AI Human-Computer Interaction of Intelligence Recommendation System: Frontier and Future Agenda
Wang Xiwei1,2,3, Wuji Siguleng1, Liu Yutong1, Luo Ran1
1.School of Business and Management, Jilin University, Changchun 130022 2.Research Center for Big Data Management, Jilin University, Changchun 130022 3.Research Center of Cyberspace Governance, Jilin University, Changchun 130022
1 吴丹, 袁方. 用户信息行为研究热点与动向: 协同、移动与智能[J]. 图书情报知识, 2017(5): 13-21. 2 李月琳, 张建伟, 王姗姗, 等. 回望“十三五”: 国内信息行为研究的特点、不足与展望[J]. 信息资源管理学报, 2022, 12(1): 21-33, 45. 3 Jugovac M, Jannach D. Interacting with recommenders—overview and research directions[J]. ACM Transactions on Interactive Intelligent Systems, 2017, 7(3): 1-46. 4 吴丹, 孙国烨. 迈向可解释的交互式人工智能: 动因、途径及研究趋势[J]. 武汉大学学报(哲学社会科学版), 2021, 74(5): 16-28. 5 Harper R H R. The role of HCI in the age of AI[J]. International Journal of Human-Computer Interaction, 2019, 35(15): 1331-1344. 6 Nunes I, Jannach D. A systematic review and taxonomy of explanations in decision support and recommender systems[J]. User Modeling and User-Adapted Interaction, 2017, 27: 393-444. 7 Shin D. The effects of explainability and causability on perception, trust, and acceptance: implications for explainable AI[J]. International Journal of Human-Computer Studies, 2021, 146: 102551. 8 Konstan J A, Riedl J. Recommender systems: from algorithms to user experience[J]. User Modeling and User-Adapted Interaction, 2012, 22: 101-123. 9 刘凯, 王伟军, 黄英辉, 等. 个性化推荐系统理论探索: 从系统向用户为中心的演进[J]. 情报理论与实践, 2016, 39(3): 52-56. 10 Tran T N T, Felfernig A, Tintarev N. Humanized recommender systems: state-of-the-art and research issues[J]. ACM Transactions on Interactive Intelligent Systems, 2021, 11(2): 1-41. 11 周琴英, 杨文正. 推荐系统用户模型的研究热点及启示——基于近十年核心文献的知识图谱分析[J]. 情报科学, 2020, 38(9): 166-173. 12 刘海鸥, 黄文娜, 张源强, 等. 移动社交网络情境化推荐关键问题研究综述[J]. 小型微型计算机系统, 2020, 41(9): 1812-1819. 13 Snyder H. Literature review as a research methodology: an overview and guidelines[J]. Journal of Business Research, 2019, 104: 333-339. 14 王晰巍, 靖继鹏, 王韦玮. 中外信息生态学术论文比较研究——基于文献计量方法[J]. 图书情报工作, 2013, 57(17): 95-101. 15 姜婷婷, 权明喆, 魏子瑶. 信息规避研究: 边界、脉络与动向[J]. 中国图书馆学报, 2020, 46(4): 99-114. 16 Tao D, Wang T Y, Wang T S, et al. A systematic review and meta-analysis of user acceptance of consumer-oriented health information technologies[J]. Computers in Human Behavior, 2020, 104: 106147. 17 孙玉伟, 成颖, 谢娟. 科研人员数据复用行为研究: 系统综述与元综合[J]. 中国图书馆学报, 2019, 45(3): 110-130. 18 Rapp A, Curti L, Boldi A. The human side of human-chatbot interaction: a systematic literature review of ten years of research on text-based chatbots[J]. International Journal of Human-Computer Studies, 2021, 151: 102630. 19 Wolfswinkel J F, Furtmueller E, Wilderom C P M. Using grounded theory as a method for rigorously reviewing literature[J]. European Journal of Information Systems, 2013, 22(1): 45-55. 20 Dunne C. The place of the literature review in grounded theory research[J]. International Journal of Social Research Methodology, 2011, 14(2): 111-124. 21 Nunes F, Verdezoto N, Fitzpatrick G, et al. Self-care technologies in HCI: trends, tensions, and opportunities[J]. ACM Transactions on Computer-Human Interaction, 2015, 22(6): Article No.33. 22 Mencarini E, Rapp A, Tirabeni L, et al. Designing wearable systems for sports: a review of trends and opportunities in human-computer interaction[J]. IEEE Transactions on Human-Machine Systems, 2019, 49(4): 314-325. 