106
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Recommendations with Benefits: Exploring Explanations in Information Sharing Recommender Systems for Temporary Teams

, ORCID Icon, , &
Received 26 Jul 2023, Accepted 30 Oct 2023, Published online: 20 Nov 2023

References

  • Aguinis, H., Villamor, I., & Ramani, R. S. (2021). Mturk research: Review and recommendations. Journal of Management, 47(4), 823–837. https://doi.org/10.1177/0149206320969787
  • Arnold, M., Bellamy, R. K. E., Hind, M., Houde, S., Mehta, S., Mojsilovic, A., Nair, R., Ramamurthy, K. N., Olteanu, A., Piorkowski, D., Reimer, D., Richards, J., Tsay, J., & Varshney, K. R. (2019). Factsheets: Increasing trust in ai services through supplier’s declarations of conformity. IBM Journal of Research and Development, 63(4/5), 6:1–6:13. https://doi.org/10.1147/JRD.2019.2942288
  • Bakker, R. M., Boroş, S., Kenis, P., & Oerlemans, L. A. (2013). It’s only temporary: Time frame and the dynamics of creative project teams. British Journal of Management, 24(3), 383–397. https://doi.org/10.1111/j.1467-8551.2012.00810.x
  • Bansal, G., Zahedi, F. M., & Gefen, D. (2016). Do context and personality matter? trust and privacy concerns in disclosing private information online. Information & Management, 53(1), 1–21. https://doi.org/10.1016/j.im.2015.08.001
  • Benbasat, I., & Wang, W. (2005). Trust in and adoption of online recommendation agents. Journal of the Association for Information Systems, 6(3), 72–101. https://doi.org/10.17705/1jais.00065
  • Benisch, M., Kelley, P. G., Sadeh, N., & Cranor, L. F. (2011). Capturing location-privacy preferences: Quantifying accuracy and user-burden tradeoffs. Personal and Ubiquitous Computing, 15(7), 679–694. https://doi.org/10.1007/s00779-010-0346-0
  • Berinsky, A. J., Margolis, M. F., & Sances, M. W. (2014). Separating the shirkers from the workers? making sure respondents pay attention on self-administered surveys. American Journal of Political Science, 58(3), 739–753. https://doi.org/10.1111/ajps.12081
  • Biddle, B. J., & Marlin, M. M. (1987). Causality, confirmation, credulity, and structural equation modeling. Child Development, 58(1), 4–17. https://doi.org/10.2307/1130287
  • Bouas, K. S., & Arrow, H. (1996). The development of group identity in computer and face-to-face groups with membership change. Computer Supported Cooperative Work (CSCW), 4(2–3), 153–178. https://doi.org/10.1007/BF00749745
  • Brawley, A. M., & Pury, C. L. (2016). Work experiences on mturk: Job satisfaction, turnover, and information sharing. Computers in Human Behavior, 54, 531–546. https://doi.org/10.1016/j.chb.2015.08.031
  • Brennan, N. M., Guillamon-Saorin, E., & Pierce, A. (2009). Methodological insights: Impression management: Developing and illustrating a scheme of analysis for narrative disclosures–A methodological note. Accounting, Auditing & Accountability Journal, 22(5), 789–832. https://doi.org/10.1108/09513570910966379
  • Brouthers, K. D., Brouthers, L. E., & Wilkinson, T. J. (1995). Strategic alliances: Choose your partners. Long Range Planning, 28(3), 2–25. https://doi.org/10.1016/0024-6301(95)00008-7
  • Burke, R., Mattei, N., Grozin, V., Voida, A., & Sonboli, N. (2022). Multi-agent social choice for dynamic fairness-aware recommendation. In Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization (pp. 234–244). https://doi.org/10.1145/3511047.3538032
  • Cannon-Bowers, J. A., & Salas, E. (1998). Team performance and training in complex environments: Recent findings from applied research. Current Directions in Psychological Science, 7(3), 83–87. https://doi.org/10.1111/1467-8721.ep10773005
  • Chatti, M. A., Guesmi, M., Vorgerd, L., Ngo, T., Joarder, S., Ain, Q. U., & Muslim, A. (2022). Is more always better? the effects of personal characteristics and level of detail on the perception of explanations in a recommender system. In Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization (pp. 254–264). https://doi.org/10.1145/3503252.3531304
  • Chen, X., Zhang, Y., & Qin, Z. (2019). Dynamic explainable recommendation based on neural attentive models. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 33, pp. 53–60). https://doi.org/10.1609/aaai.v33i01.330153
  • Chen, Z., Sun, F., Tang, Y., Chen, H., Gao, J., & Ding, B. (2023). Studying the impact of data disclosure mechanism in recommender systems via simulation. ACM Transactions on Information Systems, 41(3), 1–26. https://doi.org/10.1145/3569452
  • Colbeck, C. L., Campbell, S. E., & Bjorklund, S. A. (2000). Grouping in the dark: What college students learn from group projects. The Journal of Higher Education, 71(1), 60–83. https://doi.org/10.1080/00221546.2000.11780816
  • Converse, S., Cannon-Bowers, J., & Salas, E. (1993). Shared mental models in expert team decision making. Individual and group decision making: Current issues, 221, 221–246.
