Top-N Recommendation Based on Mutual Trust and Influence

Dewen Seng, Jiaxin Liu, Xuefeng Zhang, Jing Chen, Xujian Fang

Abstract


To improve recommendation quality, the existing trust-based recommendation methods often directly use the binary trust relationship of social networks, and rarely consider the difference and potential influence of trust strength among users. To make up for the gap, this paper puts forward a hybrid top-N recommendation algorithm that combines mutual trust and influence. Firstly, a new trust measurement method was developed based on dynamic weight, considering the difference of trust strength between users. Secondly, a new mutual influence measurement model was designed based on trust relationship, in light of the social network topology. Finally, two hybrid recommendation algorithms, denoted as FSTA(Factored Similarity model with Trust Approach) and FSTI(Factored similarity models with trust and influence), were presented to solve the data sparsity and binarity. The two algorithms integrate user similarity, item similarity, mutual trust and mutual influence. Our approach was compared with several other recommendation algorithms on three standard datasets: FilmTrust, Epinions and Ciao. The experimental results proved the high efficiency of our approach.

Keywords


mutual trust, mutual influence, social recommendation system, cold start, data sparsity

Full Text:

PDF

References


Callebert, L.; Lourdeaux, D.; BarthA¨s, J.P. (2018). Collective activity and autonomous characters: trust-based decision-making system, Revue d'Intelligence Artificielle, 31(1-2), 153-181, 2018.
https://doi.org/10.3166/ria.31.153-181

Coste, B.; Ray, C.; Coatrieux, G. (2017). Trust modelling and measurements for the security of information systems, IngAŠnierie des SystA¨mes d'Information, 22(1), 19-41, 2017.
https://doi.org/10.3166/isi.22.1.19-41

Fang, H.; Bao, Y.; Zhang, J. (2014). Leveraging decomposed trust in probabilistic matrix factorization for effective recommendation, Twenty-Eighth AAAI Conference on Artificial Intelligence, 30-36, 2014.

Guo, X.; Yin, S.; Zhang, Y.; Li, W.; He, Q. (2019). Cold start recommendation based on attribute-fused singular value decomposition, IEEE Access, 7, 11349-11359, 2019.
https://doi.org/10.1109/ACCESS.2019.2891544

Guo G.; Zhang J.; Yorke-Smith N (2016). A Novel Recommendation Model Regularized with User Trust and Item Ratings, IEEE Transactions on Knowledge & Data Engineering, 28(7), 1607-1620, 2016.
https://doi.org/10.1109/TKDE.2016.2528249

Guo G.; Zhang J.; Zhu F. et al (2017). Factored similarity models with social trust for top-N item recommendation, Knowledge-Based Systems, 122, 17-25, 2017.
https://doi.org/10.1016/j.knosys.2017.01.027

Guo G(2019). List of Recommendation Data Sets, https://www.librec.net/datasets.html, 2011/6-2013/11.

Han, Z.M.; Chen, Y.; Liu, W.; Yuan, B.H.; Li, M.Q.; Duan, D.G. (2017). Research on node influence analysis in social networks, Journal of Software, 28(1): 84-104, 2017.

Jamali, M.; Ester, M. (2010). A matrix factorization technique with trust propagation for recommendation in social networks, ACM Conference on Recommender Systems, 135-142, 2010.
https://doi.org/10.1145/1864708.1864736

Kabbur, S.; Ning, X.; Karypis, G. (2013). Fism: factored item similarity models for top-n recommender systems, Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 659-667, 2013.
https://doi.org/10.1145/2487575.2487589

Li, W.; Ye, Z.; Xin, M.; Jin, Q. (2017). Social recommendation based on trust and influence in SNS environments, Multimedia Tools & Applications, 76(9), 11585-11602, 2017.
https://doi.org/10.1007/s11042-015-2732-0

Moradi, P.; Ahmadian, S. (2015). A reliability-based recommendation method to improve trust-aware recommender systems, Expert Systems with Applications, 42(21), 7386-7398, 2015.
https://doi.org/10.1016/j.eswa.2015.05.027

Pan, W.; Chen, L. (2013). GBPR: group preference based Bayesian personalized ranking for one-class collaborative filtering, Twenty-Third International Joint Conference on Artificial Intelligence, 2691-2697.

Pan, Y.; He, F.; Yu, H. (2018). Social recommendation algorithm using implicit similarity in trust, Chinese Journal of Computers, 41(1), 65-81, 2018.

Rendle, S.; Freudenthaler, C.; Gantner, Z.; Schmidt-Thieme, L. (2009). BPR: Bayesian personalized ranking from implicit feedback, Proceedings of the twenty-fifth conference on uncertainty in artificial intelligence, 452-461, 2009.

Tang, J.; Gao, H.; Liu, H.; Sarmas, A.D. (2012). eTrust: Understanding trust evolution in an online world, Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 253-261.
https://doi.org/10.1145/2339530.2339574

Wu, M.X.; Dong, L.S.; Jie, Z.Y.; Hu, X. (2015). Research on social recommender systems, Journal of Software, (6), 1356-1372, 2015.

