Abstract
With the emergence of personality computing as a new research field related to artificial intelligence and personality psychology, we have witnessed an unprecedented proliferation of personality-aware recommendation systems. Unlike conventional recommendation systems, these new systems solve traditional problems such as the cold start and data sparsity problems. This survey aims to study and systematically classify personality-aware recommendation systems. To the best of our knowledge, this survey is the first that focuses on personality-aware recommendation systems. We explore the different design choices of personality-aware recommendation systems, by comparing their personality modeling methods, as well as their recommendation techniques. Furthermore, we present the commonly used datasets and point out some of the challenges of personality-aware recommendation systems.
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Abbasi AZ, Ting DH, Hlavacs H, Wilson B, Rehman U, Arsalan A (2020) Personality differences between videogame vs. non-videogame consumers using the HEXACO model. Current Psychol. https://doi.org/10.1007/s12144-020-00793-2
Adamopoulos P, Todri V (2015) Personality-based recommendations: evidence from amazon. com. In: RecSys Posters. Springer
ADS dataset. https://www.kaggle.com/groffo/ads16-dataset
Aguiar JJB, Fechine JM, de Barros Costa E (2020) Collaborative filtering strategy for product recommendation using personality characteristics of customers. In: Proceedings of the Brazilian symposium on multimedia and the web, pp 157–164. ACM, New York, NY, USA. https://doi.org/10.1145/3428658.3430969
Aluja A, Garcıa O, Rossier J, Garcıa LF (2005) Comparison of the NEO-FFI, the NEO-FFI-R and an alternative short version of the NEO-PI-R (NEO-60) in Swiss and Spanish samples. Personal Indiv Diffe 38(3), 591–604. https://doi.org/10.1016/j.paid.2004.05.014.https://linkinghub.elsevier.com/retrieve/pii/S0191886904001394
Alves P, Saraiva P, Carneiro J, Campos P, Martins H, Novais P, Marreiros G (2020) Modeling tourists’ personality in recommender systems: How does personality influence preferences for tourist attractions? In: Proceedings of the 28th ACM conference on user modeling, adaptation and personalization, pp 4–13. ACM, New York, NY, USA. https://doi.org/10.1145/3340631.3394843
Annalyn N, Bos MW, Sigal L, Li B (2018) Predicting personality from book preferences with user-generated content labels. IEEE Trans Affect Comput pp 1–1. https://doi.org/10.1109/TAFFC.2018.2808349. http://ieeexplore.ieee.org/document/8301566/
Asabere N.Y, Acakpovi A (2020) ROPPSA : TV program recommendation based on personality and social awareness. Math Prob Eng 2020, pp 1–15. https://doi.org/10.1155/2020/1971286.https://www.hindawi.com/journals/mpe/2020/1971286/
Asabere NY, Acakpovi A, Michael MB (2018) Improving socially-aware recommendation accuracy through personality. IEEE Trans Affect Comput 9(3), 351–361. https://doi.org/10.1109/TAFFC.2017.2695605.https://ieeexplore.ieee.org/document/7904698/
Ashton MC, Lee K, De Vries RE (2014) The hexaco honesty-humility, agreeableness, and emotionality factors: a review of research and theory. Person Soc Psychol Rev 18(2):139–152
Azucar D, Marengo D, Settanni M (2018) Predicting the Big 5 personality traits from digital footprints on social media: a meta-analysis. Personal Indiv Diff 124:150–159. https://doi.org/10.1016/j.paid.2017.12.018.https://linkinghub.elsevier.com/retrieve/pii/S0191886917307328
Balakrishnan V, Arabi H (2018) HyPeRM: A hybrid personality-aware recommender for movie. Malaysian J Comput Sci 31(1):48–62. https://doi.org/10.22452/mjcs.vol31no1.4. https://ejournal.um.edu.my/index.php/MJCS/article/view/10568
Bansal J, Flannery MB, Woolhouse MH (2020) Influence of personality on music-genre exclusivity. Psychol Music, p 030573562095361. https://doi.org/10.1177/0305735620953611
Berkovsky S, Taib R, Conway D (2017) How to recommend? User trust factors in movie recommender systems. In: Proceedings of the 22nd international conference on intelligent user interfaces, pp 287–300
Bhavya S, Pillai AS, Guazzaroni G (2020) Personality identification from social media using deep learning: a review. Soft Comput Prob Solv , pp 523–534. Springer. https://doi.org/10.1007/978-981-15-0184-5_45
Bian L, Holtzman,H (2011)Online friend recommendation through personality matching and collaborative filtering. Proceedings of UBICOMM, pp 230–235
Bolock AE, Kady AE, Herbert C, Abdennadher S (2020) Towards a character-based meta recommender for movies. In: Alfred R, Lim Y, Haviluddin H, On CK (eds) Computational science and technology. Springer Singapore, Singapore, pp 627–638
Boyle GJ (1995) Myers-briggs type indicator (mbti): some psychometric limitations. Aust Psychol 30(1):71–74
Braunhofer M, Elahi M, Ricci F (2014a) Usability assessment of a context-aware and personality-based mobile recommender system. In: Hepp M, Hoffner Y (eds) E-commerce and web technologies. Springer International Publishing, Cham, pp 77–88
