Rational or Emotional? Next-Item Recommendations in Virtual Games via Disentangling Players’ Needs

References

• Akram U, Junaid M, Zafar AU, Li Z, Fan M (2021) Online purchase intention in Chinese social commerce platforms: Being emotional or rational? J. Retailing Consumer Services 63:102669.Crossref, Google Scholar
• Bagozzi RP, Baumgartner H, Pieters R, Zeelenberg M (2003) The role of emotions in goal-directed behavior. Ratneshwar S, David GM, Huffman C, eds. The Why of Consumption (Routledge, London), 36–58.Google Scholar
• Bai T, Zou L, Zhao WX, Du P, Liu W, Nie J, Wen J (2019) CTREC: A long-short demands evolution model for continuous-time recommendation. Piwowarski B, Chevalier M, Gaussier É, Maarek Y, Nie J-Y, Scholer F, eds. Proc. 42nd Internat. ACM SIGIR Conf. Res. Development Inform. Retrieval SIGIR 2019 (ACM, New York), 675–684.Google Scholar
• Chen Z, Nguyen TD, Xu Y, Amato C, Cooper S, Sun Y, El-Nasr MS (2018) The art of drafting: A team-oriented hero recommendation system for multiplayer online battle arena games. Pera S, Ekstrand MD, Amatriain X, O’Donovan J, eds Proc. 12th ACM Conf. Recommender Systems RecSys 2018 (ACM, New York), 200–208.Google Scholar
• Deng Q, Wang K, Zhao M, Zou Z, Wu R, Tao J, Fan C, Chen L (2020) Personalized bundle recommendation in online games. d’Aquin M, Dietze S, Hauff C, Curry E, Cudré-Mauroux P, eds. CIKM ‘20 29th ACM Internat. Conf. Inform. Knowledge Management (ACM, New York), 2381–2388.Google Scholar
• Dong Y, Chawla NV, Swami A (2017) metapath2vec: Scalable representation learning for heterogeneous networks. Proc. 23rd ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (ACM, New York), 135–144.Google Scholar
• Eastlick MA, Feinberg RA (1999) Shopping motives for mail catalog shopping. J. Bus. Res. 45(3):281–290.Crossref, Google Scholar
• Elster J (1999) Alchemies of the Mind: Rationality and the Emotions (Cambridge University Press, Cambridge, UK).Google Scholar
• Guo H, Hao L, Mukhopadhyay T, Sun D (2019) Selling virtual currency in digital games: Implications for gameplay and social welfare. Inform. Systems Res. 30(2):430–446.Link, Google Scholar
• Hamari J, Lehdonvirta V (2010) Game design as marketing: How game mechanics create demand for virtual goods. Internat. J. Bus. Sci. Appl. Management 5(1):14–29.Crossref, Google Scholar
• Hochreiter S, Urgen Schmidhuber J, Elvezia C (1997) Long short-term memory. Neural Comput. 9(8):1735–1780.Crossref, Google Scholar
• Holbrook MB, Hirschman EC (1982) The experiential aspects of consumption: Consumer fantasies, feelings, and fun. J. Consumer Res. 9(2):132–140.Crossref, Google Scholar
• Jiao Y, Tang CS, Wang J (2021) Selling virtual items in free-to-play games: Transparent selling vs. opaque selling. Service Sci. 13(2):53–76.Link, Google Scholar
• Jiao Y, Tang CS, Wang J (2022) An empirical study of play duration and in-app purchase behavior in mobile games. Production Oper. Management 31(9):3435–3456.Crossref, Google Scholar
• Liu Y, Tian Y, Xu Y, Zhao S, Huang Y, Fan Y, Duan F, Guo P (2021) TPGN: A time-preference gate network for e-commerce purchase intention recognition. Knowledge-Based Systems 220:106920.Crossref, Google Scholar
• López MA (2016) Analysis of response times when purchasing rational and emotional products, compared to a cognitive task. A study using FMRI. IOSR J. Bus. Management 18(10):72–79.Crossref, Google Scholar
• Marder B, Gattig D, Collins E, Pitt L, Kietzmann J, Erz A (2019) The Avatar’s new clothes: Understanding why players purchase non-functional items in free-to-play games. Comput. Human Behav. 91:72–83.Crossref, Google Scholar
• Maslowska E, Malthouse EC, Hollebeek LD (2022) The role of recommender systems in fostering consumers’ long-term platform engagement. J. Service Management 33(4–5):721–732.Crossref, Google Scholar
• Ni Y, Ou D, Liu S, Li X, Ou W, Zeng A, Si L (2018) Perceive your users in depth: Learning universal user representations from multiple e-commerce tasks. Guo Y, Farooq F, eds. Proc. 24th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining KDD 2018 (ACM, New York), 596–605.Google Scholar
