research-article

A new confidence-based recommendation approach

Authors:

Faezeh Sadat Gohari,

Fereidoon Shams Aliee,

Hassan HaghighiAuthors Info & Claims

Information Sciences—Informatics and Computer Science, Intelligent Systems, Applications: An International Journal, Volume 422, Issue C

Pages 21 - 50

https://doi.org/10.1016/j.ins.2017.09.001

Published: 01 January 2018 Publication History

Abstract

Collaborative Filtering (CF) is one of the most successful recommendation techniques. Recently, implicit trust-based recommendation approaches have emerged that incorporate implicit trust information into CF in order to improve recommendation performance. Previous implicit trust models assume that all users have the same perception of ratings. However, although all users employ members of the same rating domain (e.g. ratings on a 15 scale), each individual has his own interpretations about a rating domain in order to express his preferences. Thus, it is reasonable that a user's rating vector has some degree of uncertainty, depending upon the rating usage trend of that user. In this paper, we present a new approach for confidence modeling in the context of recommender systems. The idea of this modeling is that confidence in a particular user depends not only on the trust in the opinions of that user but also on the certainty of these opinions. Based on this idea, we propose a new Confidence-Based Recommendation (CBR) approach. This approach employs four different confidence models that derive the users and items confidence values from both local and global perspectives. Experimental results on real-world data sets demonstrate the effectiveness of the proposed approach.

References

[1]

D.H. Alahmadi, X.-J. Zeng, Ists: implicit social trust and sentiment based approach to recommender systems, Expert Syst. Appl., 42 (2015) 8840-8849.

Digital Library

[2]

D. Anand, K.K. Bharadwaj, Utilizing various sparsity measures for enhancing accuracy of collaborative recommender systems based on local and global similarities, Expert Syst. Appl., 38 (2011) 5101-5109.

Digital Library

[3]

M.M. Azadjalal, P. Moradi, A. Abdollahpouri, M. Jalili, A trust-aware recommendation method based on Pareto dominance and confidence concepts, Knowl.-Based Syst., 116 (2017) 130-143.

Digital Library

[4]

N.X. Bach, N.D. Hai, T.M. Phuong, Personalized recommendation of stories for commenting in forum-based social media, Inf. Sci., 352353 (2016) 48-60.

Digital Library

[5]

G.S. Basheer, M.S. Ahmad, A.Y.C. Tang, S. Graf, Certainty, trust and evidence: towards an integrative model of confidence in multi-agent systems, Comput. Hum. Behav., 45 (2015) 307-315.

Digital Library

[6]

P. Bedi, R. Sharma, Trust based recommender system using ant colony for trust computation, Expert Syst. Appl., 39 (2012) 1183-1190.

Digital Library

[7]

J. Bobadilla, F. Ortega, A. Hernando, J. Bernal, A collaborative filtering approach to mitigate the new user cold start problem, Knowl.-Based Syst., 26 (2012) 225-238.

Digital Library

[8]

J.S. Breese, D. Heckerman, C. Kadie, Empirical analysis of predictive algorithms for collaborative filtering, in: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann Publishers Inc, 1998, pp. 43-52.

Digital Library

[9]

C.C. Chen, Y.-H. Wan, M.-C. Chung, Y.-C. Sun, An effective recommendation method for cold start new users using trust and distrust networks, Inf. Sci., 224 (2013) 19-36.

Digital Library

[10]

F.S. Gohari, H. Haghighi, F.S. Aliee, A semantic-enhanced trust based recommender system using ant colony optimization, Appl. Intell., 46 (2017) 328-364.

Digital Library

[11]

F.S. Gohari, M.J. Tarokh, New recommender framework: combining semantic similarity fusion and bicluster collaborative filtering, Comput. Intell., 32 (2016) 561-586.

Digital Library

[12]

G. Guo, J. Zhang, D. Thalmann, Merging trust in collaborative filtering to alleviate data sparsity and cold start, Knowl.-Based Syst., 57 (2014) 57-68.

Digital Library

[13]

J. Guo, J. Guo, Research on information entropy measure based on collaborative filtering algorithm, Int. J. Hybrid Inf. Technol., 9 (2016) 1-10.