Braunhofer M, Elahi M, Ge M, Ricci F (2014b) Context dependent preference acquisition with personality-based active learning in mobile recommender systems. In: P. Zaphiris, A. Ioannou (eds.) Learning and collaboration technologies. Technology-Rich Environments for Learning and Collaboration, pp 105–116. Springer International Publishing, Cham
Braunhofer M, Elahi M, Ricci F (2015) User personality and the new user problem in a context-aware point of interest recommender system. In: Information and communication technologies in tourism 2015, pp 537–549. Springer International Publishing, Cham. https://doi.org/10.1007/978-3-319-14343-9_39
Buettner R (2017) Predicting user behavior in electronic markets based on personality-mining in large online social networks. Electron Markets 27(3):247–265. https://doi.org/10.1007/s12525-016-0228-z
Cai X, Ning H, Dhelim S, Zhou R, Zhang T, Xu Y, Wan Y (2020) Robot and its living space: a roadmap for robot development based on the view of living space. Digital Commun Netw.https://doi.org/10.1016/j.dcan.2020.12.001.https://www.sciencedirect.com/science/article/pii/S2352864820302881
Cantador I, Fernández-Tob\(\backslash\)’\(\backslash\)ias I, Bellog\(\backslash\)’\(\backslash\)in A (2013) Relating personality types with user preferences in multiple entertainment domains. In: CEUR workshop proceedings. Shlomo Berkovsky
Chakrabarty N, Chowdhury S, Kanni SD, Mukherjee S (2020) FAFinder: friend suggestion system for social networking. In: Hemanth DJ, Shakya S, Baig Z (eds) Intelligent data communication technologies and internet of things. Springer International Publishing, Cham, pp 51–58
Chan G, Arya A, Whitehead A (2018) Keeping players engaged in exergames: a personality matchmaking approach. In: Extended abstracts of the 2018 CHI conference on human factors in computing systems, pp 1–6. ACM, New York, NY, USA. https://doi.org/10.1145/3170427.3188455
Cheng R, Tang B (2016) A music recommendation system based on acoustic features and user personalities. In: Cao H, Li J, Wang R (eds) Trends and applications in knowledge discovery and data mining. Springer International Publishing, Cham, pp 203–213
Costa Jr PT, McCrae RR (2008) The revised NEO personality inventory (NEO-PI-R). Sage Publications, Inc
Dandannavar PS, Mangalwede SR, Kulkarni PM (2018) Social media text—a source for personality prediction. In: 2018 international conference on computational techniques, electronics and mechanical systems (CTEMS), pp 62–65. IEEE..
Darliansyah A, Naeem MA, Mirza F, Pears R (2019) SENTIPEDE: a smart system for sentiment-based personality detection from short texts. J Univ Comput Sci 25(10):1323–1352
de Lima ES, Feijó B, Furtado AL (2018) Player behavior and personality modeling for interactive storytelling in games. Entertain Comput 28:32–48. https://doi.org/10.1016/j.entcom.2018.08.003.https://linkinghub.elsevier.com/retrieve/pii/S1875952118300120
Dhelim S, Ning H, Farha F, Chen L, Atzori L, Daneshmand M (2021) Iot-enabled social relationships meet artificial social intelligence. IEEE Internet Things J. https://doi.org/10.1109/JIOT.2021.3081556
Dhelim S, Aung N, Ning H (2020) Mining user interest based on personality-aware hybrid filtering in social networks. Knowl Based Syst, p 106227.
Dhelim S, Ning H, Aung N (2021) Compath: user interest mining in heterogeneous signed social networks for internet of people. IEEE Internet Things J 8(8):7024–7035.
Dhelim S, Ning H, Aung N, Huang R, Ma J (2020) Personality-aware product recommendation system based on user interests mining and metapath discovery. IEEE Trans Comput Soc Syst pp 1–13.
Dhelim S, Ning H, Bouras M.A, Ma J (2018) Cyber-enabled human-centric smart home architecture. In: 2018 IEEE smartworld, ubiquitous intelligence and computing, advanced and trusted computing, scalable computing and communications, cloud and big data computing, internet of people and smart city innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), IEEE, pp 1880–1886
Dhelim S, Ning H, Zhu T (2016) Stlf: spatial-temporal-logical knowledge representation and object mapping framework. In: 2016 IEEE international conference on systems, man, and cybernetics (SMC), IEEE, pp 001550–001554
dos Santos WR, Ramos RMS, Paraboni I (2020) Computational personality recognition from Facebook text: psycholinguistic features, words and facets. New Rev Hypermedia Multimedia 25(4):268–287. https://doi.org/10.1080/13614568.2020.1722761.
Elahi M, Braunhofer M, Ricci F, Tkalcic M (2013) Personality-based active learning for collaborative filtering recommender systems. In: Congress Ital Assoc Artif Intell , pp 360–371. Springer. https://doi.org/10.1007/978-3-319-03524-6_31
Feng H, Qian X (2013) Recommendation via user’s personality and social contextual. In: Proceedings of the 22nd ACM international conference on conference on information and knowledge management—CIKM ’13, pp 1521–1524. ACM Press, New York, New York, USA. https://doi.org/10.1145/2505515.2507834.
