Faced with increasing number of hotels in online tourism, the problem of sparse data is becoming more and more serious. On one hand, it leads to a significant decrease in recommendation accuracy; on the other hand, the computational load of traditional recommendation algorithm is increased, which is difficult to meet the real-time requirement. So, this paper firstly proposed a preference degree based personalized recommendation algorithm which mined the potential correlation between user historical data and recommend items. The novel algorithm utilized users’ historical data to calculate preference degree and then construct new features, and its realization is based on classification algorithm. Besides, the new method is applied to make personalized recommendations in online tourism. Results from real data sets showed that the proposed preference degree based personalized recommendation algorithm is effective and universal.
［1］SARWAR B, KARYPIS G, KONSTAN J, et al. Analysis of recommendation algorithms for e-commerce［C］∥ACM Conference on Electronic Commerce. Minneapolis: ACM, 2000:158-167.
［2］PERUGINI S, GONCALVES M A. Recommendation and personalization: A survey［J］. Journal of Intelligent Information Systems, 2002, 23(2):107-143.
［3］BATET M, MORENO A, ISERN D. Turist@: Agent-based personalised recommendation of tourist activities［J］. Expert Systems with Applications, 2012, 39(8): 7319-7329.
［4］GAVALAS D, KENTERIS M. A web-based pervasive recommendation system for mobile tourist guides［J］. Personal & Ubiquitous Computing, 2011, 15(7): 759-770.
［5］VINODHINI S, RAJALAKSHMI V, GOVINDARAJULU B. Building personalised recommendation system with big data and Hadoop mapreduce［J］. Metabolism Clinical & Experimental, 2009, 58(1): 38.
［6］VERMA J P, PATEL B, PATEL A. Big data analysis: Recommendation system with Hadoop framework［C］∥IEEE International Conference on Computational Intelligence & Communication Technology. Ghaziabad: IEEE, 2015: 92-97.
［7］ZHANG K, WANG K, WANG X, et al. Hotel recommendation based on user preference analysis［C］∥IEEE International Conference on Data Engineering Workshops. Seoul: IEEE, 2015:134-138.
［8］GAO H, LI W. A hotel recommendation system based on collaborative filtering and Rankboost algorithm［C］∥International Conference on Multimedia & Information Technology. Kaifeng: IEEE, 2010: 317-320.
［9］SALTON G, WONG A, YANG C S. A vector space model for automatic indexing［J］. Communications of the ACM, 1974, 18(11): 613-620.
［10］AHN J W, BRUSILOVSKY P, GRADY J, et al. Open user profiles for adaptive news systems: Help or harm?［C］∥International Conference on World Wide Web, WWW 2007. Banff: Springer International Publishing, 2007: 11-20.
［11］LI ZN, DREW M S, LIU J. Content-based retrieval in digital libraries［M］. Switzerland: Springer International Publishing, 2014: 93-95.
［12］刘健, 毕强, 刘庆旭, 等. 数字文献资源内容服务推荐研究——基于本体规则推理和语义相似度计算［J］. 现代图书情报技术, 2016, 32(9): 70-77.
LIU Jian, BI Qiang, LIU Qingxu, et al. New content recommendation service of digital literature［J］. New Technology of Library and Information Service, 2016, 32(9): 70-77.
［13］江周峰, 杨俊, 鄂海红. 结合社会化标签的基于内容的推荐算法［J］. 软件, 2015, 36(1): 1-5.
JIANG Zhoufeng, YANG Jun, E Haihong. A content-based recommendation algorithm with social tagging［J］. Software, 2015, 36(1): 1-5.
［14］WANG M, ZHENG Y, QIU M, et al. Research on schedule-based user recommendation model based on improved K-means algorithm［J］. Journal of Computational Methods in Sciences & Engineering, 2016, 16(3): 1-10.
［15］闫东东, 李红强. 一种改进的基于内容的个性化推荐模型［J］. 软件导刊, 2016, 15(4): 11-13.
YAN Dongdong, LI Hongqiang. An improved content-based personalized recommendation model［J］. Software Guide, 2016, 15(4): 11-13.
［16］李聪, 梁昌勇, 马丽. 基于领域最近邻的协同过滤推荐算法［J］. 计算机研究与发展, 2008, 45(9): 1532-1538.
LI Cong, LIANG Changyong, MA Li. A collaborative filtering recommendation algorithm based on domain nearest neighbor［J］. Journal of Computer Research and Development, 2008, 45(9): 1532-1538.
［17］黄创光, 印鉴, 汪静, 等. 不确定近邻的协同过滤推荐算法［J］. 计算机学报, 2010, 33(8): 1369-1377.
HUANG Chuangguang, YIN Jian, WANG Jing, et al. Uncertain neighbors’ collaborative filtering recommendation algorithm［J］. Chinese Journal of Computers, 2010, 33(8): 1369-1377.
［18］WANG Z, LI Q, LIU Y, et al. Online personalized recommendation based on streaming implicit user feedback［C］∥Asia-Pacific Web Conference. Guangzhou, China: Springer International Publishing, 2015: 720-731.
［19］王琳琳. 基于协同过滤的在线学习个性化推荐技术研究［J］. 微型电脑应用, 2017, 33(5): 49-51.
WANG Linlin. Research on personalized recommendation technology of online learning based on collaborative filtering［J］. Microcomputer Applications, 2017, 33(5): 49-51.
［20］应毅, 刘亚军, 陈诚. 基于云计算技术的个性化推荐系统［J］. 计算机工程与应用, 2015, 51(13): 111-117.
YING Yi, LIU Yajun, CHEN Cheng. Personalization recommender system based on cloud-computing technology［J］. Computer Engineering and Applications, 2015, 51(13): 111-117.
［21］SALAKHUTDINOV R, MNIH A, HINTON G. Restricted Boltzmann machines for collaborative filtering［C］∥Proceedings of the 24th International Conference on Machine Learning. Corvallis: ACM, 2007: 791-798.
［22］HE X, LIAO L, ZHANG H, et al. Neural collaborative filtering［C］∥Proceedings of the 26th International Conference on World Wide Web. Perth: Springer International Publishing, 2017: 173-182.
［23］HOSMER D W, LEMESBOW S. Goodness of fit tests for the multiple logistic regression model［J］. Communications in Statistics, 1980, 9(10): 1043-1069.
［24］CHEN T, HE T. Higgs boson discovery with boosted trees［C］∥NIPS 2014 Workshop on High-energy Physics and Machine Learning. Montreal: MIT Press, 2014: 69-80.
