Research on Collaborative Filtering Traveling Products Recommendation Algorithm Based on IUNCF
Zhao Ya’nan1(), Wang Yuqing2
1School of Economics and Management, Tongji University, Shanghai 200092, China 2School of Economics and Management, University of Shanghai for Science and Technology, Shanghai 200093, China
[Objective] This paper tries to address the challenges facing Smart Tourism industry, such as data sparseness and cold start, with the help of collaborative recommendation technology. [Methods] First, we clustered users with the K-means algorithm and then filtered and classified them dynamically based on the combination of collaborative recommendation technology. Then, we assigned weight to the recommended types and proposed a new algorithm based on Improved Uncertain Neighbors Collaborative Filtering (IUNCF). Finally, we examined the proposed algorithm with real world tourism data of different similarity thresholds and recommended numbers. [Results] The MAE value and F-measure reached 0.243 and 0.764, which showed the effectiveness of IUNCF in accuracy and reliability. [Limitations] The IUNCF algorithm needs to be further optimized to deal with the low frequency consumption issue. We could also extend the application of this new model. [Conclusions] The proposed IUNCF algorithm could precisely recommend smart tourism products to the consumers.
赵雅楠, 王育清. 基于不确定近邻的旅游产品协同过滤推荐算法研究*[J]. 数据分析与知识发现, 2018, 2(7): 63-71.
Zhao Ya’nan,Wang Yuqing. Research on Collaborative Filtering Traveling Products Recommendation Algorithm Based on IUNCF. Data Analysis and Knowledge Discovery, 2018, 2(7): 63-71.
(China Tourism Institute.China Tourism Economy Analysis and Forecast of Development in 2016[M]. Beijing: China Tourism Press, 2016.)
[3]
Nanopoulos A, Rafailidis D, Symeonidis P, et al.Musicbox: Personalized Music Recommendation Based on Cubic Analysis of Social Tags[J]. IEEE Transactions on Audio, Speech, and Language Processing, 2010, 18(2): 407-412.
doi: 10.1109/TASL.2009.2033973
[4]
Harper F M, Konstan J A.The Movielens Datasets: History and Context[J]. ACM Transactions on Interactive Intelligent Systems, 2016, 5(4): 19.
doi: 10.1145/2827872
[5]
Adomavicius G, Tuzhilin A.Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions[J]. IEEE Transactions on Knowledge & Data Engineering, 2005, 17(6): 734-749.
[6]
Dzyabura D, Hauser J R.Recommending Products When Consumers Learn Their Preferences[J]. Social Science Electronic Publishing, 2017, 55: 45-57.
doi: 10.2139/ssrn.2202904
[7]
Jia Z, Yang Y, Gao W, et al.User-based Collaborative Filtering for Tourist Attraction Recommendations[C]// Proceedings of the 2015 IEEE International Conference on Computational Intelligence & Communication Technology. 2015: 22-25.
[8]
Pirasteh P, Jung J J, Hwang D.Item-based Collaborative Filtering with Attribute Correlation: A Case Study on Movie Recommendation[A]//Intelligent Information and Database Systems[M]. Springer, 2014: 245-252.
[9]
Sahoo N, Singh P V, Mukhopadhyay T.A Hidden Markov Model for Collaborative Filtering[J]. MIS Quarterly, 2012, 1329-1356.
doi: 10.2139/ssrn.1700585
[10]
Ziani A, Azizi N, Schwab D, et al.Recommender System Through Sentiment Analysis[C]//Proceedings of the 2nd International Conference on Automatic Control, Telecommunications and Signals. 2017.
[11]
Dakhel G M, Mahdavi M.A New Collaborative Filtering Algorithm Using K-means Clustering and Neighbors’ Voting[C]// Proceedings of the 11th International Conference on Hybrid Intelligent Systems. IEEE, 2011: 179-184.
(Li Tao, Wang Jiandong, Ye Feiyue, et al.A Collaborative Filtering Recommendation Algorithm Based on User Clustering[J]. Systems Engineering and Electronics, 2007, 29(7): 1178-1182.)
(Zhao Wei, Lin Nan, Han Ying, et al.User-based Collaborative Filtering Recommendation Algorithm Based on Improved K-means Clustering[J]. Journal of Anhui University: Natural Science Edition, 2016, 40(2): 32-36.)
doi: 10.3969/j.issn.1000-2162.2016.02.006
[14]
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. ACM, 2001: 285-295.
(Deng Huaping.Time-Weighted Collaborative Filtering Algorithm Based on Item Clustering and Scoring[J]. Application Research of Computers, 2015, 32(7): 1966-1969.)
(Wang Xiaoyun, Qian Lu, Huang Shiyou.Collaborative Filtering Recommendation Model Based on Rough User Clustering[J]. New Technology of Library and Information Service, 2015(1): 45-51.)
[17]
Nilashi M, Bin Ibrahim O, Ithnin N, et al.A Multi-criteria Collaborative Filtering Recommender System for the Tourism Domain Using Expectation Maximization (EM) and PCA-ANFIS[J]. Electronic Commerce Research and Applications, 2015, 14(6): 542-562.