Fernández-Tobías I, Cantador I (2015) On the use of cross-domain user preferences and personality traits in collaborative filtering. In: Ricci F, Bontcheva K, Conlan O, Lawless S (eds) User modeling, adaptation and personalization. Springer International Publishing, Cham, pp 343–349
Fernández-Tobías I, Braunhofer M, Elahi M, Ricci F, Cantador I (2016) Alleviating the new user problem in collaborative filtering by exploiting personality information. User Model User Adapt Interact 26(2–3):221–255. https://doi.org/10.1007/s11257-016-9172-z
Fernández-Tobías I, Cantador I (2014) Personality-aware collaborative filtering: an empirical study in multiple domains with facebook data. In: International conference on electronic commerce and web technologies, pp 125–137. Springer. https://doi.org/10.1007/978-3-319-10491-1_13
Ferwerda B, Graus M.P, Vall A, Tkalcic M, Schedl M (2016) The influence of user's personality traits on satisfaction and attractiveness of diversified recommendation lists. In: Empire RecSys. CEUR-WS
Ferwerda B, Schedl M (2014) Enhancing music recommender systems with personality information and emotional states: a proposal. In: Umap workshops
Ferwerda B, Schedl M (2016) Personality-based user modeling for music recommender systems. Joint Eur Conf Mach Learn Knowl Dis Databases, pp 254–257. Springer
Ferwerda B, Schedl M, Tkalcic M (2015) Predicting personality traits with instagram pictures. In: Proceedings of the 3rd workshop on emotions and personality in personalized systems 2015—EMPIRE ’15, pp 7–10. ACM Press, New York, New York, USA. https://doi.org/10.1145/2809643.2809644.
Ferwerda B, Tkalcic M, Schedl M (2017) Personality traits and music genre preferences: how music taste varies over age groups. In: Proceedings of the 1st workshop on temporal reasoning in recommender systems (RecTemp) at the 11th ACM conference on recommender systems, Como. 31 August 2017
Ferwerda B, Tkalcic M, Schedl M (2017) Personality traits and music genres: What do people prefer to listen to? In: Proceedings of the 25th conference on user modeling, adaptation and personalization, pp 285–288. ACM, New York, NY, USA. https://doi.org/10.1145/3079628.3079693
Finnerty AN, Lepri B, Pianesi F (2016) Acquisition of personality, pp 81–99. Springer International Publishing, Cham. https://doi.org/10.1007/978-3-319-31413-6_5
Fong K, Mar RA (2015) What does my avatar say about me? Inferring personality from avatars. Personal Soc Psychol Bull 41(2):237–249. https://doi.org/10.1177/0146167214562761
Gelli F, He X, Chen T, Chua TS (2017) How personality affects our likes: towards a better understanding of actionable images. In: Proceedings of the 2017 ACM on multimedia conference—MM ’17, pp 1828–1837. ACM Press, New York, New York, USA. https://doi.org/10.1145/3123266.3127909.
Golbeck J, Norris E (2013) Personality, movie preferences, and recommendations. In: Proceedings of the 2013 IEEE/ACM international conference on advances in social networks analysis and mining—ASONAM ’13, pp 1414–1415. ACM Press, New York, New York, USA. https://doi.org/10.1145/2492517.2492572.
Goldberg LR (1990) An alternative “description of personality”’: the big-five factor structure. J Personal Social Psychol 59(6):1216
Gosling SD, Rentfrow PJ, Swann WB (2003) A very brief measure of the big-five personality domains. J Res Personal 37(6):504–528. https://doi.org/10.1016/S0092-6566(03)00046-1.https://linkinghub.elsevier.com/retrieve/pii/S0092656603000461
Guntuku SC, Roy S, Weisi L (2015) Personality modeling based image recommendation. In: He X, Luo S, Tao D, Xu C, Yang J, Hasan MA (eds) MultiMedia modeling. Springer International Publishing, Cham, pp 171–182
Guntuku SC, Lin W, Scott MJ, Ghinea G (2015) Modelling the influence of personality and culture on affect and enjoyment in multimedia. In: 2015 International conference on affective computing and intelligent interaction (ACII), IEEE, pp 236–242. https://doi.org/10.1109/ACII.2015.7344577. http://ieeexplore.ieee.org/document/7344577/
Guntuku SC, Zhou JT, Roy S, Lin W, Tsang IW (2018) ‘Who Likes What and Why?’ Insights into modeling user's personality based on image ‘likes’. IEEE Trans Affect Comput 9(1):130–143. https://doi.org/10.1109/TAFFC.2016.2581168.http://ieeexplore.ieee.org/document/7491295/
Gupta S, Gulati P, Bhatia S, Madaan R (2020) An automatic approach to music recommendations based on individual personality traits. SSRN Electron J. https://doi.org/10.2139/ssrn.3565276.https://www.ssrn.com/abstract=3565276
Han S, Huang H, Tang Y (2020) Knowledge of words: an interpretable approach for personality recognition from social media. Knowl Based Syst 194:105550. https://doi.org/10.1016/j.knosys.2020.105550.https://linkinghub.elsevier.com/retrieve/pii/S0950705120300459
Hariadi AI, Nurjanah D (2017) Hybrid attribute and personality based recommender system for book recommendation. In: 2017 International conference on data and software engineering (ICoDSE), pp 1–5. IEEE. https://doi.org/10.1109/ICODSE.2017.8285874. http://ieeexplore.ieee.org/document/8285874/
Harper FM, Konstan JA (2016) The MovieLens datasets. ACM Trans Interact Intell Syst 5(4):1–19. https://doi.org/10.1145/2827872
He ZW, Zhang L, Liu FY (2020) DiscoStyle: multi-level logistic ranking for personalized image style preference inference. Int J Auto Comput 17(5):637–651. https://doi.org/10.1007/s11633-020-1244-1
Hinds J, Williams EJ, Joinson AN (2020) “It wouldn’t happen to me”: privacy concerns and perspectives following the Cambridge Analytica scandal. Int J Human Comput Stud 143:102498. https://doi.org/10.1016/j.ijhcs.2020.102498.https://linkinghub.elsevier.com/retrieve/pii/S1071581920301002
Hirsh JB, Peterson JB (2009) Personality and language use in self-narratives. J Res Personal 43(3):524–527. https://doi.org/10.1016/j.jrp.2009.01.006.https://linkinghub.elsevier.com/retrieve/pii/S0092656609000439