  • Dalal, D. K., Nolan, K. P., & Gannon, L. E. (2017). Are pre-assembly shared work experiences useful for temporary-team assembly decisions? A study of Olympic ice hockey team composition. Journal of Business and Psychology, 32(5), 561–574. https://doi.org/10.1007/s10869-016-9481-6
  • Druskat, V. U., & Kayes, D. C. (2000). Learning versus performance in short-term project teams. Small Group Research, 31(3), 328–353. https://doi.org/10.1177/104649640003100304
  • Ezzamel, M., & Willmott, H. (1998). Accounting for teamwork: A critical study of group-based systems of organizational control. Administrative Science Quarterly, 43(2), 358–396. https://doi.org/10.2307/2393856
  • Freeman, G., & Wohn, D. Y. (2019). Understanding esports team formation and coordination. Computer Supported Cooperative Work (CSCW), 28(1–2), 95–126. https://doi.org/10.1007/s10606-017-9299-4
  • Garcia, I., Sebastia, L., & Onaindia, E. (2011). On the design of individual and group recommender systems for tourism. Expert Systems with Applications, 38(6), 7683–7692. https://doi.org/10.1016/j.eswa.2010.12.143
  • Gedikli, F., Jannach, D., & Ge, M. (2014). How should I explain? A comparison of different explanation types for recommender systems. International Journal of Human–Computer Studies, 72(4), 367–382. https://doi.org/10.1016/j.ijhcs.2013.12.007
  • Ghanem, N., Leitner, S., & Jannach, D. (2022). Balancing consumer and business value of recommender systems: A simulation-based analysis. Electronic Commerce Research and Applications, 55, 101195. https://doi.org/10.1016/j.elerap.2022.101195
  • Goldberg, L. R. (1999). A broad-bandwidth, public domain, personality inventory measuring the lower-level facets of several five-factor models. Personality Psychology in Europe, 7(1), 7–28. https://doi.org/10.1111/j.1749-6632.1999.tb08349.x
  • Gómez-Zará, D., Guo, M., DeChurch, L. A., & Contractor, N. (2020). The impact of displaying diversity information on the formation of self-assembling teams. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1–15). https://doi.org/10.1145/3313831.3376654
  • Greif, I., & Sarin, S. (1987). Data sharing in group work. ACM Transactions on Information Systems, 5(2), 187–211. https://doi.org/10.1145/27636.27640
  • Harris, A. M., Gómez-Zará, D., DeChurch, L. A., & Contractor, N. S. (2019). Joining together online: The trajectory of CSCW scholarship on group formation. In Proceedings of the ACM on Human–Computer Interaction, 3(CSCW) (pp. 1–27). https://doi.org/10.1145/3359250
  • Herlocker, J. L., Konstan, J. A., & Riedl, J. (2000). Explaining collaborative filtering recommendations. In Proceedings of the 2000 ACM Conference on Computer Supported Cooperative Work (pp. 241–250). https://doi.org/10.1145/358916.358995
  • Himeur, Y., Alsalemi, A., Al-Kababji, A., Bensaali, F., Amira, A., Sardianos, C., Dimitrakopoulos, G., & Varlamis, I. (2021). A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects. Information Fusion, 72, 1–21. https://doi.org/10.1016/j.inffus.2021.02.002
  • Himeur, Y., Sohail, S. S., Bensaali, F., Amira, A., & Alazab, M. (2022). Latest trends of security and privacy in recommender systems: A comprehensive review and future perspectives. Computers & Security, 118, 102746. https://doi.org/10.1016/j.cose.2022.102746
  • Hoyle, R. H. (1995). Structural equation modeling: Concepts, issues, and applications. Sage.
