
| [19] |
MA W J. Deep learning meets recommendation systems [EB/OL]. NYC Data Science Academy. (2017-01-24). [2018-12-17]. https://nycdatascience.com/blog/student-works/deep-learning-meets-recommendation-systems/.
|
|
|
| [20] |
XUE H J, DAI X, ZHANG J, et al. Deep matrix factorization models for recommender systems [C] // Proceedings of the 26th International Joint Conference on Artificial Intelligence. Melbourne: AAAI, 2017: 3203-3209.
|
|
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
| |
Shared |
|
|
|
|
| |
Discussed |
|
|
|
|