Hu R (2010) Design and user issues in personality-based recommender systems. In: Proceedings of the fourth ACM conference on recommender systems—RecSys ’10, p 357. ACM Press, New York, New York, USA. https://doi.org/10.1145/1864708.1864790. http://portal.acm.org/citation.cfm?doid=1864708.1864790
Huang Y, Liu H, Li W, Wang Z, Hu X, Wang W (2020) Lifestyles in Amazon: evidence from online reviews enhanced recommender system. Int J Mark Res 62(6):689–706. https://doi.org/10.1177/1470785319844146
Hu R, Pu P (2010) A study on user perception of personality-based recommender systems. In: International conference on user modeling, adaptation, and personalization, pp 291–302. Springer. https://doi.org/10.1007/978-3-642-13470-8_27
Hu R, Pu P (2010) Using personality information in collaborative filtering for new users. In: Proceedings of the fourth ACM conference on Recommender systems—RecSys ’10, pp 23–30. ACM Press. https://doi.org/10.1145/1864708.1864798
Hu R, Pu P (2011) Enhancing collaborative filtering systems with personality information. In: Proceedings of the fifth ACM conference on Recommender systems—RecSys ’11, p 197. ACM Press, New York, New York, USA. https://doi.org/10.1145/2043932.2043969.
Hu R, Pu P (2014) Exploring personality’s effect on user's rating behavior. In: Proceedings of the extended abstracts of the 32nd annual ACM conference on human factors in computing systems—CHI EA ’14, pp 2599–2604. ACM Press, New York, New York, USA. https://doi.org/10.1145/2559206.2581317.
IMDb api. https://developer.imdb.com/
Jeong CS, Lee JY, Jung KD (2020) Adaptive recommendation system for tourism by personality type using deep learning. Int J Internet Broadcast Commun 12(1):55–60
Karumur RP, Konstan JA (2016) Relating newcomer personality to survival and activity in recommender systems. In: Proceedings of the 2016 conference on user modeling adaptation and personalization—UMAP ’16, pp 195–205. ACM Press, New York, New York, USA. https://doi.org/10.1145/2930238.2930246.
Karumur RP, Nguyen TT, Konstan JA (2016) Exploring the value of personality in predicting rating behaviors. In: Proceedings of the 10th ACM conference on recommender systems, pp 139–142. ACM, New York, NY, USA. https://doi.org/10.1145/2959100.2959140
Karumur R.P, Nguyen T.T, Konstan J.A (2018) Personality, user preferences and behavior in recommender systems. Inf Syst Front 20(6):1241–1265. https://doi.org/10.1007/s10796-017-9800-0.http://link.springer.com/10.1007/s10796-017-9800-0
Kaushal V, Patwardhan M (2018) Emerging trends in personality identification using online social networks—a literature survey. ACM Trans Knowl Dis Data 12(2):1–30. https://doi.org/10.1145/3070645
Kedar SV, Bormane DS (2015) Automatic personality assessment: a systematic review. In: 2015 International conference on information processing (ICIP), IEEE, pp 326–331. https://doi.org/10.1109/INFOP.2015.7489402. http://ieeexplore.ieee.org/document/7489402/
Khan EM, Mukta MSH, Ali ME, Mahmud J (2020) Predicting user's movie preference and rating behavior from personality and values. ACM Trans Interact Intell Syst 10(3):1–25. https://doi.org/10.1145/3338244
Khelloufi A, Ning H, Dhelim S, Qiu T, Ma J, Huang R, Atzori L (2021) A social-relationships-based service recommendation system for siot devices. IEEE Internet Things J 8(3):1859–1870. https://doi.org/10.1109/JIOT.2020.3016659
Khodabandehlou S, Hashemi Golpayegani SA, Zivari Rahman M (2020) An effective recommender system based on personality traits, demographics and behavior of customers in time context. Data Technol Appl. (ahead-of-print). https://doi.org/10.1108/DTA-04-2020-0094. https://www.emerald.com/insight/content/doi/10.1108/DTA-04-2020-0094/full/html
Kim JH, Kim Y (2019) Instagram user characteristics and the color of their photos: colorfulness, color diversity, and color harmony. Inf Process Manag 56(4):1494–1505. https://doi.org/10.1016/j.ipm.2018.10.018.https://linkinghub.elsevier.com/retrieve/pii/S0306457318304394
Kim Y, Kim JH (2018) Using computer vision techniques on Instagram to link user's personalities and genders to the features of their photos: an exploratory study. Inf Process Manag 54(6):1101–1114. https://doi.org/10.1016/j.ipm.2018.07.005.https://linkinghub.elsevier.com/retrieve/pii/S0306457317309081
Kleć M (2017) The influence of listener personality on music choices. Comput Sci 18
Kouki P, Schaffer J, Pujara J, O’Donovan J, Getoor L (2020) Generating and understanding personalized explanations in hybrid recommender systems. ACM Trans Interact Intell Syst 10(4):1–40. https://doi.org/10.1145/3365843
Last.fm dataset. http://millionsongdataset.com/lastfm/
Li Bian, Holtzman H, Tuan Huynh Montpetit M.J (2012) MatchMaker: a friend recommendation system through TV character matching. In: 2012 IEEE Consumer communications and networking conference (CCNC), IEEE, pp. 714–718. https://doi.org/10.1109/CCNC.2012.6180983. http://ieeexplore.ieee.org/document/6180983/
Liu R, Hu X (2020) A multimodal music recommendation system with listeners’ personality and physiological signals. In: Proceedings of the ACM/IEEE joint conference on digital libraries in 2020, pp 357–360. ACM, New York, NY, USA. https://doi.org/10.1145/3383583.3398623
Li L, Zhu H, Zhao S, Ding G, Jiang H, Tan A (2019) Personality driven multi-task learning for image aesthetic assessment. In: 2019 IEEE international conference on multimedia and expo (ICME), IEEE, pp 430–435. https://doi.org/10.1109/ICME.2019.00081. https://ieeexplore.ieee.org/document/8784759/
Li L, Zhu H, Zhao S, Ding G, Lin W (2020) Personality-assisted multi-task learning for generic and personalized image aesthetics assessment. IEEE Trans Image Process 29:3898–3910. https://doi.org/10.1109/TIP.2020.2968285.https://ieeexplore.ieee.org/document/8970458/
Majumder N, Poria S, Gelbukh A, Cambria E (2017) Deep learning-based document modeling for personality detection from text. IEEE Intell Syst 32(2):74–79.