[14]

C. Haydar, A. Roussanaly, A. Boyer, Local trust versus global trust networks in subjective logic, in: Web Intelligence (WI) and Intelligent Agent Technologies (IAT), 2013 IEEE/WIC/ACM International Joint Conferences On, IEEE, 2013, pp. 29-36.

Digital Library

[15]

A. Hernando, J. Bobadilla, F. Ortega, A non negative matrix factorization for collaborative filtering recommender systems based on a Bayesian probabilistic model, Knowl.-Based Syst., 97 (2016) 188-202.

Digital Library

[16]

A. Hernando, J. Bobadilla, F. Ortega, A. Gutirrez, A probabilistic model for recommending to new cold-start non-registered users, Inf. Sci., 376 (2017) 216-232.

Digital Library

[17]

Y. Huang, B. Cui, J. Jiang, K. Hong, W. Zhang, Y. Xie, Real-time Video Recommendation Exploration, in: Proceedings of the 2016 International Conference on Management of Data, ACM, 2016.

Digital Library

[18]

C.-S. Hwang, Y.-P. Chen, Using trust in collaborative filtering recommendation, Springer, 2007.

[19]

D.I. Ignatov, S.I. Nikolenko, T. Abaev, J. Poelmans, Online recommender system for radio station hosting based on information fusion and adaptive tag-aware profiling, Expert Syst. Appl., 55 (2016) 546-558.

Digital Library

[20]

X. Jin, Y. Zhou, B. Mobasher, A maximum entropy web recommendation system: combining collaborative and content features, in: Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, ACM, 2005, pp. 612-617.

Digital Library

[21]

C. Kaleli, An entropy-based neighbor selection approach for collaborative filtering, Knowl.-Based Syst., 56 (2014) 273-280.

Digital Library

[22]

H.-N. Kim, A.-T. Ji, G.-S. Jo, Enhanced Prediction Algorithm for Item-Based Collaborative Filtering Recommendation, in: E-Commerce and Web Technologies, Springer, Berlin Heidelberg, 2006, pp. 41-50.

Digital Library

[23]

Y. Koren, Collaborative filtering with temporal dynamics, Commun. ACM., 53 (2010) 89-97.

Digital Library

[24]

H.-J. Kwon, T.-H. Lee, J.-H. Kim, K.-S. Hong, Improving Prediction accuracy using entropy weighting in collaborative filtering, in: Ubiquitous, Autonomic and Trusted Computing, 2009. UIC-ATC09. Symposia and Workshops On, IEEE, 2009, pp. 40-45.

Digital Library

[25]

C.-H. Lai, D.-R. Liu, C.-S. Lin, Novel personal and group-based trust models in collaborative filtering for document recommendation, Inf. Sci., 239 (2013) 31-49.

Digital Library

[26]

N. Lathia, S. Hailes, L. Capra, Trust-based collaborative filtering, in: Trust Management II, Springer, 2008.

[27]

H. Liu, Z. Hu, A. Mian, H. Tian, X. Zhu, A new user similarity model to improve the accuracy of collaborative filtering, Knowl.-Based Syst., 56 (2014) 156-166.

Digital Library

[28]

P. Massa, P. Avesani, Trust-aware collaborative filtering for recommender systems, Springer, 2004.

[29]

A. Mnih, R.R. Salakhutdinov, Probabilistic matrix factorization, Adv. Neural Inf. Process. Syst., 20 (2008) 1257-1264.

Digital Library

[30]

X. Ning, C. Desrosiers, G. Karypis, A comprehensive survey of neighborhood-based recommendation methods, Springer, 2015.

[31]

J. O'Donovan, B. Smyth, Trust in recommender systems, in: Proceedings of the 10th International Conference on Intelligent User Interfaces, ACM, 2005, pp. 167-174.

Digital Library

[32]

M. Papagelis, D. Plexousakis, T. Kutsuras, Alleviating the sparsity problem of collaborative filtering using trust inferences, Springer, 2005.

[33]

N. Pappas, A. Popescu-Belis, Adaptive sentiment-aware one-class collaborative filtering, Expert Syst. Appl., 43 (2016) 23-41.

Digital Library

[34]

C. Park, D. Kim, J. Oh, H. Yu, Improving top-K recommendation with truster and trustee relationship in user trust network, Inf. Sci., 374 (2016) 100-114.

