[
Asabere, N. Y., Acakpovi, A., & Michael, M. B. (2017). Improving socially-aware recommendation accuracy through personality. IEEE Transactions on Affective Computing, 9(3), 351-361.
]Search in Google Scholar
[
Bagher, R. C., Hassanpour, H., & Mashayekhi, H. (2017). User trends modeling for a content-based recommender system. Expert Systems with Applications, 87, 209-219.
]Search in Google Scholar
[
Bandura, A. (1999). Social cognitive theory of personality. Handbook of personality, 2, 154-96.
]Search in Google Scholar
[
Becker, P. (1999). Beyond the big five. Personality and individual differences, 26(3), 511-530.
]Search in Google Scholar
[
Berkovsky, S., Taib, R., Hijikata, Y., Braslavsku, P., & Knijnenburg, B. (2018, July). A cross-cultural analysis of trust in recommender systems. In Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization (pp. 285-289).
]Search in Google Scholar
[
Bobadilla, J., Ortega, F., Hernando, A., & Gutiérrez, A. (2013). Recommender systems survey. Knowledge-based systems, 46, 109-132.
]Search in Google Scholar
[
Bucerzan, D., & Bejan, C. A. (2021). Blockchain. Today applicability and implications. In Soft Computing Applications: Proceedings of the 8th International Workshop Soft Computing Applications (SOFA 2018), Vol. I 8 (pp. 152-164). Springer International Publishing.
]Search in Google Scholar
[
Burke, R. (2000). Knowledge-based recommender systems. Encyclopedia of library and information systems, 69(Supplement 32), 175-186.
]Search in Google Scholar
[
Burke, R., Felfernig, A., & Göker, M. H. (2011). Recommender systems: An overview. AI Magazine, 32(3), 13-18.
]Search in Google Scholar
[
Burke, R., O’Mahony, M. P., & Hurley, N. J. (2015). Robust collaborative recommendation. Recommender systems handbook, 961-995.
]Search in Google Scholar
[
Cai, W., Jin, Y., & Chen, L. (2022, April). Impacts of Personal Characteristics on User Trust in Conversational Recommender Systems. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (pp. 1-14).
]Search in Google Scholar
[
Caro-Martínez, M., Jiménez-Díaz, G., & Recio-García, J. A. (2021). Conceptual modeling of explainable recommender systems: an ontological formalization to guide their design and development. Journal of Artificial Intelligence Research, 71, 557-589.
]Search in Google Scholar
[
Chen, L., De Gemmis, M., Felfernig, A., Lops, P., Ricci, F., & Semeraro, G. (2013). Human decision making and recommender systems. ACM Transactions on Interactive Intelligent Systems (TiiS), 3(3), 1-7.
]Search in Google Scholar
[
Chen, L., Wu, W., & He, L. (2013). How personality influences users’ needs for recommendation diversity?. In CHI’13 extended abstracts on human factors in computing systems (pp. 829-834).
]Search in Google Scholar
[
Del Olmo, F. H., & Gaudioso, E. (2008). Evaluation of recommender systems: A new approach. Expert Systems with Applications, 35(3), 790-804.
]Search in Google Scholar
[
Dhelim, S., Aung, N., Bouras, M. A., Ning, H., & Cambria, E. (2022). A survey on personality-aware recommendation systems. Artificial Intelligence Review, 1-46.
]Search in Google Scholar
[
Dinu, V. (2021). Artificial intelligence in wholesale and retail. Amfiteatru Economic, 23(56), 5-7.
]Search in Google Scholar
[
Dinu, V., Bucur, M., Enache, C., Fratiloiu, B., Cohen-Tzedec, B., & Vasiliu, C. (2022). European consumer trust as a driving force of mobile commerce. Transformations in Business & Economics, 21(2), 419-434.
]Search in Google Scholar
[
Dong, M., Yuan, F., Yao, L., Wang, X., Xu, X., & Zhu, L. (2022). A survey for trust-aware recommender systems: A deep learning perspective. Knowledge-Based Systems, 249, 108954.
]Search in Google Scholar
[
Drachsler, H., Verbert, K., Santos, O. C., & Manouselis, N. (2015). Panorama of recommender systems to support learning. Recommender systems handbook, 421-451.