Mehta Y, Fatehi S, Kazameini A, Stachl C, Cambria E, Eetemadi S (2020) Bottom-up and top-down: predicting personality with psycholinguistic and language model features. In: Proceedings of the international conference of data mining, IEEE
Mehta Y, Majumder N, Gelbukh A, Cambria E (2020) Recent trends in deep learning based personality detection. Artif Intell Rev 53(4):2313–2339. https://doi.org/10.1007/s10462-019-09770-z.http://link.springer.com/10.1007/s10462-019-09770-z
Melchiorre AB, Schedl M (2020) Personality correlates of music audio preferences for modelling music listeners. In: Proceedings of the 28th ACM conference on user modeling, adaptation and personalization, pp 313–317. ACM, New York, NY, USA. https://doi.org/10.1145/3340631.3394874. https://dl.acm.org/doi/10.1145/3340631.3394874
Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. In: 1st International conference on learning representations, ICLR 2013—workshop track proceedings
Moher D, Liberati A, Tetzlaff J, Altman DG et al (2010) Preferred reporting items for systematic reviews and meta-analyses: the prisma statement. Int J Surg 8(5):336–341
Moscato V, Picariello A, Sperli G (2020) An emotional recommender system for music. IEEE Intell Syst, pp 1–1.
Mou Y, Shi C, Shen T, Xu K (2020) A Systematic Review of the personality of robot: mapping its conceptualization, operationalization, contextualization and effects. Int J Human Comput Interact 36(6):591–605.https://doi.org/10.1080/10447318.2019.1663008.https://www.tandfonline.com/doi/full/10.1080/10447318.2019.1663008
MovieLens dataset. https://grouplens.org/datasets/personality-2018/
Mugge R, Govers PC, Schoormans JP (2009) The development and testing of a product personality scale. Des Stud 30(3):287–302. https://doi.org/10.1016/j.destud.2008.10.002.https://linkinghub.elsevier.com/retrieve/pii/S0142694X08000859
Mukta MSH, Ali ME, Mahmud J (2016) Identifying and validating personality traits-based homophilies for an egocentric network. Social Netw Anal Min 6(1):74. https://doi.org/10.1007/s13278-016-0383-4
myPersonality dataset. https://www.psychometrics.cam.ac.uk/productsservices/mypersonality
Nalmpantis O, Tjortjis C (2017) The 50/50 recommender: a method incorporating personality into movie recommender systems. In: Boracchi G, Iliadis L, Jayne C, Likas A (eds) Engineering applications of neural networks. Springer International Publishing, Cham, pp 498–507
Nave G, Minxha J, Greenberg DM, Kosinski M, Stillwell D, Rentfrow J (2018) Musical preferences predict personality: evidence from active listening and facebook likes. Psychol Sci 29(7):1145–1158. https://doi.org/10.1177/0956797618761659.http://journals.sagepub.com/doi/10.1177/0956797618761659
Neehal N, Mottalib MA (2019) Prediction of preferred personality for friend recommendation in social networks using artificial neural network. In: 2019 International conference on electrical, computer and communication engineering (ECCE), IEEE, pp 1–6. https://doi.org/10.1109/ECACE.2019.8679375. https://ieeexplore.ieee.org/document/8679375/
Newsfullness dataset. www.newsfullness.live/dataset
Nguyen TT, Maxwell Harper F, Terveen L, Konstan JA (2018) User personality and user satisfaction with recommender systems. Inf Syst Front 20(6):1173–1189.https://doi.org/10.1007/s10796-017-9782-y.http://link.springer.com/10.1007/s10796-017-9782-y
Ning H, Dhelim S, Bouras MA, Khelloufi A, Ullah A (2018) Cyber-syndrome and its formation, classification, recovery and prevention. IEEE Access 6:35501–35511.
Ning H, Dhelim S, Aung N (2019) PersoNet: Friend recommendation system based on big-five personality traits and hybrid filtering. IEEE Trans Comput Soc Syst pp 1–9.
