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2021 | OriginalPaper | Buchkapitel

TAP: A Two-Level Trust and Personality-Aware Recommender System

verfasst von : Shahpar Yakhchi, Seyed Mohssen Ghafari, Mehmet Orgun

Erschienen in: Service-Oriented Computing – ICSOC 2020 Workshops

Verlag: Springer International Publishing

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Abstract

Recommender systems (RSs) have been adopted in a variety set of web services to provide a list of items which a user may interact with in near future. Collaborative filtering (CF) is one of the most widely used mechanism in RSs that focuses on preferences of neighbours of similar users. Therefore, it is a critical challenge for CF models to discover a set of appropriate neighbors for a particular user. Most of the current approaches exploit users’ ratings information to find similar users by comparing their rating patterns. However, this may be a simple idea and over-tested by the current studies, which may fail under data sparsity problem. Recommender system as an intelligent system needs to help users with their decision making process, and facilitate them with personalized suggestions. In real world, people are willing to share similar interest with those who have the same personality type; and then among all similar personality users pope may only take advice and recommendation from the trustworthy ones. Therefore, in this paper we propose a two-level model, TAP, which analyzes users’ behaviours to first detect their personality types, and then incorporate trust information to provide more customized recommendations. We mathematically model our approach based on the matrix factorization to consider personality and trust information simultaneously. Experimental results on a real-world dataset demonstrate the effectiveness of our model.

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Vorheriges Kapitel Adaptive Summaries: A Personalized Concept-Based Summarization Approach by Learning from Users’ Feedback

Nächstes Kapitel Am I Rare? an Intelligent Summarization Approach for Identifying Hidden Anomalies

https://www.netflix.com/.

https://www.spotify.com/.

https://www.amazon.com/.

https://www.ebay.com/.

https://www.facebook.com/.

https://twitter.com/.

https://reviews.history.ac.uk/review/287a.

Abdullah, N.A., Nishioka, D., Tanaka, Y., Murayama, Y.: Why i retweet? exploring user’s perspective on decision-making of information spreading during disasters. In: HICSS, pp. 1–10 (2017)

Adomavicius, G., Kwon, Y.: Improving aggregate recommendation diversity using ranking-based techniques. IEEE Trans. Knowl. Data Eng. 24(5), 896–911 (2012)CrossRef

Altman, I., Taylor, D.: Social Penetration: The Development of Interpersonal Relationships. Holt, Rinehart & Winston (1973)

Azaria, A., Hong, J.: Recommender systems with personality. In: Sen, S., Geyer, W., Freyne, J., Castells, P. (eds.) Proceedings of the 10th ACM Conference on Recommender Systems, Boston, MA, USA, September 15–19, 2016, pp. 207–210. ACM (2016)

Beheshti, A., Hashemi, V.M., Yakhchi, S., Motahari-Nezhad, H.R., Ghafari, S.M., Yang, J.: personality2vec: enabling the analysis of behavioral disorders in social networks. In: Caverlee, J., Hu, X.B., Lalmas, M., Wang, W. (eds.) Conf. on WSDM, USA, pp. 825–828. ACM (2020)

Beheshti, A., Yakhchi, S., Mousaeirad, S., Ghafari, S.M., Goluguri, S.R., Edrisi, M.A.: Towards cognitive recommender systems. Algorithms 13(8), 176 (2020)CrossRef

Bild, D.R., Liu, Y., Dick, R.P., Mao, Z.M., Wallach, D.S.: Aggregate characterization of user behavior in twitter and analysis of the retweet graph. ACM Trans. Internet Technol. 15(1), 1–24 (2015)CrossRef

Braunhofer, M., Elahi, M., Ricci, F.: User personality and the new user problem in a context-aware point of interest recommender System. In: Tussyadiah, I., Inversini, A. (eds.) Inf. Commun. Technol. Tourism 2015, pp. 537–549. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-14343-9_39CrossRef

Brynjolfsson, E., Hu, Y.J., Simester, D.: Goodbye pareto principle, hello long tail: the effect of search costs on the concentration of product sales. Manag. Sci. 57(8), 1373–1386 (2011)CrossRef

10.

