review-article

The Power of Personalization: A Systematic Review of Personality-Adaptive Chatbots

Authors:

Tarek Ait Baha,

Mohamed El Hajji,

Youssef Es-Saady,

Hammou FadiliAuthors Info & Claims

SN Computer Science, Volume 4, Issue 5

https://doi.org/10.1007/s42979-023-02092-6

Published: 30 August 2023 Publication History

Abstract

Conversation serves as a crucial means for individuals to express their intentions, feelings, attitudes, and personalities, making natural conversational interaction a vital technical competency for conversational agents. With the growing advancements in deep learning and natural language processing techniques, these systems have gained significant traction across various sectors. However, a key challenge lies in developing chatbots that can generate human-like replies adaptable to users’ personalities. This article presents a comprehensive review of previous research on personality adaptive chatbots. By employing a systematic review approach and analyzing 3524 publications from recognized digital databases, we identify 66 relevant works focused on personality adaptive chatbots. The review examines recent studies on personality interpretations and definitions, providing valuable insights into different perspectives on personality. Additionally, it categorizes and analyzes various deep learning approaches used in personality recognition, identifying effective techniques for capturing and understanding a users’ personalities. Furthermore, the review explores the consideration of personal characteristics such as linguistic style, behavior traits, and Persona in adapting chatbot responses to users’ preferences. Lastly, through an analysis of relevant chatbot implementations, the review identifies trends in incorporating personality adaptation and metrics to assess effectiveness and user satisfaction. By synthesizing these findings, we contribute to a deeper understanding of personality adaptive chatbots and the challenges and opportunities associated with developing chatbots that can generate human-like replies adaptable to users’ personalities.

References

[1]

Abdul-Kader SA and Woods J Survey on chatbot design techniques in speech conversation systems Int J Adv Comput Sci Appl 2015

[2]

Adiwardana D, Luong M-T, So DR, Hall J, Fiedel N, Thoppilan R, Yang Z, Kulshreshtha A, Nemade G, Lu Y, Le QV. Towards a human-like open-domain chatbot. 2020. http://arxiv.org/abs/2001.09977.

[3]

Ahmad H, Asghar MZ, Khan AS, and Habib A A systematic literature review of personality trait classification from textual content Open Comput Sci 2020 10 1 175-193

[4]

Ahmad R, Siemon D, Gnewuch U, Robra-Bissantz S. The benefits and caveats of personality-adaptive conversational agents in mental health care. In: AMCIS 2021 proceedings. 2021. https://aisel.aisnet.org/amcis2021/sig_hci/sig_hci/21.

[5]

Ait Baha T, El Hajji M, Es-saady Y, and Fadili H Towards highly adaptive Edu-Chatbot Procedia Comput Sci 2022 198 397-403

[6]

Al-Rfou R, Pickett M, Snaider J, Sung Y, Strope B, Kurzweil R. Conversational contextual cues: the case of personalization and history for response ranking. 2016. http://arxiv.org/abs/1606.00372.

[7]

Arnoux P-H, Xu A, Boyette N, Mahmud J, Akkiraju R, Sinha V. 25 Tweets to know you: a new model to predict personality with social media. 2017. https://arxiv.org/abs/1704.05513v1.

[8]

Chen H, Liu X, Yin D, and Tang J A survey on dialogue systems: recent advances and new frontiers ACM SIGKDD Explor Newsl 2017 19 2 25-35

[9]

Manning CD Introduction to information retrieval Nat Lang Eng 2010 16 1 100-103

[10]

Chu E, Vijayaraghavan P, Roy D. Learning personas from dialogue with attentive memory networks. In: Proceedings of the 2018 conference on empirical methods in natural language processing. 2018. pp. 2638–2646.

[11]

Dixit A and Jakhar SK Airport capacity management: a review and bibliometric analysis J Air Transp Manag 2021 91 102010

[12]

Dušek O, Novikova J, and Rieser V Evaluating the state-of-the-art of end-to-end natural language generation: the E2E NLG challenge Comput Speech Lang 2020 59 123-156

[13]

El-Demerdash K, El-Khoribi RA, Ismail Shoman MA, and Abdou S Deep learning based fusion strategies for personality prediction Egypt Inform J 2021

[14]

Brown PF An estimate of an upper bound for the entropy of English Comput Linguist 1992

[15]

Gao J, Galley M, Li L. Neural approaches to conversational AI. In: Proceedings of the 56th annual meeting of the association for computational linguistics: tutorial abstracts. 2018. Pp. 2–7.