Odić A, Tkalčič M, Tasič J, Košir A (2013) Personality and social context: impact on emotion induction from movies. In: Empire RecSys. CEUR-WS
Onori M, Micarelli A, Sansonetti G (2016) A comparative analysis of personality-based music recommender systems. In: Empire RecSys, pp 55–59
Pedregon CA, Farley RL, Davis A, Wood JM, Clark RD (2012) Social desirability, personality questionnaires, and the “better than average” effect. Personal Indiv Diff 52(2):213–217.
Polignano M, Narducci F, de Gemmis M, Semeraro G (2021) Towards emotion-aware recommender systems: an affective coherence model based on emotion-driven behaviors. Exp Syst Appl 170:114382
Potash P, Rumshisky A (2016) Recommender system incorporating user personality profile through analysis of written reviews. In: Empire RecSys, pp 60–66
PsychoFlickr dataset. http://vips.sci.univr.it/dataset/psychoflickr/
Qamhieh M, Sammaneh H, Demaidi MN (2020) PCRS: personalized career-path recommender system for engineering students. IEEE Access 8:214039–214049.
Quijano-Sanchez L, Recio-Garcia JA, Diaz-Agudo B (2010) Personality and social trust in group recommendations. In: 2010 22nd IEEE international conference on tools with artificial intelligence, IEE, Epp 121–126.
Quijano-Sanchez L, Recio-Garcia JA, Diaz-Agudo B (2011) HappyMovie: a facebook application for recommending movies to groups. In: 2011 IEEE 23rd international conference on tools with artificial intelligence, IEEE, pp 239–244. https://doi.org/10.1109/ICTAI.2011.44. http://ieeexplore.ieee.org/document/6103334/
Rammstedt B, John OP (2007) Measuring personality in one minute or less: a 10-item short version of the big five inventory in English and German. J Res Personal 41(1):203–212. https://doi.org/10.1016/j.jrp.2006.02.001.https://linkinghub.elsevier.com/retrieve/pii/S0092656606000195
Recio-Garcia JA, Jimenez-Diaz G, Sanchez-Ruiz AA, Diaz-Agudo B (2009) Personality aware recommendations to groups. In: Proceedings of the third ACM conference on recommender systems—RecSys ’09, p 325. ACM Press, New York, New York, USA. https://doi.org/10.1145/1639714.1639779.
Robert L (2018) Personality in the human robot interaction literature: a review and brief critique. In: Proceedings of the 24th Americas conference on information systems, pp 16–18
Robert L, Alahmad R, Esterwood C, Kim S, You S, Zhang Q (2020) A review of personality in human-robot interactions. SSRN Electron J. https://doi.org/10.2139/ssrn.3528496
Roffo G (2016) Towards personality-aware recommendation. arXiv preprint: arXiv:1607.05088
Roffo G, Vinciarelli A (2016) Personality in computational advertising: a benchmark. http://eprints.gla.ac.uk/149660/
Santamaria T, Nathan-Roberts D (2017) Personality measurement and design in human-robot interaction: a systematic and critical review. In: Proceedings of the human factors and ergonomics society annual meeting 61(1):853–857. https://doi.org/10.1177/1541931213601686.http://journals.sagepub.com/doi/10.1177/1541931213601686
Schedl M, Melenhorst M, Liem C.C.S, Martorell A, Mayor Ó, Tkalčič M (2016) A personality-based adaptive system for visualizing classical music performances. In: Proceedings of the 7th international conference on multimedia systems—MMSys ’16, pp 1–7. ACM Press, New York, New York, USA. https://doi.org/10.1145/2910017.2910604.
Scott MJ, Guntuku SC, Lin W, Ghinea G (2016) Do personality and culture influence perceived video quality and enjoyment? IEEE Trans Multimedia 18(9):1796–1807
Scott M.J, Guntuku SC, Lin W, Ghinea G (2016) Do personality and culture influence perceived video quality and enjoyment? IEEE Trans Multimedia 18(9):1796–1807. https://doi.org/10.1109/TMM.2016.2574623.http://ieeexplore.ieee.org/document/7480836/
Segalin C, Perina A, Cristani M, Vinciarelli A (2017) The pictures we like are our image: continuous mapping of favorite pictures into self-assessed and attributed personality traits. IEEE Trans Affect Comput 8(2):268–285. https://doi.org/10.1109/TAFFC.2016.2516994.http://ieeexplore.ieee.org/document/7378902/
Sertkan M, Neidhardt J, Werthner H (2019) What is the “personality” of a tourism destination? Inf Technol Tour 21(1):105–133.https://doi.org/10.1007/s40558-018-0135-6
Shayegan MJ, Valizadeh M (2020) A recommender system based on the analysis of personality traits in telegram social network. http://arxiv.org/abs/2010.00643
Silva B, Paraboni I (2018) Learning personality traits from facebook text. IEEE Latin Am Trans 16(4):1256–1262. https://doi.org/10.1109/TLA.2018.8362165.https://ieeexplore.ieee.org/document/8362165/
Silveira Jacques Junior JC, Gucluturk Y, Perez M, Guclu U, Andujar C, Baro X, Escalante HJ, Guyon I, Van Gerven MAJ, Van Lier R, Escalera S (2019) First impressions: a survey on vision-based apparent personality trait analysis. IEEE Trans Affect Comput. https://doi.org/10.1109/taffc.2019.2930058
Sofia G, Marianna S, George L, Panos K (2016) Investigating the role of personality traits and influence strategies on the persuasive effect of personalized recommendations. In: 4th Workshop on emotions and personality in personalized systems (EMPIRE), p 9
Song M, Namgoong H, Kim HG, Eune J (2009) A proposed movie recommendation method using emotional word selection. In: Ozok AA, Zaphiris P (eds) Online communities and social computing. Springer, Berlin, pp 525–534
Stillwell DJ, Kosinski M (2014) MyPersonality project: example of successful utilization of online social networks for large-scale social research. Am Psychol 59(2):93–104
Sun J, Ren D, Xu D (2018) Leveraging user personality and tag information for one class collaborative filtering. In: Hong R, Cheng WH, Yamasaki T, Wang M, Ngo CW (eds) Advances in multimedia information processing—PCM 2018. Springer International Publishing, Cham, pp 830–840
Sun J, Geng J, Cheng X, Zhu M, Xu Q, Liu Y (2020) Leveraging personality information to improve community recommendation in e-learning platforms. Br J Educ Technol 51(5):1711–1733
Sun X, Liu B, Meng Q, Cao J, Luo J, Yin H (2020) Group-level personality detection based on text generated networks. World Wide Web 23(3):1887–1906. https://doi.org/10.1007/s11280-019-00729-2.http://link.springer.com/10.1007/s11280-019-00729-2
Tadesse MM, Lin H, Xu B, Yang L (2018) Personality predictions based on user behavior on the facebook social media platform. IEEE Access 6:61959–61969.