]Search in Google Scholar
[
Earl, P. E. (2012). On Kahneman’s Thinking, Fast and Slow: what you see is not all there is. Prometheus, 30(4), 449-455.
]Search in Google Scholar
[
Elahi, M., Ricci, F., & Rubens, N. (2016). A survey of active learning in collaborative filtering recommender systems. Computer Science Review, 20, 29-50.
]Search in Google Scholar
[
Eysenck, H. J. (Ed.). (2012). A model for personality. Springer Science & Business Media.
]Search in Google Scholar
[
Fei, G., Mukherjee, A., Liu, B., Hsu, M., Castellanos, M., & Ghosh, R. (2013). Exploiting burstiness in reviews for review spammer detection. In Proceedings of the international AAAI conference on web and social media (Vol. 7, No. 1, pp. 175-184).
]Search in Google Scholar
[
Ge, Y., Liu, S., Fu, Z., Tan, J., Li, Z., Xu, S., ... & Zhang, Y. (2022). A survey on trustworthy recommender systems. arXiv preprint arXiv:2207.12515.
]Search in Google Scholar
[
Grădinaru, C., Obadă, D. R., Grădinaru, I. A., & Dabija, D. C. (2022). Enhancing Sustainable Cosmetics Brand Purchase: A Comprehensive Approach Based on the SOR Model and the Triple Bottom Line. Sustainability, 14(21), 14118.
]Search in Google Scholar
[
Gunes, I., Kaleli, C., Bilge, A., & Polat, H. (2014). Shilling attacks against recommender systems: a comprehensive survey. Artificial Intelligence Review, 42, 767-799.
]Search in Google Scholar
[
Guo, G., Zhang, J., Thalmann, D., Basu, A., & Yorke-Smith, N. (2014, March). From ratings to trust: an empirical study of implicit trust in recommender systems. In Proceedings of the 29th annual acm symposium on applied computing (pp. 248-253).
]Search in Google Scholar
[
Guy, I., & Carmel, D. (2011, March). Social recommender systems. In Proceedings of the 20th international conference companion on World wide web (pp. 283-284).
]Search in Google Scholar
[
Hardy, A., Tolmeijer, E., Edwards, V., Ward, T., Freeman, D., Emsley, R., ... & Garety, P. (2020). Measuring reasoning in paranoia: development of the fast and slow thinking questionnaire. Schizophrenia Bulletin Open, 1(1), sgaa035.
]Search in Google Scholar
[
Harman, J. L., O’Donovan, J., Abdelzaher, T., & Gonzalez, C. (2014, October). Dynamics of human trust in recommender systems. In Proceedings of the 8th ACM Conference on Recommender systems (pp. 305-308).
]Search in Google Scholar
[
Herlocker, J. L., Konstan, J. A., Terveen, L. G., & Riedl, J. T. (2004). Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems (TOIS), 22(1), 5-53.
]Search in Google Scholar
[
Herse, S., Vitale, J., Tonkin, M., Ebrahimian, D., Ojha, S., Johnston, B., ... & Williams, M. A. (2018, August). Do you trust me, blindly? Factors influencing trust towards a robot recommender system. In 2018 27th IEEE international symposium on robot and human interactive communication (RO-MAN) (pp. 7-14). IEEE.
]Search in Google Scholar
[
Hu, R., & Pu, P. (2010). A study on user perception of personality-based recommender systems. In User Modeling, Adaptation, and Personalization: 18th International Conference, UMAP 2010, Big Island, HI, USA, June 20-24, 2010. Proceedings 18 (pp. 291-302). Springer Berlin Heidelberg.
]Search in Google Scholar
[
Jameson, A., Willemsen, M. C., Felfernig, A., De Gemmis, M., Lops, P., Semeraro, G., & Chen, L. (2015). Human decision making and recommender systems. Recommender systems handbook, 611-648.
]Search in Google Scholar
[
Jannach, D., Zanker, M., Felfernig, A., & Friedrich, G. (2010). Recommender systems: an introduction. Cambridge University Press.
]Search in Google Scholar
[
Javed, U., Shaukat, K., Hameed, I. A., Iqbal, F., Alam, T. M., & Luo, S. (2021). A review of content-based and context-based recommendation systems. International Journal of Emerging Technologies in Learning (iJET), 16(3), 274-306.