[16]

Gao J, Galley M, Li L. Neural approaches to conversational AI. 2019. http://arxiv.org/abs/1809.08267.

[17]

Gao X, Lee S, Zhang Y, Brockett C, Galley M, Gao J, Dolan B. Jointly optimizing diversity and relevance in neural response generation. 2019. http://arxiv.org/abs/1902.11205.

[18]

Geng D, Feng Y, and Zhu Q Sustainable design for users: a literature review and bibliometric analysis Environ Sci Pollut Res 2020 27 24 29824-29836

[19]

Goldberg LR An alternative “description of personality”: the big-five factor structure J Pers Soc Psychol 1990 59 6 1216-1229

[20]

Gupta K, Joshi M, Chatterjee A, Damani S, Narahari KN, Agrawal P. Insights from building an open-ended conversational agent. In: Proceedings of the first workshop on NLP for conversational AI. 2019. pp. 106–112.

[21]

Harrison V, Reed L, Oraby S, Walker M. Maximizing stylistic control and semantic accuracy in NLG: personality variation and discourse contrast. 2019. http://arxiv.org/abs/1907.09527

[22]

Hernandez R, Scott I. Predicting Myers–Briggs type indicator with text. In: 31st Conference on neural information processing systems (NIPS 2017). 2017.

[23]

Houlsby N, Giurgiu A, Jastrzebski S, Morrone B, de Laroussilhe Q, Gesmundo A, Attariyan M, Gelly S. Parameter-efficient transfer learning for NLP. 2019. http://arxiv.org/abs/1902.00751.

[24]

Hu T, Xu A, Liu Z, You Q, Guo Y, Sinha V, Luo J, Akkiraju R. Touch your heart: a tone-aware chatbot for customer care on social media. 2018. https://arxiv.org/abs/1803.02952v2.

[25]

Inaba M, Takahashi K. Estimating user interest from open-domain dialogue. In: Proceedings of the 19th annual SIGdial meeting on discourse and dialogue. 2018. pp. 32–40.

[26]

Isard A, Brockmann C, Oberlander J. Individuality and alignment in generated dialogues. In: Proceedings of the fourth international natural language generation conference. 2006. pp 25–32. https://aclanthology.org/W06-1405

[27]

Jiang H, Guo A, Ma J. Personality-aware chatbot: an emerging area in conversational agents. 2020.

[28]

Joshi CK, Mi F, Faltings B. Personalization in goal-oriented dialog. 2017. http://arxiv.org/abs/1706.07503

[29]

Kaushal V and Patwardhan M Emerging trends in personality identification using online social networks—a literature survey ACM Trans Knowl Discov Data 2018 12 2 1-30

[30]

Keh SS, Cheng I. Myers–Briggs personality classification and personality-specific language generation using pre-trained language models. 2019. arXiv.

[31]

Khatua A, Cambria E, Khatua A. Let's chat about brexit! A politically-sensitive dialog system based on twitter data. 2017. https://ieeexplore.ieee.org/document/8215689/

[32]

Kim W, Khan GF, Wood J, and Mahmood MT Employee engagement for sustainable organizations: keyword analysis using social network analysis and burst detection approach Sustainability 2016 8 7 631 Article 7

[33]

Kim Y, Bang J, Choi J, Ryu S, Koo S, Lee GG. Acquisition and use of long-term memory for personalized dialog systems. In: MA3HMI@INTERSPEECH. 2014.

[34]

Kottur S, Wang X, Carvalho V. Exploring personalized neural conversational models. In: Proceedings of the twenty-sixth international joint conference on artificial intelligence. 2017. pp. 3728–3734.

[35]

Li J, Galley M, Brockett C, Spithourakis G, Gao J, Dolan B. A persona-based neural conversation model. In: Proceedings of the 54th annual meeting of the association for computational linguistics (volume 1: long papers). 2016. pp. 994–1003.