Tanasescu V, Jones CB, Colombo G, Chorley MJ, Allen SM, Whitaker RM (2013) The personality of venues: places and the five-factors (’Big Five’) model of personality. In: 2013 Fourth international conference on computing for geospatial research and application, IEEE, pp 76–81.
Tausczik Y.R, Pennebaker J.W (2010) The psychological meaning of words: LIWC and computerized text analysis methods. J Lang Soc Psychol 29(1):24–54.
Ting TL, Varathan KD (2018) Job recommendation using Facebook personality scores. Malay J Comput Sci 31(4):311–331. https://doi.org/10.22452/mjcs.vol31no4.5. https://ejournal.um.edu.my/index.php/MJCS/article/view/14244
Tkalcic M, Kunaver M, Tasic J, Košir A (2009) Personality based user similarity measure for a ollaborative recommender system. In: Proceedings of the 5th workshop on emotion in human-computer interaction-real world challenges, pp 30–37
Tommasel A, Corbellini A, Godoy D, Schiaffino S (2015) Exploring the role of personality traits in followee recommendation. Online Inf Rev 39(6):812–830. https://doi.org/10.1108/OIR-04-2015-0107.https://www.emerald.com/insight/content/doi/10.1108/OIR-04-2015-0107/full/html
Tommasel A, Corbellini A, Godoy DL, Schiaffino S (2015) On the role of personality traits in followee recommendation algorithms. In: Argentine symposium on artificial intelligence, pp 105–112. http://sedici.unlp.edu.ar/handle/10915/52104
Tommasel A, Corbellini A, Godoy D, Schiaffino S (2016) Personality-aware followee recommendation algorithms: an empirical analysis. Eng Appl Artif Intell 51:24–36. https://doi.org/10.1016/j.engappai.2016.01.016.https://linkinghub.elsevier.com/retrieve/pii/S0952197616000208
Topolewska E, Skimina E, Strus W, Cieciuch J, Rowiński T (2014) The short IPIP-BFM-20 questionnaire for measuring the Big Five. Roczniki Psychologiczne 17(2):385–402
Twitter api. https://developer.twitter.com
Uddin MF, Banerjee S, Lee J (2016) Recommender system framework for academic choices: personality based recommendation engine (PBRE). In: 2016 IEEE 17th international conference on information reuse and integration (IRI), IEEE, pp 476–483. https://doi.org/10.1109/IRI.2016.70. http://ieeexplore.ieee.org/document/7785779/
Vinciarelli A, Mohammadi G (2014) A survey of personality computing. IEEE Trans Affect Comput 5(3):273–291. https://doi.org/10.1109/TAFFC.2014.2330816.http://ieeexplore.ieee.org/document/6834774/
Vinciarelli A, Mohammadi G (2014) More personality in personality computing. IEEE Trans Affect Comput 5(3):297–300. https://doi.org/10.1109/TAFFC.2014.2341252.http://ieeexplore.ieee.org/document/6866147/
Wang J (2015) A collaborative filtering systems based on personality information. In: Proceedings of the 2015 international industrial informatics and computer engineering conference. Atlantis Press, Paris, France. https://doi.org/10.2991/iiicec-15.2015.163. http://www.atlantis-press.com/php/paper-details.php?id=17049
Wang W, Chen J, Wang J, Chen J, Liu J, Gong Z (2020) Trust-enhanced collaborative filtering for personalized point of interests recommendation. IEEE Trans Ind Inf 16(9):6124–6132.https://doi.org/10.1109/TII.2019.2958696.https://ieeexplore.ieee.org/document/8930072/
Wei XS, Zhang CL, Zhang H, Wu J (2017) Deep bimodal regression of apparent personality traits from short video sequences. IEEE Trans Affect Comput 9(3):303–315
Wright AGC (2014) Current directions in personality science and the potential for advances through computing. IEEE Trans Affect Comput 5(3):292–296. https://doi.org/10.1109/TAFFC.2014.2332331. http://ieeexplore.ieee.org/document/6933966/
Wu W, Chen L (2015) Implicit acquisition of user personality for augmenting movie recommendations. In: Lecture notes in Computer science, pp 302–314. Springer International Publishing. https://doi.org/10.1007/978-3-319-20267-9_25
Wu W, Chen L, He L (2013) Using personality to adjust diversity in recommender systems. In: Proceedings of the 24th ACM conference on hypertext and social media—HT ’13, pp 225–229. ACM Press, New York, New York, USA. https://doi.org/10.1145/2481492.2481521. http://dl.acm.org/citation.cfm?doid=2481492.2481521
Wu W, Chen L, Yang Q, Li Y (2019) Inferring students‘ personality from their communication behavior in web-based learning systems. Int J Artif Intell Educ 29(2):189–216. https://doi.org/10.1007/s40593-018-00173-9.http://link.springer.com/10.1007/s40593-018-00173-9