Wang, M.; Ma, J. (2016). A novel recommendation approach based on users' weighted trust relations and the rating similarities, Soft Computing, 20(10), 3981-3990, 2016.
https://doi.org/10.1007/s00500-015-1734-1

Wang, Q.; Wang, J.H. (2015). Collaborative filtering recommendation algorithm combining trust mechanism with user preferences, Computer Engineering and Applications, 51(10), 261-265, 2015.

Yang, X.; Guo, Y.; Liu, Y.; Steck, H. (2014). A survey of collaborative filtering based social recommender systems, Computer Communications, 41, 1-10, 2014.
https://doi.org/10.1016/j.comcom.2013.06.009

Yao, Q.; Shi, R.; Zhou, C.; Wang, P.; Guo, L. (2016). Topic-aware social influence minimization, Proceedings of the 24th International Conference on World Wide Web, 139-140 2015.
https://doi.org/10.1145/2740908.2742767

Zhao, F.; Guo, Y. (2016). Improving Top-N recommendation with heterogeneous loss, International Joint Conference on Artificial Intelligence, 2378-2384, 2016.

Zhao, H.Y.; Hou, J.D.; Chen, Q.K. (2015). Collaborative filtering recommendation algorithm combining time weight and trust relationship, Application Research of Computers, 32(12), 3565-3568, 2015.

Zhang, D.; Sui, J.; Gong, Y. (2017). Large scale software test data generation based on collective constraint and weighted combination method, Tehnicki Vjesnik, 24(4), 1041-1050, 2017.
https://doi.org/10.17559/TV-20170319045945

Zhang, J.; Tang, J.; Li, J.; Liu, Y.; Xing, C.X. (2015). Who influenced you? Predicting retweet via social influence locality, ACM Transactions on Knowledge Discovery from Data (TKDD), 9(3), 25, 2015.
https://doi.org/10.1145/2700398




DOI: https://doi.org/10.15837/ijccc.2019.4.3578



Copyright (c) 2019 Dewen Seng, Jiaxin Liu, Jiaxin Liu, Xuefeng Zhang, Xuefeng Zhang, Jing Chen, Jing Chen, Xujian Fang, Xujian Fang

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

CC-BY-NC  License for Website User

Articles published in IJCCC user license are protected by copyright.

Users can access, download, copy, translate the IJCCC articles for non-commercial purposes provided that users, but cannot redistribute, display or adapt:

  • Cite the article using an appropriate bibliographic citation: author(s), article title, journal, volume, issue, page numbers, year of publication, DOI, and the link to the definitive published version on IJCCC website;
  • Maintain the integrity of the IJCCC article;
  • Retain the copyright notices and links to these terms and conditions so it is clear to other users what can and what cannot be done with the  article;
  • Ensure that, for any content in the IJCCC article that is identified as belonging to a third party, any re-use complies with the copyright policies of that third party;
  • Any translations must prominently display the statement: "This is an unofficial translation of an article that appeared in IJCCC. Agora University  has not endorsed this translation."

This is a non commercial license where the use of published articles for commercial purposes is forbiden. 

Commercial purposes include: 

  • Copying or downloading IJCCC articles, or linking to such postings, for further redistribution, sale or licensing, for a fee;
  • Copying, downloading or posting by a site or service that incorporates advertising with such content;
  • The inclusion or incorporation of article content in other works or services (other than normal quotations with an appropriate citation) that is then available for sale or licensing, for a fee;
  • Use of IJCCC articles or article content (other than normal quotations with appropriate citation) by for-profit organizations for promotional purposes, whether for a fee or otherwise;
  • Use for the purposes of monetary reward by means of sale, resale, license, loan, transfer or other form of commercial exploitation;

    The licensor cannot revoke these freedoms as long as you follow the license terms.

[End of CC-BY-NC  License for Website User]


INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL (IJCCC), With Emphasis on the Integration of Three Technologies (C & C & C),  ISSN 1841-9836.

IJCCC was founded in 2006,  at Agora University, by  Ioan DZITAC (Editor-in-Chief),  Florin Gheorghe FILIP (Editor-in-Chief), and  Misu-Jan MANOLESCU (Managing Editor).

Ethics: This journal is a member of, and subscribes to the principles of, the Committee on Publication Ethics (COPE).

Ioan  DZITAC (Editor-in-Chief) at COPE European Seminar, Bruxelles, 2015:

IJCCC is covered/indexed/abstracted in Science Citation Index Expanded (since vol.1(S),  2006); JCR2018: IF=1.585..

IJCCC is indexed in Scopus from 2008 (CiteScore2018 = 1.56):

Nomination by Elsevier for Journal Excellence Award Romania 2015 (SNIP2014 = 1.029): Elsevier/ Scopus

IJCCC was nominated by Elsevier for Journal Excellence Award - "Scopus Awards Romania 2015" (SNIP2014 = 1.029).

IJCCC is in Top 3 of 157 Romanian journals indexed by Scopus (in all fields) and No.1 in Computer Science field by Elsevier/ Scopus.

 

 Impact Factor in JCR2018 (Clarivate Analytics/SCI Expanded/ISI Web of Science): IF=1.585 (Q3). Scopus: CiteScore2018=1.56 (Q2); Editors-in-Chief: Ioan DZITAC & Florin Gheorghe FILIP.