  • Jameson, A., & Smyth, B. (2007). Recommendation to groups. In The adaptive web (pp. 596–627). Springer.
  • Jessup, S. A., Schneider, T. R., Alarcon, G. M., Ryan, T. J., & Capiola, A. (2019). The measurement of the propensity to trust automation. In Virtual, Augmented and Mixed Reality. Applications and Case Studies: 11th International Conference, VAMR 2019, Held as Part of the 21st HCI International Conference, HCII 2019, Orlando, FL, USA, July 26–31, 2019, Proceedings, Part II 21 (pp. 476–489). Springer.
  • Johnson, J. A. (2014). Measuring thirty facets of the five factor model with a 120-item public domain inventory: Development of the ipip-neo-120. Journal of Research in Personality, 51, 78–89. https://doi.org/10.1016/j.jrp.2014.05.003
  • Jozani, M., Ayaburi, E., Ko, M., & Choo, K.-K R. (2020). Privacy concerns and benefits of engagement with social media-enabled apps: A privacy calculus perspective. Computers in Human Behavior, 107, 106260. https://doi.org/10.1016/j.chb.2020.106260
  • Kizilcec, R. F. (2016). How much information? effects of transparency on trust in an algorithmic interface. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (pp. 2390–2395).
  • Klimoski, R., & Mohammed, S. (1994). Team mental model: Construct or metaphor? Journal of Management, 20(2), 403–437. https://doi.org/10.1177/014920639402000206
  • Knijnenburg, B. P. (2015). A user-tailored approach to privacy decision support. University of California.
  • Knijnenburg, B. P., Anaraky, R. G., Wilkinson, D., Namara, M., He, Y., Cherry, D., & Ash, E. (2021). User-tailored privacy. In Modern socio-technical perspectives on privacy (pp. 367–393). Springer International Publishing.
  • Knijnenburg, B. P., & Kobsa, A. (2013). Making decisions about privacy: Information disclosure in context-aware recommender systems. ACM Transactions on Interactive Intelligent Systems, 3(3), 1–23. https://doi.org/10.1145/2499670
  • Knijnenburg, B. P., & Kobsa, A. (2014). Increasing sharing tendency without reducing satisfaction: Finding the best privacy-settings user interface for social networks. In ICIS.
  • Knijnenburg, B. P., Kobsa, A., & Jin, H. (2013). Dimensionality of information disclosure behavior. International Journal of Human–Computer Studies, 71(12), 1144–1162. https://doi.org/10.1016/j.ijhcs.2013.06.003
  • Komiak, S. Y. X. (2003). [The impact of internalization and familiarity on trust and adoption of recommendation agents] [PhD thesis]. University of British Columbia.
  • Kouki, P., Schaffer, J., Pujara, J., O’Donovan, J., & Getoor, L. (2019). Personalized explanations for hybrid recommender systems. In Proceedings of the 24th International Conference on Intelligent User Interfaces (pp. 379–390). https://doi.org/10.1145/3301275.3302306
  • Lenters, K., & Winters, K.-L. (2013). Fracturing writing spaces: Multimodal storytelling ignites process writing. The Reading Teacher, 67(3), 227–237. https://doi.org/10.1002/TRTR.1210
  • Liang, H., Xu, Y., Li, Y., Nayak, R., & Weng, L.-T. (2009). Personalized recommender systems integrating social tags and item taxonomy. In 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (Vol. 1, pp. 540–547). IEEE. https://doi.org/10.1109/WI-IAT.2009.89
  • Lu, H., Ma, W., Wang, Y., Zhang, M., Wang, X., Liu, Y., Chua, T.-S., & Ma, S. (2023). User perception of recommendation explanation: Are your explanations what users need? ACM Transactions on Information Systems, 41(2), 1–31. https://doi.org/10.1145/3565480
  • Lv, M., & Feng, S. (2021). Temporary teams: Current research focus and future directions. Quality & Quantity, 55(1), 1–18. https://doi.org/10.1007/s11135-020-00990-y
  • Lykourentzou, I., Antoniou, A., Naudet, Y., & Dow, S. P. (2016). Personality matters: Balancing for personality types leads to better outcomes for crowd teams. In Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing (pp. 260–273).