23 李韬奋, 杨水利, 祝明伟. 体验型产品个性化推荐的结构维度实证研究——以图书产品为例[J]. 软科学, 2021, 35(6): 139-144. 24 Kowatsch T, Maass W. In-store consumer behavior: how mobile recommendation agents influence usage intentions, product purchases, and store preferences[J]. Computers in Human Behavior, 2010, 26(4): 697-704. 25 ?man P, Liikkanen L A. Interacting with context factors in music recommendation and discovery[J]. International Journal of Human-Computer Interaction, 2017, 33(3): 165-179. 26 Jahanbakhsh F, Awadallah A H, Dumais S T, et al. Effects of past interactions on user experience with recommended documents[C]// Proceedings of the 2020 Conference on Human Information Interaction and Retrieval. New York: ACM Press, 2020: 153-162. 27 Patrick Rau P L, Li Y, Li D J. Effects of communication style and culture on ability to accept recommendations from robots[J]. Computers in Human Behavior, 2009, 25(2): 587-595. 28 Farzan R, Brusilovsky P. Encouraging user participation in a course recommender system: an impact on user behavior[J]. Computers in Human Behavior, 2011, 27(1): 276-284. 29 Barko-Sherif S, Elsweiler D, Harvey M. Conversational agents for recipe recommendation[C]// Proceedings of the 2020 Conference on Human Information Interaction and Retrieval. New York: ACM Press, 2020: 73-82. 30 Ghasemaghaei M, Hassanein K, Benbasat I. Assessing the design choices for online recommendation agents for older adults: older does not always mean simpler information technology[J]. MIS Quarterly, 2019, 43(1): 329-346. 31 Kotkov D, Konstan J, Zhao Q, et al. Investigating serendipity in recommender systems based on real user feedback[C]// Proceedings of the 33rd Annual ACM Symposium on Applied Computing. New York: ACM Press, 2018: 1341-1350. 32 Alam I, Khusro S, Khan M. Factors affecting the performance of recommender systems in a smart TV environment[J]. Technologies, 2019, 7(2): 41. 33 Rabbi M, Aung M S, Gay G, et al. Feasibility and acceptability of mobile phone-based auto-personalized physical activity recommendations for chronic pain self-management: pilot study on adults[J]. Journal of Medical Internet Research, 2018, 20(10): e10147. 34 Samson B P V, Sumi Y. Exploring factors that influence connected drivers to (not) use or follow recommended optimal routes[C]// Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. New York: ACM Press, 2019: Paper No.371. 35 吴继飞, 于洪彦, 朱翊敏, 等. 人工智能推荐对消费者采纳意愿的影响[J]. 管理科学, 2020, 33(5): 29-43. 36 吕巍, 杨颖, 张雁冰. AI个性化推荐下消费者感知个性化对其点击意愿的影响[J]. 管理科学, 2020, 33(5): 44-57. 37 李贺, 侯力铁, 祝琳琳. 移动图书馆情景感知信息推荐服务用户接受行为研究[J]. 图书情报工作, 2019, 63(12): 94-104. 38 Kim M S, Kim S. Factors influencing willingness to provide personal information for personalized recommendations[J]. Computers in Human Behavior, 2018, 88: 143-152. 39 曹树金, 王雅琪. 图书馆微信公众号图书阅读推广文章采纳行为影响因素[J]. 图书馆论坛, 2021, 41(1): 99-110. 40 王伟军, 王阳, 王玉珠, 等. 移动商务用户个性化推荐采纳行为影响因素的实证研究[J]. 系统管理学报, 2017, 26(5): 816-823. 41 于微微, 王心妍, 相静. 个性化推荐系统用户接受意愿和采纳行为影响因素研究[J]. 图书情报导刊, 2018, 3(4): 74-79. 42 Shi Y N, Zeng Q, Nah F F H, et al. Effect of timing and source of online product recommendations: an eye-tracking study[C]// Proceedings of the 4th International Conference on HCI in Business, Government and Organizations. Cham: Springer, 2017: 95-104. 43 陈梅梅, 董晨光, 茅金波, 等. 推荐时机对个性化推荐采纳影响的实验研究[J]. 情报探索, 2020(5): 9-15. 44 李治, 孙锐. 社会互动对推荐系统用户感知及信息采纳的影响研究[J]. 情报学报, 2019, 38(11): 1138-1149. 45 段尧清, 刘宇明, 蔡诗茜, 等. 