]Search in Google Scholar
[
Johnson, M. K., Rustichini, A., & MacDonald III, A. W. (2009). Suspicious personality predicts behavior on a social decision-making task. Personality and individual Differences, 47(1), 30-35.
]Search in Google Scholar
[
Kahneman, D. (2011). Thinking, fast and slow. Macmillan.
]Search in Google Scholar
[
Kenny, D. A., & Judd, C. M. (2014). Power anomalies in testing mediation. Psychological science, 25(2), 334-339.
]Search in Google Scholar
[
Kompan, M., & Bieliková, M. (2014, July). Social Structure and Personality Enhanced Group Recommendation. In UMAP Workshops (pp. 1-7).
]Search in Google Scholar
[
Konstan, J. A. (Ed.). (2004). Introduction to recommender systems: Algorithms and evaluation. ACM Transactions on Information Systems (TOIS), 22(1), 1-4.
]Search in Google Scholar
[
Kunaver, M., & Požrl, T. (2017). Diversity in recommender systems–A survey. Knowledge-based systems, 123, 154-162.
]Search in Google Scholar
[
Kunkel, J., Donkers, T., Michael, L., Barbu, C. M., & Ziegler, J. (2019, May). Let me explain: Impact of personal and impersonal explanations on trust in recommender systems. In Proceedings of the 2019 CHI conference on human factors in computing systems (pp. 1-12).
]Search in Google Scholar
[
Lin, Y., Ren, P., Chen, Z., Ren, Z., Ma, J., & De Rijke, M. (2019). Explainable outfit recommendation with joint outfit matching and comment generation. IEEE Transactions on Knowledge and Data Engineering, 32(8), 1502-1516.
]Search in Google Scholar
[
Linett, A., Monforton, J., MacKenzie, M. B., McCabe, R. E., Rowa, K., & Antony, M. M. (2019). The Social Suspiciousness Scale: Development, validation, and implications for understanding social anxiety disorder. Journal of Psychopathology and Behavioral Assessment, 41, 280-293.
]Search in Google Scholar
[
Lops, P., De Gemmis, M., & Semeraro, G. (2011). Content-based recommender systems: State of the art and trends. Recommender systems handbook, 73-105.
]Search in Google Scholar
[
Lu, J., Wu, D., Mao, M., Wang, W., & Zhang, G. (2015). Recommender system application developments: a survey. Decision support systems, 74, 12-32.
]Search in Google Scholar
[
Lu, L., Medo, M., Yeung, C. H., Zhang, Y. C., Zhang, Z. K., & Zhou, T. (2012). Recommender systems. Physics reports, 519(1), 1-49.
]Search in Google Scholar
[
Ma, H., Zhou, D., Liu, C., Lyu, M. R., & King, I. (2011, February). Recommender systems with social regularization. In Proceedings of the fourth ACM international conference on Web search and data mining (pp. 287-296).
]Search in Google Scholar
[
Martinez-Cruz, C., Porcel, C., Bernabé-Moreno, J., & Herrera-Viedma, E. (2015). A model to represent users trust in recommender systems using ontologies and fuzzy linguistic modeling. Information Sciences, 311, 102-118.
]Search in Google Scholar
[
McNee, S. M. (2006). Meeting user information needs in recommender systems. University of Minnesota.
]Search in Google Scholar
[
Melville, P., & Sindhwani, V. (2010). Recommender systems. Encyclopedia of machine learning, 1, 829-838.
]Search in Google Scholar
[
Moradi, P., & Ahmadian, S. (2015). A reliability-based recommendation method to improve trust-aware recommender systems. Expert Systems with Applications, 42(21), 7386-7398.
]Search in Google Scholar
[
O’Rourke, H. P., & MacKinnon, D. P. (2018). Reasons for testing mediation in the absence of an intervention effect: A research imperative in prevention and intervention research. Journal of studies on alcohol and drugs, 79(2), 171-181.
]Search in Google Scholar
[
O’Donovan, J., & Smyth, B. (2005, January). Trust in recommender systems. In Proceedings of the 10th international conference on Intelligent user interfaces (pp. 167-174).