  • Marks, R. B., Sibley, S. D., & Arbaugh, J. B. (2005). A structural equation model of predictors for effective online learning. Journal of Management Education, 29(4), 531–563. https://doi.org/10.1177/1052562904271199
  • McNeese, N. J., & Reddy, M. C. (2017). The role of team cognition in collaborative information seeking. Journal of the Association for Information Science and Technology, 68(1), 129–140. https://doi.org/10.1002/asi.23614
  • Mehdy, A. N., Ekstrand, M. D., Knijnenburg, B. P., & Mehrpouyan, H. (2021). Privacy as a planned behavior: Effects of situational factors on privacy perceptions and plans. In Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization (pp. 169–178). https://doi.org/10.1145/3450613.3456829
  • Mohammed, S., Ferzandi, L., & Hamilton, K. (2010). Metaphor no more: A 15-year review of the team mental model construct. Journal of Management, 36(4), 876–910. https://doi.org/10.1177/0149206309356804
  • Mohseni, S., Zarei, N., & Ragan, E. D. (2021). A multidisciplinary survey and framework for design and evaluation of explainable ai systems. ACM Transactions on Interactive Intelligent Systems, 11(3–4), 1–45. https://doi.org/10.1145/3387166
  • Mulder, I., Swaak, J., & Kessels, J. (2004). In search of reflective behavior and shared understanding in ad hoc expert teams. Cyberpsychology & Behavior, 7(2), 141–154. https://doi.org/10.1089/109493104323024410
  • Musick, G., Schelble, B. G., Mallick, R., & McNeese, N. J. (2023). Selective sharing is caring: Toward the design of a collaborative tool to facilitate team sharing. In Proceedings of the 56th Hawaii International Conference on System Sciences.
  • Najafian, S., Inel, O., & Tintarev, N. (2020). Someone really wanted that song but it was not me! evaluating which information to disclose in explanations for group recommendations. In Proceedings of the 25th International Conference on Intelligent User Interfaces Companion (pp. 85–86).
  • Najafian, S., Musick, G., Knijnenburg, B., & Tintarev, N. (2023). How do people make decisions in disclosing personal information in tourism group recommendations in competitive versus cooperative conditions? User Modeling and User-Adapted Interaction. https://doi.org/10.1007/s11257-023-09375-w
  • Najafian, S., & Tintarev, N. (2018). Generating consensus explanations for group recommendations: An exploratory study. In Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization (pp. 245–250).
  • Nothdurft, F., Heinroth, T., & Minker, W. (2013). The impact of explanation dialogues on human–computer trust. In International Conference on Human–Computer Interaction (pp. 59–67). Springer.
  • Palan, S., & Schitter, C. (2018). Prolific. ac—A subject pool for online experiments. Journal of Behavioral and Experimental Finance, 17, 22–27. https://doi.org/10.1016/j.jbef.2017.12.004
  • Panagiotakis, C., Papadakis, H., Papagrigoriou, A., & Fragopoulou, P. (2021). Improving recommender systems via a dual training error based correction approach. Expert Systems with Applications, 183, 115386. https://doi.org/10.1016/j.eswa.2021.115386
  • Rahim, M. A. (1983). A measure of styles of handling interpersonal conflict. Academy of Management Journal. Academy of Management, 26(2), 368–376. https://doi.org/10.2307/255985
  • Resnick, P., & Varian, H. R. (1997). Recommender systems. Communications of the ACM, 40(3), 56–58. https://doi.org/10.1145/245108.245121
  • Ricci, F., Rokach, L., & Shapira, B. (2015). Recommender systems: Introduction and challenges. In Recommender systems handbook (pp. 1–34). Springer.
