[1]CHEN Juntong,GU Tianlong,CHANG Liang,et al.A tourist group recommendation method combining collaborative filtering and user preferences[J].CAAI Transactions on Intelligent Systems,2018,13(6):999-1005.[doi:10.11992/tis.201802011]
Copy

A tourist group recommendation method combining collaborative filtering and user preferences

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 13 Number of periods: 2018 6 Page number: 999-1005 Column: 学术论文—机器学习 Public date: 2018-10-25
Title:
A tourist group recommendation method combining collaborative filtering and user preferences
Author(s):
CHEN Juntong;  GU Tianlong;  CHANG Liang;  BIN Chenzhong;  LIANG Cong
Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin 541004, China
Keywords:
group recommendation; tourism recommendation; data sparsity; collaborative filtering; user’s preference; average strategy; least misery strategy
CLC:
TP391
DOI:
10.11992/tis.201802011
Abstract:
In recent years, the group recommendation system has gained much attention in the field of tourism recommendation. The problem of data sparsity faced by the traditional recommendation system also exists in the group recommendation system. In the scoring-based recommendation system, the group recommendation system can be divided into two stages:preference prediction for individual users and aggregation of the forecast results of group members. To improve the effect of recommendation, a tourist group recommendation approach is proposed that incorporates collaborative filtering and users’ preferences. It considers the accuracy of user’s predictive scores and the group recommendation result. In the collaborative filtering, the predictive score is calculated by adding the similarity impact factor and the relevancy factor. Based on the average strategy and the least misery strategy, a satisfaction balance strategy is proposed, which considers both of the partial satisfaction and whole satisfaction of the group members. A series of conducted experiments show that the proposed method yields more accurate recommendations.
References:
[1] DE PESSEMIER T, DHONDT J, MARTENS L. Hybrid group recommendations for a travel service[J]. Multimedia tools and applications, 2017, 76(2):2787-2811.
[2] 雷震, 文益民, 王志强, 等. 基于影响力控制的热传导算法[J]. 智能系统学报, 2016, 11(3):328-335 LEI Zhen, WEN Yimin, WANG Zhiqiang, et al. Heat conduction controlled by the influence of users and items[J]. CAAI transactions on intelligent systems, 2016, 11(3):328-335
[3] YU Zhiwen, ZHOU Xingshe, HAO Yanbin, et al. TV program recommendation for multiple viewers based on user profile merging[J]. User modeling and user-adapted interaction, 2006, 16(1):63-82.
[4] 常亮, 曹玉婷, 孙文平, 等. 旅游推荐系统研究综述[J]. 计算机科学, 2017, 44(10):1-6 CHANG Liang, CAO Yuting, SUN Wenping, et al. Review on tourism recommendation system[J]. Computer science, 2017, 44(10):1-6
[5] 胡川, 孟祥武, 张玉洁, 等. 一种改进的偏好融合组推荐方法[J]. 软件学报, doi:10.13328/j.cnki.jos.005288. HU Chuan, MENG Xiangwu, ZHANG Yujie, et al. An enhanced group recommendation method based on preference aggregation[J]. Journal of software, doi:10.13328/j.cnki.jos.005288., doi:10. 13328/j.cnki.jos.005288
[6] 吴毅涛, 张兴明, 王兴茂, 等. 基于用户模糊相似度的协同过滤算法[J]. 通信学报, 2016, 37(1):198-206 WU Yitao, ZHANG Xingming, WANG Xingmao, et al. User fuzzy similarity-based collaborative filtering recommendation algorithm[J]. Journal on communications, 2016, 37(1):198-206
[7] 张玉洁, 杜雨露, 孟祥武. 组推荐系统及其应用研究[J]. 计算机学报, 2016, 39(4):745-764 ZHANG Yujie, DU Yulu, MENG Xiangwu. Research on group recommender systems and their applications[J]. Chinese journal of computers, 2016, 39(4):745-764
[8] 李慧, 马小平, 胡云, 等. 融合上下文信息的社会网络推荐系统[J]. 智能系统学报, 2015, 10(2):293-300 LI Hui, MA Xiaoping, HU Yun, et al. Social network recommendaton system mixing contex information[J]. CAAI transactions on intelligent systems, 2015, 10(2):293-300
[9] MASTHOFF J. Group modeling:selecting a sequence of television items to suit a group of viewers[J]. User modeling and user-adapted interaction, 2004, 14(1):37-85.
[10] MASTHOFF J. Group recommender systems:combining individual models[M]//RICCI F, ROKACH L, SHAPIRA B, et al. Recommender Systems Handbook. Boston, USA:Springer, 2011:677-702.
[11] BOBADILLA J, ORTEGA F, HERNANDO A, et al. Recommender systems survey[J]. Knowledge-based systems, 2013, 46:109-132.
[12] LI Wenqiang, XU Hongji, JI Mingyang, et al. A hierarchy weighting similarity measure to improve user-based collaborative filtering algorithm[C]//Proceedings of the 20162nd IEEE International Conference on Computer and Communications. Chengdu, China, 2016:843-846.
[13] CHEN Xuegang, XIA Mingna, CHENG Jieren, et al. Trend prediction of internet public opinion based on collaborative filtering[C]//Proceedings of the 201612th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery. Changsha, China, 2016:583-588.
[14] 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 Ailin, ZHU Yangyong, SHI Baile. A collaborative filtering recommendation algorithm based on item rating prediction[J]. Journal of software, 2003, 14(9):1621-1628
[15] LI Dongsheng, CHEN Chao, LV Qin, et al. An algorithm for efficient privacy-preserving item-based collaborative filtering[J]. Future generation computer systems, 2016, 55:311-320.
[16] CHRISTENSEN I, SCHIAFFINO S, ARMENTANO M. Social group recommendation in the tourism domain[J]. Journal of intelligent information systems, 2016, 47(2):209-231.
[17] BALTRUNAS L, MAKCINSKAS T, RICCI F. Group recommendations with rank aggregation and collaborative filtering[C]//Proceedings of the 4th ACM Conference on Recommender Systems. Barcelona, Spain, 2010:119-126.
Similar References:

Memo

-

Last Update: 2018-12-25

Copyright © CAAI Transactions on Intelligent Systems