Wu W, Chen L, Zhao Y (2018)Personalizing recommendation diversity based on user personality. User Model User Adapt Interact 28(3):237–276.https://doi.org/10.1007/s11257-018-9205-x.http://link.springer.com/10.1007/s11257-018-9205-x
Xia F, Asabere NY, Liu H, Chen Z, Wang W (2017) Socially aware conference participant recommendation with personality traits. IEEE Syst J 11(4):2255–2266. https://doi.org/10.1109/JSYST.2014.2342375.http://ieeexplore.ieee.org/document/6877610/
Xiao P, Fan Y, Du Y (2017) A personality-aware followee recommendation model based on text semantics and sentiment analysis. In: National CCF conference on natural language processing and Chinese computing, pp 503–514. Springer. https://doi.org/10.1007/978-3-319-73618-1_42
Yakhchi S, Beheshti A, Ghafari SM, Orgun M (2020) Enabling the analysis of personality aspects in recommender systems. http://arxiv.org/abs/2001.04825
Yang HC, Huang Z (2019) Mining personality traits from social messages for game recommender systems. Knowl Based Syst 165:157–168. https://doi.org/10.1016/j.knosys.2018.11.025.https://linkinghub.elsevier.com/retrieve/pii/S095070511830577X
Yang HC, Lin CS, Huang ZR, Tsai TH (2017) Text mining on player personality for game recommendation. In: Proceedings of the 4th multidisciplinary international social networks conference on ZZZ—MISNC ’17, pp 1–6. ACM Press, New York, New York, USA. https://doi.org/10.1145/3092090.3092132.
Yi MY, Lee OJ, Jung JJ (2016) MBTI-based collaborative recommendation system: a case study of webtoon contents. In: Vinh PC, Alagar V (eds) Context-aware systems and applications. Springer International Publishing, Cham, pp 101–110
Youyou W, Stillwell D, Schwartz HA, Kosinski M (2017) Birds of a feather do flock together: behavior-based personality-assessment method reveals personality similarity among couples and friends. Psychol Sci 28(3):276–284
Youyou W, Stillwell D, Schwartz HA, Kosinski M (2017) Birds of a feather do flock together: behavior-based personality-assessment method reveals personality similarity among couples and friends. Psychol Sci 28(3):276–284. https://doi.org/10.1177/0956797616678187.http://journals.sagepub.com/doi/10.1177/0956797617697667
Yusefi Hafshejani Z, Kaedi M, Fatemi A (2018) Improving sparsity and new user problems in collaborative filtering by clustering the personality factors. Electron Comm Res 18(4):813–836. https://doi.org/10.1007/s10660-018-9287-x.http://link.springer.com/10.1007/s10660-018-9287-x
Zeigler-Hill V, Monica S (2015) The HEXACO model of personality and video game preferences. Entertain Comput 11:21–26.https://doi.org/10.1016/j.entcom.2015.08.001.https://linkinghub.elsevier.com/retrieve/pii/S1875952115000105
Zhang Z, Zhao X, Wang G, Bi X (2018) A new point-of-interest classification model with an extreme learning machine. Cognit Comput 10(6):951–964. https://doi.org/10.1007/s12559-018-9599-0
Zhang L, Peng S, Winkler S (2020) PersEmoN: a deep network for joint analysis of apparent personality, emotion and their relationship. IEEE Trans Affect Comput pp 1–1. https://doi.org/10.1109/TAFFC.2019.2951656. https://ieeexplore.ieee.org/document/8897617/
Zheng Y, Subramaniyan A (2019) Personality-aware collaborative learning: models and explanations. In: International conference on advanced information networking and applications, pp 631–642. Springer. https://doi.org/10.1007/978-3-030-15032-7_53. http://link.springer.com/10.1007/978-3-030-15032-7_53
Zhou JL, Fu Y, Lu H, Sun CJ (2011) From popularity to personality—a heuristic music recommendation method for niche market. J Comput Sci Technol 26(5):816. https://doi.org/10.1007/s11390-011-0180-5
Zhu H, Li L, Jiang H, Tan A (2020) Inferring personality traits from attentive regions of user liked images via weakly supervised dual convolutional network. Neural Process Lett 51(3):2105–2121. https://doi.org/10.1007/s11063-019-09987-7.http://link.springer.com/10.1007/s11063-019-09987-7
Acknowledgements
Special thanks to Alessandro Vinciarelli for the insightful discussion about personality computing. This work was supported by the National Natural Science Foundation of China under Grant 61872038.
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Dhelim, S., Aung, N., Bouras, M. et al. A survey on personality-aware recommendation systems. Artif Intell Rev 55, 2409–2454 (2022). https://doi.org/10.1007/s10462-021-10063-7
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DOI: https://doi.org/10.1007/s10462-021-10063-7