Burger, J.: Introduction to Personality. Cengage Learning (2011)

11.

Costa, P. T.,M.R.R.: Domains and facets: hierarchical personality assessment using the revised neo personality inventory. J. Pers. Assess. 64(1), 21–50 (1995)

12.

Funder., D.: Personality. Ann. Rev. Psychol. 52, 197–221 (2001)

13.

Ghafari, S.M., et al.: A survey on trust prediction in online social networks. IEEE Access 8, 144292–144309 (2020)

14.

Ghafari, S.M., Beheshti, A., Yakhchi, S., Orgun, M.: Social context-aware trust prediction: a method for identifying fake news. In: Conference on WISE, pp. 161–177 (2018)

15.

Ghafari, S.M.: Towards time-aware context-aware deep trust prediction in online social networks. arXiv preprint arXiv:2003.09543 (2020)

16.

Ghafari, S.M., Joshi, A., Beheshti, A., Paris, C., Yakhchi, S., Orgun, M.A.: DCAT: a deep context-aware trust prediction approach for online social networks. In: Haghighi, P.D., Salvadori, I.L., Steinbauer, M., Khalil, I., Anderst-Kotsis, G. (eds.) Conference on MoMM, pp. 20–27. ACM (2019)

17.

Ghafari, S.M., Yakhchi, S., Beheshti, A., Orgun, M.: SETTRUST: social exchange theory based context-aware trust prediction in online social networks. In: Hacid, H., Sheng, Q.Z., Yoshida, T., Sarkheyli, A., Zhou, R. (eds.) QUAT 2018. LNCS, vol. 11235, pp. 46–61. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-19143-6_4CrossRef

18.

Ghafari, S.M., Yakhchi, S., Beheshti, A., Orgun, M.: Social context-aware trust prediction: methods for identifying fake news. In: Hacid, H., Cellary, W., Wang, H., Paik, H.-Y., Zhou, R. (eds.) WISE 2018. LNCS, vol. 11233, pp. 161–177. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-02922-7_11CrossRef

19.

Golbeck, J.: Generating predictive movie recommendations from trust in social networks. In: Trust Management, 4th International Conference, iTrust, Italy, pp. 93–104 (2006)

20.

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

21.

Guo, G., Zhang, J., Yorke-Smith, N.: Leveraging multiviews of trust and similarity to enhance clustering-based recommender systems. Knowl. Based Syst. 74, 14–27 (2015)CrossRef

22.

Guo, G., Zhang, J., Yorke-Smith, N.: Trustsvd: collaborative filtering with both the explicit and implicit influence of user trust and of item ratings. In: Bonet, B., Koenig, S. (eds.) Conference on AAAI, USA. pp. 123–129. AAAI Press (2015)

23.

He, X., Zhang, H., Kan, M., Chua, T.: Fast matrix factorization for online recommendation with implicit feedback. In: Conference on SIGIR, Italy, pp. 549–558 (2016)

24.

Hu, R., Pu, P.: A study on user perception of personality-based recommender systems. In: De Bra, P., Kobsa, A., Chin, D. (eds.) UMAP 2010. LNCS, vol. 6075, pp. 291–302. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13470-8_27CrossRef

25.

Hu, R., Pu, P.: Enhancing collaborative filtering systems with personality information. In: Mobasher, B., Burke, R.D., Jannach, D., Adomavicius, G. (eds.) Proceedings of the 2011 ACM Conference on Recommender Systems, RecSys 2011, Chicago, IL, USA, October 23–27, 2011, pp. 197–204. ACM (2011)

26.

