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Abstract

Collaborative filtering is a popular tool for recommendation systems. However, collaborative filtering technologies often suffer from high time complexity, the cold-start problem, and low coverage. Recent research shows that social networks and trust-aware methods can effectively solve these problems. Therefore, we propose a Trust Domain Expert Collaborative Filtering recommendation system. First, we divide the user item rating matrix into multiple sub-matrices based on the domain attributes of each item. For each sub-matrix, we then use domain experts to construct a user–expert trust matrix. Finally, combined with the target user’s domain of interest, we predict their missing ratings. Experimental results show that this method not only improves the accuracy and recommended coverage of collaborative filtering-based methods, but also reduces the computation time.

Keywords

Recommendation expert trust-aware collaborative filtering

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References

Asabere

N.Y.

Acakpovi

and Michael

, Improving socially-aware recommendation accuracy through personality, IEEE Transactions on Affective Computing, 2017.

Bobadilla

Ortega

Hernando

and Bernal

, A collaborative filtering approach to mitigate the new user cold start problem, Knowledge-Based Systems26 (2012), 225–238.

Bobadilla

Ortega

Hernando

and GutiÃ©rrez

, Recommender systems survey, Knowledge-Based Systems46 (2013), 109–132.

Cacheda

Carneiro

Fernández

and Formoso

, Comparison of collaborative filtering algorithms: Limitations of current techniques and proposals for scalable, high-performance recommender systems, ACM Trans Web5(1) (Feb. 2011), 2:1–2:33.

Cai

Leung

H.-F.

Min

Tang

and Li

, Typicality-based collaborative filtering recommendation, IEEE Trans. on Knowl. and Data Eng.26(3) (Mar. 2014), 766–779.

Deng

Huang

and Wu

, On deep learning for trust-aware recommendations in social networks, IEEE Transactions on Neural Networks & Learning Systems28 (2017), 1–14.

Dongyan

and Fuzhi

, A collaborative filtering recommendation algorithm based on double neighbor choosing strategy, Journal of Computer Research and Development5 (2013), 020.

Golbeck

J.A.

, Computing and Applying Trust in Web-based Social Networks, PhD thesis, College Park, MD, USA, 2005. AAI3178583.

Gunawardana

and Shani

, A survey of accuracy evaluation metrics of recommendation tasks, Journal of Machine Learning Research10 (Dec 2009), 2935–2962.

10.

Hema

and SowrirajaPillai

M.N.

, Efficient mining and recommendation of sparse data through collaborative filtering technique in medical transcriptions, In ICICES2014 – S.A. Engineering College, Chennai, Tamil Nadu, India, 2014.

11.

Jamali

and Ester

, Trustwalker: a random walk model for combining trust-based and item-based recommendation, In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, 2009, pp. 397–406.

12.

Zhu

and Zhang

, Rating knowledge sharing in cross-domain collaborative filtering,IEEE Transactions on Cybernetics45 (2014).

13.

Liang

and Ma

, A collaborative filtering recommendation algorithm based on domain nearest neighbor, Journal of Computer Research and Development45(9) (2008), 1532.

14.

Liu

Xia

Chen

Asabere

N.Y.

and Huang

, Trucom: Exploiting domain-specific trust networks for multicategory item recommendation, IEEE Systems Journal11 (2017), 295–304.

15.

Liu

Wang

and Ye

, A recommendation system algorithm based on large scale internet environment, In 2016 13th Web Information Systems and Applications Conference, 2016.

16.

Luo

OuYang

Xiong

and Yuan

, The effect of similarity support in k-nearest-neighborhood based collaborative filtering, Chinese Journal of Computer2 (2010), 5.

17.

Luo

Zhou

M.C.

Leung

Xia

Zhu

You

and Li

, An incremental-and-static-combined scheme for matrix-factorization-based collaborative filtering, IEEE Transactions on Automation Science & Engineering13(1) (2016), 333–343.

18.

Massa

and Avesani

, Trust-aware collaborative filtering for recommender systems, In OTM Confederated International Conferences “On the Move to Meaningful Internet Systems”, Springer, 2004, pp 492–508.

19.

Massa

and Avesani

, Trust-aware recommender systems, In Proceedings of the 2007 ACM conference on Recommender systems, ACM, 2007, pp. 17–24.

20.

Massa

and Avesani

, Trust metrics on controversial users, International Journal on Semantic Web & Information Systems3(1) (2009), 39–64.

21.