数字图书馆个性化推荐用户信息采纳行为影响研究——信息采纳意向的中介效应[J]. 现代情报, 2019, 39(2): 85-93. 46 Viswanathan S, Omidvar-Tehrani B, Bruyat A, et al. Hybrid Wizard of Oz: concept testing a recommender system[C]// Proceedings of the Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems. New York: ACM Press, 2020: 1-7. 47 徐玲玲, 朱婧. 移动电商个性化推荐对消费者购买意愿影响分析[J]. 商业经济研究, 2018(6): 54-57. 48 张钟月, 向菲. 高校用户对图书馆资源荐购使用意愿的影响因素研究[J]. 图书馆研究, 2020, 50(5): 44-51. 49 Qin L, Kong S E. Perceived helpfulness, perceived trustworthiness, and their impact upon social commerce users’ intention to seek shopping recommendations[J]. Journal of Internet Commerce, 2015, 14(4): 492-508. 50 Yang X. Determinants of consumers’ continuance intention to use social recommender systems: a self-regulation perspective[J]. Technology in Society, 2021, 64: 101464. 51 Kim B. Understanding key factors of users’ intentions to repurchase and recommend digital items in social virtual worlds[J]. Cyberpsychology, Behavior and Social Networking, 2012, 15(10): 543-550. 52 李治, 孙锐. 推荐解释对改变用户行为意向的研究——基于传播说服理论的视阈[J]. 中国软科学, 2019(6): 176-184. 53 张雄涛, 甘明鑫. 隐私视角下社交媒体推荐对用户在线交互意向的影响机理研究[J]. 现代情报, 2021, 41(5): 33-43, 103. 54 Chang T S, Hsiao W H. Factors influencing intentions to use social recommender systems: a social exchange perspective[J]. Cyberpsychology, Behavior and Social Networking, 2013, 16(5): 357-363. 55 Ma X M, Sun Y Q, Guo X T, et al. Understanding users’ negative responses to recommendation algorithms in short-video platforms: a perspective based on the Stressor-Strain-Outcome (SSO) framework[J]. Electronic Markets, 2022, 32(1): 41-58. 56 Ziesemer A, Müller L, Silveira M S. Just rate it! Gamification as part of recommendation[C]// Proceedings of the 16th International Conference on Human-Computer Interaction. Cham: Springer, 2014: 786-796. 57 Shin D. How do users interact with algorithm recommender systems? The interaction of users, algorithms, and performance[J]. Computers in Human Behavior, 2020, 109: 106344. 58 罗婷予, Miguel Baptista Nunes. 从用户视角理解智能推荐系统[J]. 数字图书馆论坛, 2019(10): 30-36. 59 Shin D. Embodying algorithms, enactive artificial intelligence and the extended cognition: You can see as much as you know about algorithm[J]. Journal of Information Science, 2023, 49(1): 18-31. 60 范梓腾, 宁晶, 魏娜. 社交媒体用户的算法推荐内容接受度研究[J]. 电子政务, 2021(7): 113-124. 61 Ghasemaghaei M. The impact of in-depth online recommendation agents on consumer disorientation and cognitive absorption perceptions[J]. Behaviour & Information Technology, 2020, 39(4): 414-430. 62 Furner C P, Drake J R, Zinko R, et al. Online review antecedents of trust, purchase, and recommendation intention: a simulation-based experiment for hotels and AirBnBs[J]. Journal of Internet Commerce, 2022, 21(1): 79-103. 63 Bastian M, Helberger N, Makhortykh M. Safeguarding the journalistic DNA: attitudes towards the role of professional values in algorithmic news recommender designs[J]. Digital Journalism, 2021, 9(6): 835-863. 64 Mican D, Sitar-T?ut D A, Moisescu O I. Perceived usefulness: a silver bullet to assure user data availability for online recommendation systems[J]. Decision Support Systems, 2020, 139: 113420. 65 Kim J, Gambino A. Do we trust the crowd or information system? Effects of personalization and bandwagon cues on users' attitudes and behavioral intentions toward a restaurant recommendation website[J]. Computers in Human Behavior, 2016, 65: 369-379. 