Hu, Y., Lee, P., Chen, K., Tarn, J.M., Dang, D.: Hotel recommendation system based on review and context information: a collaborative filtering appro. In: Conference on PACIS, Taiwan, p. 221 (2016)

27.

Hurley, N., Zhang, M.: Novelty and diversity in top-n recommendation-analysis and evaluation. ACM Trans. Internet Techn. 10(4), 14:1–14:30 (2011)

28.

Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Conference on SIGKDD, USA, pp. 426–434 (2008)

29.

Kosinski, M., Stillwell, D., Graepel, T.: Private traits and attributes are predictable from digital records of human behavior. Proc. National Acad. Sci. 110, 5802–5805 (2013)CrossRef

30.

Lika, B., Kolomvatsos, K., Hadjiefthymiades, S.: Facing the cold start problem in recommender systems. Expert Syst. Appl. 41, 2065–2073 (2014)CrossRef

31.

Luhmann, N.: Trust and power. Wiley, Chichester (1979)

32.

Ma, X., Lu, H., Gan, Z.: Implicit trust and distrust prediction for recommender systems. In: Conference on WISE, USA, pp. 185–199 (2015)

33.

Mairesse, F., Walker, M.A., Mehl, M.R., Moore, R.K.: Using linguistic cues for the automatic recognition of personality in conversation and text. J. Artif. Intell. Res. 30, 457–500 (2007)CrossRef

34.

Martínez-Cruz, C., Porcel, C., Bernabé-Moreno, J., Herrera-Viedma, E.: A model to represent users trust in recommender systems using ontologies and fuzzy linguistic modeling. Inf. Sci. 311, 102–118 (2015)CrossRef

35.

Mayer, R.C.: An integrative model of organizational trust. Acad. Manag. Rev. 20(3), 709–734 (1995)CrossRef

36.

McCrae, R.: The five-factor model of personality traits: consensus and controversy. In: The Cambridge Handbook of Personality Psychology, pp. 148–161

37.

McNee, S.M., Riedl, J., Konstan, J.A.: Being accurate is not enough: how accuracy metrics have hurt recommender systems. In: Olson, G.M., Jeffries, R. (eds.) Con. on CHI, Canada, pp. 1097–1101. ACM (2006)

38.

Moradi, P., Ahmadian, S.: A reliability-based recommendation method to improve trust-aware recommender systems. Expert Syst. Appl. 42(21), 7386–7398 (2015)CrossRef

39.

Nunes, M.A.S.N., Hu, R.: Personality-based recommender systems: an overview. In: Cunningham, P., Hurley, N.J., Guy, I., Anand, S.S. (eds.) Conference on RecSys, Ireland, pp. 5–6. ACM (2012)

40.

Paiva, F.A.P., Costa, J.A.F., Silva, C.R.M.: A personality-based recommender system for semantic searches in vehicles sales portals. In: Martínez de Pisón, F.J., Urraca, R., Quintián, H., Corchado, E. (eds.) HAIS 2017. LNCS (LNAI), vol. 10334, pp. 600–612. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59650-1_51CrossRef

41.

Peter J. Rentfrow, Samuel D. Gosling, e.a.: The do re mi’s of everyday life: the structure and personality correlates of music preferences. J. Personal. Soc. Psychol. 84(6), 1236 (2003)

42.

Recio-García, J.A., Jiménez-Díaz, G., Sánchez-Ruiz-Granados, A.A., Díaz-Agudo, B.: Personality aware recommendations to groups. In: Conference on RecSys, USA, pp. 325–328. ACM (2009)

43.

Roshchina, A., Cardiff, J., Rosso, P.: TWIN: personality-based intelligent recommender system. J. Intell. Fuzzy Syst. 28(5), 2059–2071 (2015)CrossRef

44.

Schwartz, H.A., et al.: Toward personality insights from language exploration in social media. In: Analyzing Microtext, Papers from the 2013 AAAI Spring Symposium, Palo Alto, California, USA, March 25–27, 2013. AAAI Technical Report, vol. SS-13-01. AAAI (2013)

45.