Massa

and Bhattacharjee

, Using trust in recommender systems: an experimental analysis, In International Conference on Trust Management, Springer, 2004, pp. 221–235.

22.

Meng

Liu

Zhang

and Hu

, Research on social recommender systems, Journal of Software26(6) (2015), 1356–1372.

23.

Middleton

S.E.

Shadbolt

N.R.

and De Roure

D.C.

, Ontological user profiling in recommender systems, ACM Trans. Inf. Syst.22(1) (Jan 2004), 54–88.

24.

Mustafa

Ibrahim

A.O.

Ahmed

and Abdullah

, Collaborative filtering: Techniques and applications, In 2017 International Conference on Communication, Control, Computing and Electronics Engineering (ICCCCEE), Khartoum, Sudan, 2017.

25.

O’Donovan

and Smyth

, Trust in recommender systems, In International Conference on Intelligent User Interfaces, January 10–13, 2005, San Diego, California, Usa, 2005, pp 167–174.

26.

Pazzani

M.J.

, A framework for collaborative, content-based and demographic filtering, Artificial intelligence review13(5-6) (1999), 393–408.

27.

Qiao

Yang

and Chen

, A trust calculating algorithm based on social networking service users’ context, Chinese Journal of Computers12 (2011), 015.

28.

Ray

and Mahanti

, Strategies for effective shilling attacks against recommender systems, In Lecture Notes in Computer Science, volume 5456, 2009, pp 111–125.

29.

Ren

Zhu

and Zhang

, A new collaborative filtering technique to improve recommendation diversity, In 2016 2nd IEEE International Conference on Computer and Communications, 2016.

30.

Resnick

Iacovou

Suchak

Bergstrom

and Riedl

, Grouplens: An open architecture for collaborative filtering of netnews, In Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work, CSCW ’94, New York, NY, USA, 1994. ACM, pp. 175–186.

31.

Sarwar

Karypis

Konstan

and Riedl

, Item-based collaborative filtering recommendation algorithms, In Proceedings of the 10th International Conference on World Wide Web, WWW ’01, New York, NY, USA, 2001. ACM, pp 285–295.

32.

Shani

and Gunawardana

, Evaluating recommendation systems, Recommender Systems Handbook, 2011, pp. 257–297.

33.

Song

, A collaborative filtering recommendation algorithm based on multi-dimensional da ta filling, In 2016 2nd IEEE International Conference on Computer and Communications, 2016.

34.

Wang

and Xu

, Trust-aware recommender system based on expert user, Computer Science, 2011.

35.

Wang

and Yin

, Real-time personalized recommendation based on implicit user feedback data stream, Chinese Journal of Computers39 (2016).

36.

Wei

Chen

Zhou

and Tang

, Collaborative filtering and deep learning based hybrid recommendation for cold start problem, In 2016 IEEE 14th Intl Conf on Dependable, Autonomic and Secure Computing, 14th Intl Conf on Pervasive Intelligence and Computing, 2nd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress, 2016.

37.

Zhang

Wang

Liu

and Kong

, Exploiting trust and usage context for cross-domain recommendation,IEEE Access4 (2016), 2398–2407.

38.

Yang

Hao

Min

Kim

and Yau

, An efficient approach to generating location-sensitive recommendations in ad-hoc social network environments, IEEE Transactions on Services Computing8(3) (2015), 520–533.

39.

Yang

Guo

Liu

and Steck

, A survey of collaborative filtering based social recommender systems, Computer Communications41 (2014), 1–10.

40.

Yang

Liang

and Zhao

, Collaborative filtering-based recommendation of online social voting, IEEE Transactions on Computational Social Systems (2017).

41.

Yang

and Zheng

, A survey of collaborative filtering-based recommender systems for mobile internet applications, IEEE Access, 2016.

42.

Yao

Sheng

Q.Z.

Ngu

A.H.H.

and Segev

, Unified collaborative and content-based web service recommendation, IEEE Transactions on Services Computing8(3) (2015), 453–466.

43.

Yongping

D.U.

Xiaoyan

D.U.

and Huang

, Improve the collaborative filtering recommender system performance by trust network construction, Chinese Journal of Electronics25(3) (2016), 418–423.

44.

Zhang

and Xu

, Study on the trust evaluation approach based on cloud model, Chinese Journal of Computers36(2) (2013), 422–431.

45.

Zheng

Zhu

and Yao

, Recommendation rating predicition based on attribute boosting with partial sampling, Chinese Journal of Computers (2016).