]Search in Google Scholar
[
Ogrean, C. & Herciu, M. (2020). Digital Transformation as strategic shift-a bibliometric analysis. Studies in Business & Economics, 16(3), 136-151.
]Search in Google Scholar
[
Ogrean, C. & Herciu, M. (2020). Digital Transformation of Centru Region–Romania. Needs Assessment. Studies in Business and Economics, 15(2), 270-281.
]Search in Google Scholar
[
Ojagh, S., Malek, M. R., & Saeedi, S. (2020). A social–aware recommender system based on user’s personal smart devices. ISPRS International Journal of Geo-Information, 9(9), 519.
]Search in Google Scholar
[
Park, D. H., Kim, H. K., Choi, I. Y., & Kim, J. K. (2012). A literature review and classification of recommender systems research. Expert systems with applications, 39(11), 10059-10072.
]Search in Google Scholar
[
Pelau, C., Barbul, M., & Bojescu, I. (2022). A conceptual comparative approach on personal AI assistants and external service robots. In Proceedings of the International Conference on Business Excellence (Vol. 16, No. 1, pp. 1466-1474).
]Search in Google Scholar
[
Pelau, C., Dabija, D. C., & Ene, I. (2021). What makes an AI device human-like? The role of interaction quality, empathy and perceived psychological anthropomorphic characteristics in the acceptance of artificial intelligence in the service industry. Computers in Human Behavior, 122, 106855.
]Search in Google Scholar
[
Pelau, C., Dabija, D. C., & Serban, D. (2023). The Physical Presence and Relationship Distance for Efficient Consumer–AI-Business Interactions and Marketing. In The Palgrave Handbook of Interactive Marketing (pp. 239-254). Cham: Springer International Publishing.
]Search in Google Scholar
[
Pelau, C., Pop, M. I., Stanescu, M., & Sanda, G. (2023). The Breaking News Effect and Its Impact on the Credibility and Trust in Information Posted on Social Media. Electronics, 12(2), 423.
]Search in Google Scholar
[
Pop, R. A., Hlédik, E., & Dabija, D. C. (2023). Predicting consumers’ purchase intention through fast fashion mobile apps: The mediating role of attitude and the moderating role of COVID-19. Technological Forecasting and Social Change, 186, 122111.
]Search in Google Scholar
[
Pytlik, N., Soll, D., & Mehl, S. (2020). Thinking preferences and conspiracy belief: Intuitive thinking and the jumping to conclusions-bias as a basis for the belief in conspiracy theories. Frontiers in psychiatry, 11, 568942.
]Search in Google Scholar
[
Radu, I., Sendroiu, C., & Demeter, M. (2018). Model for Monitorising and Evaluating Global Performance for Public Service Operators. In Proceedings of Administration and Public Management International Conference (Vol. 14, No. 1, pp. 47-53). Research Centre in Public Administration and Public Services, Bucharest, Romania.
]Search in Google Scholar
[
Radu, I., Sendroiu, C., Demeter, M. L., & Cazacu, F. (2018). Possible Future Applications of the Blockchain Technology. In Proceedings of the International Conference on Economics and Social Sciences (Vol. 1, pp. 245-251). Bucharest University of Economic Studies, Romania.
]Search in Google Scholar
[
Raţiu, C., Bucerzan, D., & Crăciun, M. (2013). Contribution to Watermarking Techniques. In Soft Computing Applications: Proceedings of the 5th International Workshop Soft Computing Applications (SOFA) (pp. 403-410). Springer Berlin Heidelberg.
]Search in Google Scholar
[
Recio-Garcia, J. A., Jimenez-Diaz, G., Sanchez-Ruiz, A. A., & Diaz-Agudo, B. (2009, October). Personality aware recommendations to groups. In Proceedings of the third ACM conference on Recommender systems (pp. 325-328).
]Search in Google Scholar
[
Ricci, F., Rokach, L., & Shapira, B. (2010). Introduction to recommender systems handbook. In Recommender systems handbook (pp. 1-35). Boston, MA: Springer US.
]Search in Google Scholar
[
Samson, A., & Voyer, B. G. (2012). Two minds, three ways: dual system and dual process models in consumer psychology. AMS review, 2, 48-71.