Digital Library

[35]

Y. Park, S. Park, W. Jung, S. Lee, CF Reversed, A fast collaborative filtering algorithm using a k-nearest neighbor graph, Expert Syst. Appl., 42 (2015) 4022-4028.

Digital Library

[36]

B.K. Patra, R. Launonen, V. Ollikainen, S. Nandi, A new similarity measure using Bhattacharyya coefficient for collaborative filtering in sparse data, Knowl.-Based Syst., 82 (2015) 163-177.

Digital Library

[37]

C.-H. Piao, J. Zhao, L.-J. Zheng, Research on entropy-based collaborative filtering algorithm and personalized recommendation in e-commerce, Serv. Oriented Comput. Appl., 3 (2009) 147-157.

[38]

P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, J. Riedl, GroupLens: an open architecture for collaborative filtering of netnews, in: Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work, ACM, 1994, pp. 175-186.

Digital Library

[39]

B. Sarwar, G. Karypis, J. Konstan, J. Riedl, Item-based collaborative filtering recommendation algorithms, in: Proceedings of the 10th International Conference on World Wide Web, ACM, 2001, pp. 285-295.

Digital Library

[40]

J. Serrano-Guerrero, F.P. Romero, J.A. Olivas, Hiperion: A fuzzy approach for recommending educational activities based on the acquisition of competences, Inf. Sci., 248 (2013) 114-129.

[41]

Q. Shambour, J. Lu, A trust-semantic fusion-based recommendation approach for e-business applications, Decis. Support Syst., 54 (2012) 768-780.

Digital Library

[42]

Q. Shambour, J. Lu, An effective recommender system by unifying user and item trust information for B2B applications, J. Comput. Syst. Sci, 81 (2015) 1110-1126.

Digital Library

[43]

C.E. Shannon, A mathematical theory of communication, Bell Syst. Tech. J, 27 (1948) 379-423.

[44]

Y. Shi, M. Larson, A. Hanjalic, Unifying rating-oriented and ranking-oriented collaborative filtering for improved recommendation, Inf. Sci., 229 (2013) 29-39.

Digital Library

[45]

A. Tejeda-Lorente, C. Porcel, J. Bernab-Moreno, E. Herrera-Viedma, REFORE: A recommender system for researchers based on bibliometrics, Appl. Soft Comput., 30 (2015) 778-791.

Digital Library

[46]

. Tejeda-Lorente, C. Porcel, E. Peis, R. Sanz, E. Herrera-Viedma, A quality based recommender system to disseminate information in a university digital library, Inf. Sci., 261 (2014) 52-69.

Digital Library

[47]

N. Verbiest, C. Cornelis, P. Victor, E. Herrera-Viedma, Trust and distrust aggregation enhanced with path length incorporation, Fuzzy Sets Syst., 202 (2012) 61-74.

Digital Library

[48]

P. Victor, N. Verbiest, C. Cornelis, M.D. Cock, Enhancing the trust-based recommendation process with explicit distrust, ACM Trans. Web., 7 (2013).

Digital Library

[49]

W. Wang, G. Zhang, J. Lu, Collaborative filtering with entropy-driven user similarity in recommender systems, Int. J. Intell. Syst., 30 (2015) 854-870.

Digital Library

[50]

W. Yuan, D. Guan, Y.-K. Lee, S. Lee, S.J. Hur, Improved trust-aware recommender system using small-worldness of trust networks, Knowl.-Based Syst., 23 (2010) 232-238.

Digital Library

Cited By

Tang CZhao SChen BLu XZhang Q(2024)A two-dimensional time-aware cloud service recommendation approach with enhanced similarity and trustJournal of Parallel and Distributed Computing10.1016/j.jpdc.2024.104889190:COnline publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1016/j.jpdc.2024.104889
Sun NLuo QRan LJia P(2023)Similarity matrix enhanced collaborative filtering for e-government recommendationData & Knowledge Engineering10.1016/j.datak.2023.102179145:COnline publication date: 1-May-2023
https://dl.acm.org/doi/10.1016/j.datak.2023.102179
Kagita VPujari APadmanabhan VKumar V(2022)Inductive conformal recommender systemKnowledge-Based Systems10.1016/j.knosys.2022.109108250:COnline publication date: 17-Aug-2022
https://dl.acm.org/doi/10.1016/j.knosys.2022.109108
Show More Cited By