  • Rice, D. J., Davidson, B. D., Dannenhoffer, J. F., & Gay, G. K. (2007). Improving the effectiveness of virtual teams by adapting team processes. Computer Supported Cooperative Work (CSCW), 16(6), 567–594. https://doi.org/10.1007/s10606-007-9070-3
  • Rouse, W. B., & Morris, N. M. (1986). On looking into the black box: Prospects and limits in the search for mental models. Psychological Bulletin, 100(3), 349–363. https://doi.org/10.1037/0033-2909.100.3.349
  • Salehi, N., McCabe, A., Valentine, M., & Bernstein, M. (2017). Huddler: Convening stable and familiar crowd teams despite unpredictable availability. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (pp. 1700––1713).
  • Sardianos, C., Varlamis, I., Chronis, C., Dimitrakopoulos, G., Alsalemi, A., Himeur, Y., Bensaali, F., & Amira, A. (2021). The emergence of explainability of intelligent systems: Delivering explainable and personalized recommendations for energy efficiency. International Journal of Intelligent Systems, 36(2), 656–680. https://doi.org/10.1002/int.22314
  • Sardianos, C., Varlamis, I., Dimitrakopoulos, G., Anagnostopoulos, D., Alsalemi, A., Bensaali, F., Himeur, Y., & Amira, A. (2020). Rehab-c: Recommendations for energy habits change. Future Generation Computer Systems, 112, 394–407. https://doi.org/10.1016/j.future.2020.05.041
  • Son, J., & Rojas, E. M. (2011). Evolution of collaboration in temporary project teams: An agent-based modeling and simulation approach. Journal of Construction Engineering and Management, 137(8), 619–628. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000331
  • Stidham, H., Flynn, M., Summers, J. D., & Shuffler, M. (2018). Understanding team personality evolution in student engineering design teams using the five factor model. In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (Vol. 51784, p. V003T04A012). American Society of Mechanical Engineers.
  • Stidham, H., Summers, J., Shuffler, M. (2018). Using the five factor model to study personality convergence on student engineering design teams. In DS 92: Proceedings of the DESIGN 2018 15th International Design Conference (pp. 2145–2154). https://doi.org/10.21278/idc.2018.0508
  • Stray, J., Halevy, A., Assar, P., Hadfield-Menell, D., Boutilier, C., Ashar, A., Beattie, L., Ekstrand, M., Leibowicz, C., Sehat, C. M. (2022). Building human values into recommender systems: An interdisciplinary synthesis. arXiv preprint arXiv:2207.10192.
  • Symeonidis, P., Nanopoulos, A., & Manolopoulos, Y. (2009). Moviexplain: A recommender system with explanations. In Proceedings of the Third ACM Conference on Recommender Systems (pp. 317–320).
  • Tannenbaum, S. I., Mathieu, J. E., Salas, E., & Cohen, D. (2012). Teams are changing: Are research and practice evolving fast enough? Industrial and Organizational Psychology, 5(1), 2–24. https://doi.org/10.1111/j.1754-9434.2011.01396.x
  • Thomas, K. W. (2008). Thomas-Kilmann conflict mode. TKI Profile and Interpretive Report, 1(11). https://doi.org/10.1037/t02326-000
  • Tintarev, N. (2007). Explanations of recommendations. In Proceedings of the 2007 ACM Conference on Recommender Systems (pp. 203–206). https://doi.org/10.1145/1297231.1297275
  • Tintarev, N., & Masthoff, J. (2009). Evaluating recommender explanations: Problems experienced and lessons learned for the evaluation of adaptive systems. In UCDEAS workshop associated with UMAP (pp. 54–63). CEUR-WS.
  • Tintarev, N., & Masthoff, J. (2011). Designing and evaluating explanations for recommender systems. In Recommender systems handbook (pp. 479–510). Springer.
