[1]陈君同,古天龙,常亮,等.融合协同过滤与用户偏好的旅游组推荐方法[J].智能系统学报,2018,13(6):999-1005.[doi:10.11992/tis.201802011]
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]
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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]
融合协同过滤与用户偏好的旅游组推荐方法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
13
期数:
2018年第6期
页码:
999-1005
栏目:
学术论文—机器学习
出版日期:
2018-10-25
- Title:
- A tourist group recommendation method combining collaborative filtering and user preferences
- Keywords:
- group recommendation; tourism recommendation; data sparsity; collaborative filtering; user’s preference; average strategy; least misery strategy
- 分类号:
- TP391
- 摘要:
- 近年来，组推荐系统已经逐渐成为旅游推荐领域的研究热点之一。传统的推荐系统面临的数据稀疏性问题在组推荐系统中同样存在。基于评分的推荐系统中，可以把组推荐系统分为对单个用户的偏好预测和对组内成员预测结果的融合两个阶段。为提高推荐的效果，提出了一种融合协同过滤与用户偏好的旅游组推荐方法，它考虑了用户的预测评分和组推荐结果的准确性。在协同过滤中通过加入相似性影响因子和关联性因子进行预测评分，然后在均值策略和最小痛苦策略的基础上，提出了满意度平衡策略，该策略考虑了组内成员的局部满意度和整体满意度。实验表明，所提出的方法提高了推荐的准确率。
- 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.
- 参考文献/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.
备注/Memo
收稿日期:2018-02-07。
基金项目:国家自然科学基金项目（61572146，U1501252，U1711263）；广西创新驱动重大专项项目（AA17202024）；广西自然科学基金项目（2016GXNSFDA380006）.
作者简介:陈君同,男,1989年生,硕士研究生,主要研究方向为智能推荐系统;古天龙,男,1964年生,教授,博士生导师,博士,主要研究方向为形式化方法、知识工程与符号推理、协议工程与移动计算、可信泛在网络、嵌入式系统。主持国家863计划项目、国家自然科学基金、国防预研重点项目、国防预研基金等30余项。发表学术论文130余篇,其中被SCI、EI检索60余篇,出版学术著作3部;常亮,男,1980年生,教授,博士,中国计算机学会高级会员。主要研究方向为数据与知识工程、形式化方法、智能系统。主持并完成多项科研项目,其中国家自然科学基金项目1项、广西自然科学基金项目1项,发表学术论文70余篇,其中被SCI、EI检索60余篇。
通讯作者:宾辰忠.E-mail:binchenzhong@guet.edu.cn
更新日期/Last Update:
2018-12-25