66 Rubin E R, Argyris Y A, Benbasat I. Consumers’ trust in price-forecasting recommendation agents[C]// Proceedings of the 4th International Conference on HCI in Business, Government and Organizations. Cham: Springer, 2017: 71-80. 67 Tokushige H, Narumi T, Ono S, et al. Trust lengthens decision time on unexpected recommendations in human-agent interaction[C]// Proceedings of the 5th International Conference on Human Agent Interaction. New York: ACM Press, 2017: 245-252. 68 Barbu C M, Carbonell G, Ziegler J. The influence of trust cues on the trustworthiness of online reviews for recommendations[C]// Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing. New York: ACM Press, 2019: 1687-1689. 69 Yang X. Influence of informational factors on purchase intention in social recommender systems[J]. Online Information Review, 2020, 44(2): 417-431. 70 Kunkel J, Donkers T, Michael L, et al. Let me explain: impact of personal and impersonal explanations on trust in recommender systems[C]// Proceedings of the 2019 CHI Conference on Human Factors in Computing. New York: ACM Press, 2019: Paper No.487. 71 Talwar S, Dhir A, Scuotto V, et al. Barriers and paradoxical recommendation behaviour in online to offline (O2O) services. A convergent mixed-method study[J]. Journal of Business Research, 2021, 131: 25-39. 72 Wen E M J, Yap B, Fu S. Improving of user trust in machine learning recommender-based business applications through UI design: a case study[C]// Proceedings of the 5th International Conference on HCI in Business, Government, and Organizations. Cham: Springer, 2018: 715-729. 73 Bigras E, Jutras M A, Sénécal S, et al. In AI we trust: characteristics influencing assortment planners’ perceptions of AI based recommendation agents[C]// Proceedings of the 5th International Conference on HCI in Business, Government, and Organizations. Cham: Springer, 2018: 3-16. 74 Ashraf M, Ismawati Jaafar N, Sulaiman A. System- vs. consumer-generated recommendations: affective and social-psychological effects on purchase intention[J]. Behaviour & Information Technology, 2019, 38(12): 1259-1272. 75 郭浩, 张芷茵. 智能算法推荐与用户情绪关系的实验探究[J]. 新媒体研究, 2019, 5(15): 15-17. 76 Reimer T, Benkenstein M. Not just for the recommender: how eWOM incentives influence the recommendation audience[J]. Journal of Business Research, 2018, 86: 11-21. 77 Rui J R. Objective evaluation or collective self-presentation: what people expect of LinkedIn recommendations[J]. Computers in Human Behavior, 2018, 89: 121-128. 78 Rafique H, Almagrabi A O, Shamim A, et al. Investigating the acceptance of mobile library applications with an extended technology acceptance model (TAM)[J]. Computers & Education, 2020, 145: 103732. 79 聂静虹, 宋甲子. 泛化与偏见: 算法推荐与健康知识环境的构建研究——以今日头条为例[J]. 新闻与传播研究, 2020, 27(9): 23-42, 126. 80 Nechushtai E, Lewis S C. What kind of news gatekeepers do we want machines to be? Filter bubbles, fragmentation, and the normative dimensions of algorithmic recommendations[J]. Computers in Human Behavior, 2019, 90: 298-307. 81 Hasan M R, Jha A K, Liu Y. Excessive use of online video streaming services: impact of recommender system use, psychological factors, and motives[J]. Computers in Human Behavior, 2018, 80: 220-228. 82 Kim J W. Scan and click: the uses and gratifications of social recommendation systems[J]. Computers in Human Behavior, 2014, 33: 184-191. 