  • Tintarev, N., & Masthoff, J. (2012). Evaluating the effectiveness of explanations for recommender systems. User Modeling and User-Adapted Interaction, 22(4–5), 399–439. https://doi.org/10.1007/s11257-011-9117-5
  • Tran, T. N. T., Atas, M., Felfernig, A., Le, V. M., Samer, R., & Stettinger, M. (2019). Towards social choice-based explanations in group recommender systems. In Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization (pp. 13–21). https://doi.org/10.1145/3320435.3320437
  • Tsai, J. Y., Kelley, P. G., Cranor, L. F., & Sadeh, N. (2010). Location-sharing technologies: Privacy risks and controls. ISJLP, 6, 119. https://ssrn.com/abstract=1997782
  • Tuckman, B. W., & Jensen, M. A. C. (1977). Stages of small-group development revisited. Group & Organization Studies, 2(4), 419–427. https://doi.org/10.1177/105960117700200404
  • Tuffley, D. (2008). Leadership of complex virtual teams. In CSCW 2008 Workshop (Vol. 9).
  • Twyman, M., Newman, D. A., DeChurch, L., & Contractor, N. (2022). Teammate invitation networks: The roles of recommender systems and prior collaboration in team assembly. Social Networks, 68, 84–96. https://doi.org/10.1016/j.socnet.2021.04.008
  • Varlamis, I., Sardianos, C., Chronis, C., Dimitrakopoulos, G., Himeur, Y., Alsalemi, A., Bensaali, F., & Amira, A. (2023). Using big data and federated learning for generating energy efficiency recommendations. International Journal of Data Science and Analytics, 16(3), 353–369. https://doi.org/10.1007/s41060-022-00331-2
  • Vig, J., Sen, S., & Riedl, J. (2009). Tagsplanations: Explaining recommendations using tags. In Proceedings of the 14th International Conference on Intelligent User Interfaces (pp. 47–56).
  • Wang, W., & Benbasat, I. (2007). Recommendation agents for electronic commerce: Effects of explanation facilities on trusting beliefs. Journal of Management Information Systems, 23(4), 217–246. https://doi.org/10.2753/MIS0742-1222230410
  • Webber, S. S., Detjen, J., MacLean, T. L., & Thomas, D. (2019). Team challenges: Is artificial intelligence the solution? Business Horizons, 62(6), 741–750. https://doi.org/10.1016/j.bushor.2019.07.007
  • Wilkinson, D., Alkan, Ö., Liao, Q. V., Mattetti, M., Vejsbjerg, I., Knijnenburg, B. P., & Daly, E. (2021). Why or why not? the effect of justification styles on chatbot recommendations. ACM Transactions on Information Systems, 39(4), 1–21. https://doi.org/10.1145/3441715
  • Wu, L., Lu, W., Zhao, R., Xu, J., Li, X., & Xue, F. (2022). Using blockchain to improve information sharing accuracy in the onsite assembly of modular construction. Journal of Management in Engineering, 38(3), 04022014. https://doi.org/10.1061/(ASCE)ME.1943-5479.0001029
  • Xu, F., Michael, K., & Chen, X. (2013). Factors affecting privacy disclosure on social network sites: An integrated model. Electronic Commerce Research, 13(2), 151–168. https://doi.org/10.1007/s10660-013-9111-6
  • Yang, X., Tong, Y., & Teo, H. H. (2015). Fostering fast-response spontaneous virtual team: Effects of member skill awareness and shared governance on team cohesion and outcomes. Journal of the Association for Information Systems, 16(11), 919–946. https://doi.org/10.17705/1jais.00414
  • Zengy, J., Wang, X., Liu, J., Chen, Y., Liang, Z., Chua, T.-S., & Chua, Z. L. (2022). Shadewatcher: Recommendation-guided cyber threat analysis using system audit records. In 2022 IEEE Symposium on Security and Privacy (SP) (pp. 489–506). IEEE. https://doi.org/10.1109/SP46214.2022.9833669
  • Zhang, S., Jiang, Z., Yao, J., Feng, F., Kuang, K., Zhao, Z., Li, S., Yang, H., Chua, T., & Wu, F. (2023). Causal distillation for alleviating performance heterogeneity in recommender systems. IEEE Transactions on Knowledge and Data Engineering, 1–16. https://doi.org/10.1109/TKDE.2023.3290545
  • Zhang, Y., & Chen, X. (2020). Explainable recommendation: A survey and new perspectives. Foundations and Trends® in Information Retrieval, 14(1), 1–101. https://doi.org/10.1561/1500000066
  • Zhao, R., Benbasat, I., & Cavusoglu, H. (2019). Do users always want to know more? Investigating the relationship between system transparency and users’ trust in advice-giving systems.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.