]Search in Google Scholar
[
Schafer, J. B., Konstan, J., & Riedl, J. (1999, November). Recommender systems in e-commerce. In Proceedings of the 1st ACM conference on Electronic commerce (pp. 158-166).
]Search in Google Scholar
[
Schwartz, L. P., & Hursh, S. R. (2022). A behavioral economic analysis of smartwatches using internet‐based hypothetical demand. M anagerial and Decision Economics, 43(7), 2729-2736.
]Search in Google Scholar
[
Shin, D. (2020). How do users interact with algorithm recommender systems? The interaction of users, algorithms, and performance. Computers in Human Behavior, 109, 106344.
]Search in Google Scholar
[
Si, M., & Li, Q. (2020). Shilling attacks against collaborative recommender systems: a review. Artificial Intelligence Review, 53, 291-319.
]Search in Google Scholar
[
Tang, J., Du, X., He, X., Yuan, F., Tian, Q., & Chua, T. S. (2019). Adversarial training towards robust multimedia recommender system. IEEE Transactions on Knowledge and Data Engineering, 32(5), 855-867.
]Search in Google Scholar
[
Tavakolifard, M., & Almeroth, K. C. (2012). Social computing: an intersection of recommender systems, trust/reputation systems, and social networks. IEEE Network, 26(4), 53-58.
]Search in Google Scholar
[
Tkalcic, M., & Chen, L. (2015). Personality and recommender systems. Recommender systems handbook, 715-739.
]Search in Google Scholar
[
Tsai, C. H., & Brusilovsky, P. (2021). The effects of controllability and explainability in a social recommender system. User Modeling and User-Adapted Interaction, 31, 591-627.
]Search in Google Scholar
[
Vozalis, E., & Margaritis, K. G. (2003, September). Analysis of recommender systems algorithms. In The 6th Hellenic European Conference on Computer Mathematics & its Applications (pp. 732-745).
]Search in Google Scholar
[
Wang, B., Ester, M., Bu, J., & Cai, D. (2014, July). Who also likes it? generating the most persuasive social explanations in recommender systems. In Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence (pp. 173-179).
]Search in Google Scholar
[
Wang, H., Lian, D., Tong, H., Liu, Q., Huang, Z., & Chen, E. (2021). Hypersorec: Exploiting hyperbolic user and item representations with multiple aspects for social-aware recommendation. ACM Transactions on Information Systems (TOIS), 40(2), 1-28.
]Search in Google Scholar
[
Wang, M., Wu, Z., Sun, X., Feng, G., & Zhang, B. (2019). Trust-aware collaborative filtering with a denoising autoencoder. Neural Processing Letters, 49, 835-849.
]Search in Google Scholar
[
Xiao, L., Min, Z., Yiqun, L., & Shaoping, M. (2017). A neural network model for social-aware recommendation. In Information Retrieval Technology: 13th Asia Information Retrieval Societies Conference, AIRS 2017, Jeju Island, South Korea, November 22-24, 2017, Proceedings 13 (pp. 125-137). Springer International Publishing.
]Search in Google Scholar
[
Yakhchi, S., Ghafari, S. M., & Orgun, M. (2021, May). TAP: A two-level trust and personality-aware recommender system. In Service-Oriented Computing–ICSOC 2020 Workshops: AIOps, CFTIC, STRAPS, AI-PA, AI-IOTS, and Satellite Events, Dubai, United Arab Emirates, December 14–17, 2020, Proceedings (pp. 294-308). Cham: Springer International Publishing.
]Search in Google Scholar
[
Zhang, Y., & Chen, X. (2020). Explainable recommendation: A survey and new perspectives. Foundations and Trends® in Information Retrieval, 14(1), 1-101.
]Search in Google Scholar
[
Zhang, Y., Lai, G., Zhang, M., Zhang, Y., Liu, Y., & Ma, S. (2014, July). Explicit factor models for explainable recommendation based on phrase-level sentiment analysis. In Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval (pp. 83-92).
]Search in Google Scholar
[
Zhao, W. X., Li, S., He, Y., Wang, L., Wen, J. R., & Li, X. (2016). Exploring demographic information in social media for product recommendation. Knowledge and Information Systems, 49, 61-89.
]Search in Google Scholar