Tang, J., Gao, H., Hu, X., Liu, H.: Exploiting homophily effect for trust prediction. In: Conference on WSDM, Italy, pp. 53–62 (2013)

46.

Tang, J., Liu, H.: Trust in social media. Morgan and Claypool Publishers, San Rafael, California (2015)

47.

Vinciarelli, A., Mohammadi, G.: A survey of personality computing. IEEE Trans. Affective Comput. 5(3), 273–291 (2014)CrossRef

48.

Wang, C., Blei, D.M.: Collaborative topic modeling for recommending scientific articles. In: Confereence on SIGKDD, USA, pp. 448–456 (2011)

49.

Wang, J., de Vries, A.P., Reinders, M.J.T.: Unifying user-based and item-based collaborative filtering approaches by similarity fusion. In: SIGIR 2006: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Seattle, Washington, USA, August 6–11, 2006, pp. 501–508 (2006)

50.

Xie, F., Chen, Z., Shang, J., Huang, W., Li, J.: Item similarity learning methods for collaborative filtering recommender systems. In: 29th IEEE International Conference on Advanced Information Networking and Applications, AINA 2015, Gwangju, South Korea, March 24–27, 2015, pp. 896–903 (2015)

51.

Xue, H., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: Conference on IJCAI, Australia, pp. 3203–3209 (2017)

52.

Yakhchi, S., Beheshti, A., Ghafari, S.M., Orgun, M.A., Liu, G.: Towards a deep attention-based sequential recommender system. IEEE Access 8, 178073–178084 (2020)CrossRef

53.

Yakhchi, S., Beheshti, A., Ghafari, S.M., Orgun, M.A.: Enabling the analysis of personality aspects in recommender systems. In: Wei, K.K., Huang, W.W., Lee, J.K., Xu, D., Jiang, J.J., Kim, H. (eds.) Conference on PACIS, China, p. 143 (2019)

54.

Yakhchi, S., Ghafari, S.M., Beheshti, A.: CNR: cross-network recommendation embedding user’s personality. In: Hacid, H., Sheng, Q.Z., Yoshida, T., Sarkheyli, A., Zhou, R. (eds.) Data Quality and Trust in Big Data - 5th International Workshop, QUAT 2018, Held in Conjunction with WISE 2018, Dubai, UAE, November 12–15, 2018, Revised Selected Papers. Lecture Notes in Computer Science, vol. 11235, pp. 62–77. Springer (2018)

55.

Yakhchi, S., Ghafari, S.M., Tjortjis, C., Fazeli, M.: Armica-improved: a new approach for association rule mining. In: Li, G., Ge, Y., Zhang, Z., Jin, Z., Blumenstein, M. (eds.) Conference on KSEM, Australia. Lecture Notes in Computer Science, vol. 10412, pp. 296–306. Springer (2017)

56.

Yao, Y., Tong, H., Yan, X., Xu, F., Lu, J.: Matri: a multi-aspect and transitive trust inference model. In: Proceedings of the 22nd International Conference on World Wide Web, pp. 1467–1476 (2013)

57.

Yu, Y., Gao, Y., Wang, H., Wang, R.: Joint user knowledge and matrix factorization for recommender systems. In: Conference on WISE, China, pp. 77–91 (2016)

58.

Zhang, Y., Chen, H., Wu, Z.: A social network-based trust model for the semantic web. In: ATC, pp. 183–192 (2006)

Titel: TAP: A Two-Level Trust and Personality-Aware Recommender System
verfasst von: Shahpar Yakhchi
Seyed Mohssen Ghafari
Mehmet Orgun
Verlag: Springer International Publishing
Buch: Service-Oriented Computing – ICSOC 2020 Workshops
Print ISBN: 978-3-030-76351-0

Electronic ISBN: 978-3-030-76352-7

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-76352-7_30

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