Recommendations

A social recommendation method based on an adaptive neighbor selection mechanism

An adaptive neighbor selection mechanism is proposed for recommender systems.The proposed method incorporates social information into recommendation process.The quality of predicted ratings is evaluated using a reliability measure.A new confidence model ...
Trust-based collaborative filtering: tackling the cold start problem using regular equivalence
RecSys '18: Proceedings of the 12th ACM Conference on Recommender Systems

User-based Collaborative Filtering (CF) is one of the most popular approaches to create recommender systems. This approach is based on finding the most relevant k users from whose rating history we can extract items to recommend. CF, however, suffers ...
A trust-aware recommendation method based on Pareto dominance and confidence concepts

We proposed a trust-based collaborative filtering method called CPD.Implicit trust statements are identified and used in the recommendation process.A reliability measure is used to evaluate quality of implicit trust statements.Most prominent users are ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Information Sciences: an International Journal

Information Sciences: an International Journal Volume 422, Issue C

January 2018

543 pages

ISSN:0020-0255

Issue’s Table of Contents

Copyright © Elsevier Inc.

Publisher

Elsevier Science Inc.

United States

Publication History

Published: 01 January 2018

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

16
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 06 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Tang CZhao SChen BLu XZhang Q(2024)A two-dimensional time-aware cloud service recommendation approach with enhanced similarity and trustJournal of Parallel and Distributed Computing10.1016/j.jpdc.2024.104889190:COnline publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1016/j.jpdc.2024.104889
Sun NLuo QRan LJia P(2023)Similarity matrix enhanced collaborative filtering for e-government recommendationData & Knowledge Engineering10.1016/j.datak.2023.102179145:COnline publication date: 1-May-2023
https://dl.acm.org/doi/10.1016/j.datak.2023.102179
Kagita VPujari APadmanabhan VKumar V(2022)Inductive conformal recommender systemKnowledge-Based Systems10.1016/j.knosys.2022.109108250:COnline publication date: 17-Aug-2022
https://dl.acm.org/doi/10.1016/j.knosys.2022.109108
Alsaadi FWang ZAlharbi NLiu YAlotaibi N(2022)A new framework for collaborative filtering with p-moment-based similarity measureKnowledge-Based Systems10.1016/j.knosys.2022.108874248:COnline publication date: 19-Jul-2022
https://dl.acm.org/doi/10.1016/j.knosys.2022.108874
Qie XWu JLi YSun Y(2022)A stage model for agent-based emotional persuasion with an adaptive targetInformation Sciences: an International Journal10.1016/j.ins.2022.07.147610:C(90-113)Online publication date: 1-Sep-2022
https://dl.acm.org/doi/10.1016/j.ins.2022.07.147
Li JLin J(2022)A probability distribution detection based hybrid ensemble QoS prediction approachInformation Sciences: an International Journal10.1016/j.ins.2020.01.046519:C(289-305)Online publication date: 22-Apr-2022
https://dl.acm.org/doi/10.1016/j.ins.2020.01.046
Rahim ADurrani MGillani SAli ZHasan NKim M(2022)An efficient recommender system algorithm using trust dataThe Journal of Supercomputing10.1007/s11227-021-03991-278:3(3184-3204)Online publication date: 1-Feb-2022
https://dl.acm.org/doi/10.1007/s11227-021-03991-2
Ramezani MAkhlaghian Tab FAbdollahpouri AAbdulla Mohammad M(2021)A new generalized collaborative filtering approach on sparse data by extracting high confidence relations between usersInformation Sciences: an International Journal10.1016/j.ins.2021.04.025570:C(323-341)Online publication date: 1-Sep-2021
https://dl.acm.org/doi/10.1016/j.ins.2021.04.025
Gohari FAliee FHaghighi H(2020)A significance-based trust-aware recommendation approachInformation Systems10.1016/j.is.2019.10142187:COnline publication date: 1-Jan-2020
https://dl.acm.org/doi/10.1016/j.is.2019.101421
Qi FTong XYu LWang Y(2019)Personalized project recommendations: using reinforcement learningEURASIP Journal on Wireless Communications and Networking10.1186/s13638-019-1619-62019:1Online publication date: 21-Dec-2019
https://dl.acm.org/doi/10.1186/s13638-019-1619-6
Show More Cited By

View Options

View options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Issue’s Table of Contents