83 Pian W J, Chi J X, Ma F C. The causes, impacts and countermeasures of COVID-19 “Infodemic”: a systematic review using narrative synthesis[J]. Information Processing & Management, 2021, 58(6): 102713. 84 Joer? T, Hoffmann S, Mai R, et al. Digitalization as solution to environmental problems? When users rely on augmented reality-recommendation agents[J]. Journal of Business Research, 2021, 128: 510-523. 85 Rhee C E, Choi J. Effects of personalization and social role in voice shopping: an experimental study on product recommendation by a conversational voice agent[J]. Computers in Human Behavior, 2020, 109: 106359. 86 da Paix?o Pinto N, dos Santos Fran?a J B, de Sá Sousa H P, et al. Conversational agents for elderly interaction[C]// Proceedings of the 2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design. Piscataway: IEEE, 2021: 1-6. 87 Aguiar N R. A paradigm for assessing adults’ and children’s concepts of artificially intelligent virtual characters[J]. Human Behavior and Emerging Technologies, 2021, 3(4): 618-634. 88 王晰巍, 罗然, 刘宇桐. AI在智慧图书馆应用趋势: 机遇与挑战[J]. 情报科学, 2021, 39(9): 3-10, 41. 89 化柏林. “数据、技术、应用”三位一体的公共文化服务智慧化[J]. 中国图书馆学报, 2021, 47(2): 40-52. 90 Marjanovic O, Cecez-Kecmanovic D, Vidgen R. Algorithmic pollution: making the invisible visible[J]. Journal of Information Technology, 2021, 36(4): 391-408. 91 Shin D. The perception of humanness in conversational journalism: an algorithmic information-processing perspective[J]. New Media & Society, 2022, 24(12): 2680-2704. 92 Hu Q, Lu Y B, Pan Z, et al. Can AI artifacts influence human cognition? The effects of artificial autonomy in intelligent personal assistants[J]. International Journal of Information Management, 2021, 56: 102250. 93 Wang C L, Teo T S H, Janssen M. Public and private value creation using artificial intelligence: an empirical study of AI voice robot users in Chinese public sector[J]. International Journal of Information Management, 2021, 61: 102401. 94 Mahmud H, Najmul Islam A K M, Ahmed S I, et al. What influences algorithmic decision-making? A systematic literature review on algorithm aversion[J]. Technological Forecasting and Social Change, 2022, 175: 121390. 95 Kim J, Giroux M, Lee J C. When do you trust AI? The effect of number presentation detail on consumer trust and acceptance of AI recommendations[J]. Psychology & Marketing, 2021, 38(7): 1140-1155. 96 Kordzadeh N, Ghasemaghaei M. Algorithmic bias: review, synthesis, and future research directions[J]. European Journal of Information Systems, 2022, 31(3): 388-409. 97 Stephanidis C, Salvendy G, Antona M, et al. Seven HCI grand challenges[J]. International Journal of Human-Computer Interaction, 2019, 35(14): 1229-1269. 98 Guo Y Y, Lu Z Z, Kuang H B, et al. Information avoidance behavior on social network sites: information irrelevance, overload, and the moderating role of time pressure[J]. International Journal of Information Management, 2020, 52: 102067. 99 Frischknecht R. A social cognition perspective on autonomous technology[J]. Computers in Human Behavior, 2021, 122: 106815. 100 Langer M, Landers R N. The future of artificial intelligence at work: a review on effects of decision automation and augmentation on workers targeted by algorithms and third-party observers[J]. Computers in Human Behavior, 2021, 123: 106878. 101 Xi N N, Hamari J. Shopping in virtual reality: a literature review and future agenda[J]. Journal of Business Research, 2021, 134: 37-58.
