Trust-based Modelling of Multi-criteria Crowdsourced Data

被引：9
作者：
Leal F. [1 ,2 ]
Malheiro B. [2 ,3 ]
González-Vélez H. [4 ]
Burguillo J.C. [1 ]
机构：
[1] EET/Uvigo – School of Telecommunications Engineering, University of Vigo, Vigo
[2] INESC TEC, Porto
[3] ISEP/IPP – School of Engineering, Polytechnic Institute of Porto, Porto
[4] CCC/NCI – Cloud Competency Centre, National College of Ireland, Dublin
来源：
Data Science and Engineering | 2017年 / 2卷 / 3期
关键词：
Collaborative filtering; Multi-criteria ratings; Prediction models; Tourism crowdsourcing; Trust;
D O I：
10.1007/s41019-017-0045-1
中图分类号：
学科分类号：
摘要：
As a recommendation technique based on historical user information, collaborative filtering typically predicts the classification of items using a single criterion for a given user. However, many application domains can benefit from the analysis of multiple criteria, e.g. tourists usually rate attractions (hotels, attractions, restaurants, etc.) using multiple criteria. In this paper, we argue that the personalised combination of multi-criteria data together with the creation and application of trust models should not only refine the tourist profile, but also improve the quality of the collaborative recommendations. The main contributions of this work are: (1) a novel profiling approach which takes advantage of the multi-criteria crowdsourced data and builds pairwise trust models and (2) the k-NN prediction of user ratings using trust-based neighbour selection. Significant experimental work has been performed using crowdsourced datasets from the Expedia and TripAdvisor platforms. © 2017, The Author(s).
引用
收藏
页码：199 / 209
页数：10
相关论文
共 37 条
  • [1] Adomavicius G., Kwon Y., New recommendation techniques for multicriteria rating systems, IEEE Intell Syst, 22, 3, pp. 48-55, (2007)
  • [2] Adomavicius G., Kwon Y., Multi-criteria recommender systems, Recommender Systems Handbook, 24, pp. 847-880, (2015)
  • [3] Amatriain X., Mining large streams of user data for personalized recommendations, ACM SIGKDD Explor Newsl, 14, 2, pp. 37-48, (2013)
  • [4] Bilge A., Kaleli C., A multi-criteria item-based collaborative filtering framework, JCSSE 2014, pp. 18-22, (2014)
  • [5] Breese J.S., Heckerman D., Kadie C., Empirical analysis of predictive algorithms for collaborative filtering, UAI’98, Madison, July 1998, pp. 43-52, (1998)
  • [6] Cremonesi P., Koren Y., Turrin R., Performance of recommender algorithms on top-n recommendation tasks, RecSys’10, Barcelona, September, pp. 39-46, (2010)
  • [7] Davoudi A., Chatterjee M., Modeling trust for rating prediction in recommender systems, Mlrec 2016, pp. 1-8, (2016)
  • [8] Ebadi A., Krzyzak A., A hybrid multi-criteria hotel recommender system using explicit and implicit feedbacks, Int J Comput Electr Autom Control Inf Eng, 10, 8, pp. 1377-1385, (2016)
  • [9] Egger R., Gula I., Walcher D., Open Tour Open Innov, (2016)
  • [10] Ekstrand M.D., Riedl J.T., Konstan J.A., Et al., Collaborative filtering recommender systems, Found Trends Hum Comput Interact, 4, 2, pp. 81-173, (2011)
1234