• Przybylski AK, Rigby CS, Ryan RM (2010) A motivational model of video game engagement. Rev. General Psych. 14(2):154–166.Crossref, Google Scholar
• Rashed A, Elsayed S, Schmidt-Thieme L (2022) Context and attribute-aware sequential recommendation via cross-attention. Golbeck J, Harper FM, Murdock V, Ekstrand MD, Shapira B, Basilico J, Lundgaard KT, Oldridge E, eds. RecSys ‘22 Sixteenth ACM Conf. Recommender Systems (ACM, New York), 71–80.Google Scholar
• Tang J, Wang K (2018) Personalized top-n sequential recommendation via convolutional sequence embedding. Chang Y, Zhai C, Liu Y, Maarek Y, eds. Proc. Eleventh ACM Internat. Conf. Web Search Data Mining WSDM 2018 (ACM, New York), 565–573.Google Scholar
• Tran V, Salha-Galvan G, Sguerra B, Hennequin R (2023) Attention mixtures for time-aware sequential recommendation. Chen H-H, Duh W-J, Huang H-H, Kato MP, Mothe J, Poblete B, eds. Proc. 46th Internat. ACM SIGIR Conf. Research Development Inform. Retrieval SIGIR 2023 (ACM, New York), 1821–1826.Google Scholar
• Wang XJ, Liu T, Fan W (2023) TGVX: Dynamic personalized poi deep recommendation model. INFORMS J. Comput. 35(4):786–796.Link, Google Scholar
• Wang Y, Wu J, Wu Z, Adomavicius G (2025) Efficient and flexible long-tail recommendation using cosine patterns. INFORMS J. Comput. 37(2):446–464.Link, Google Scholar
• Wu L, He X, Wang X, Zhang K, Wang M (2023) A survey on accuracy-oriented neural recommendation: From collaborative filtering to information-rich recommendation. IEEE Trans. Knowledge Data Engrg. 35(5):4425–4445.Google Scholar
• Yang H, Huang Z (2019) Mining personality traits from social messages for game recommender systems. Knowledge-Based Systems 165:157–168.Crossref, Google Scholar
• Yang Z, Yang D, Dyer C, He X, Smola AJ, Hovy EH (2016) Hierarchical attention networks for document classification. Knight K, Nenkova A, Rambow O, eds. NAACL HLT 2016 Conf. North Amer. Chapter Assoc. Comput Linguistics Human Language Tech. (Association for Computational Linguistics, Vienna), 1480–1489.Google Scholar
• Zhang H, Sun J, Liu F, G Knight J (2014) Be rational or be emotional: Advertising appeals, service types and consumer responses. Eur. J. Marketing 48(11–12):2105–2126.Crossref, Google Scholar
• Zhao Y, Yang S, Shum M, Dutta S (2022) A dynamic model of player level-progression decisions in online gaming. Management Sci. 68(11):8062–8082.Link, Google Scholar
• Zhao H, Zhao C, Wu R, Ge Y (2026) Rational or emotional? Next-item recommendations in virtual games via disentangling players’ needs. https://doi.org/10.1287/ijoc.2025.1179.cd, https://github.com/INFORMSJoC/2025.1179.Google Scholar
• Zhao H, Zhao C, Zhang X, Liu N, Zhu H, Liu Q, Xiong H (2023) An ensemble learning approach with gradient resampling for class-imbalance problems. INFORMS J. Comput. 35(4):747–763.Link, Google Scholar
• Zhou S, Dai X, Chen H, Zhang W, Ren K, Tang R, He X, Yu Y (2020) Interactive recommender system via knowledge graph-enhanced reinforcement learning. Huang JX, Chang Y, Cheng X, Kamps J, Murdock V, Wen J-R, Liu Y, eds. Proc. 43rd Internat. ACM SIGIR Conf. Res. Development Inform. Retrieval SIGIR 2020 (ACM, New York), 179–188.Google Scholar
• Zhou G, Mou N, Fan Y, Pi Q, Bian W, Zhou C, Zhu X, Gai K (2019) Deep interest evolution network for click-through rate prediction. Thirty-Third AAAI Conf. Artificial Intelligence AAAI 2019 Thirty-First Innovative Appl. Artificial Intelligence Conf. IAAI 2019 Ninth AAAI Sympos. Educational Adv. Artificial Intelligence EAAI 2019 (AAAI Press, Palo Alto, CA), 5941–5948.Google Scholar
• Zhou G, Zhu X, Song C, Fan Y, Zhu H, Ma X, Yan Y, Jin J, Li H, Gai K (2018) Deep interest network for click-through rate prediction. Guo Y, Farooq F, eds. Proc. 24th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining KDD 2018 (ACM, New York), 1059–1068.Google Scholar