[36]

Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gøtzsche PC, Ioannidis JPA, Clarke M, Devereaux PJ, Kleijnen J, and Moher D The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration PLoS Med 2009 6 7 e1000100

[37]

Lin C-Y. ROUGE: a package for automatic evaluation of summaries. In: Text summarization branches out. 2004. pp. 74–81. https://aclanthology.org/W04-1013

[38]

Lin Z, Madotto A, Bang Y, and Fung P the adapter-bot: all-in-one controllable conversational model Proc AAAI Conf Artif Intell 2021 35 18 16081-16083 Article 18

[39]

Liu Learning to rank for information retrieval Found Trends Inf Retr 2009 3 3 225-331

[40]

Liu Perez J, Nowson S. A language-independent and compositional model for personality trait recognition from short texts. 2016. https://arxiv.org/abs/1610.04345v1

[41]

Liu Q, Chen Y, Chen B, Lou J-G, Chen Z, Zhou B, Zhang D. You impress me: dialogue generation via mutual persona perception. In: Proceedings of the 58th annual meeting of the association for computational linguistics. 2020. pp. 1417–1427.

[42]

Lowe R, Pow N, Serban I, Pineau J. The Ubuntu dialogue corpus: a large dataset for research in unstructured multi-turn dialogue systems. 2016. http://arxiv.org/abs/1506.08909

[43]

Luo L, Huang W, Zeng Q, Nie Z, Sun X. Learning personalized end-to-end goal-oriented dialog. In: Proceedings of the thirty-third AAAI conference on artificial intelligence and thirty-first innovative applications of artificial intelligence conference and ninth AAAI symposium on educational advances in artificial intelligence. 2019. pp. 6794–6801.

[44]

Ma Y, Nguyen KL, Xing FZ, and Cambria E A survey on empathetic dialogue systems Inf Fusion 2020 64 50-70

[45]

Mairesse F, Walker M. PERSONAGE: personality generation for dialogue. ACL. 2007.

[46]

Mairesse F and Walker MA Towards personality-based user adaptation: psychologically informed stylistic language generation User Model User-Adap Inter 2010 20 3 227-278

[47]

Majumder N, Poria S, Gelbukh A, and Cambria E Deep learning-based document modeling for personality detection from text IEEE Intell Syst 2017 32 2 74-79

[48]

Mazaré P-E, Humeau S, Raison M, Bordes A. Training millions of personalized dialogue agents. 2018. http://arxiv.org/abs/1809.01984

[49]

Minaee S, Kalchbrenner N, Cambria E, Nikzad N, Chenaghlu M, and Gao J Deep learning–based text classification: a comprehensive review ACM Comput Surv 2021 54 3 1-40

[50]

Mo K, Zhang Y, Li S, Li J, and Yang Q Personalizing a dialogue system with transfer reinforcement learning AAAI 2018

[51]

Morbini F, Forbell E, DeVault D. A mixed-initiative conversational dialogue system for healthcare—ACL anthology. 2012. https://aclanthology.org/W12-1620/

[52]

Myers IB and Myers PB Gifts differing: understanding personality type 1995 Boston Davies-Black Pub

[53]

Nass C, Moon Y, Fogg BJ, Reeves B, and Dryer DC Can computer personalities be human personalities? Int J Hum Comput Stud 1995 43 2 223-239

[54]

Olabiyi O, Khazane A, Salimov A, Mueller E. An adversarial learning framework for a persona-based multi-turn dialogue model. In: Proceedings of the workshop on methods for optimizing and evaluating neural language generation. 2019. pp. 1–10.

[55]

Oraby S. Controlling personality-based stylistic variation with neural natural language generators. In: Proceedings of the 19th annual SIGdial meeting on discourse and dialogue. 2018. pp. 180–190.

[56]

Oraby S, Reed L, Tandon S, Walker M. Neural multivoice models for expressing novel personalities in dialog. 2018. http://arxiv.org/abs/1809.01331

[57]

Papineni K, Roukos S, Ward T, Zhu W-J. BLEU: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting on association for computational linguistics. 2002. pp. 311–318.

[58]

Qian Q, Huang M, Zhao H, Xu J, Zhu X. Assigning personality/identity to a chatting machine for coherent conversation generation. 2017. http://arxiv.org/abs/1706.02861

[59]

Rashkin H, Smith EM, Li M, Boureau Y-L. Towards empathetic open-domain conversation models: a new benchmark and dataset. 2019. http://arxiv.org/abs/1811.00207

[60]

Sennrich R, Haddow B, Birch A. Controlling politeness in neural machine translation via side constraints. In: Proceedings of the 2016 conference of the north american chapter of the association for computational linguistics: human language technologies. 2016. pp. 35–40.

[61]

Serban IV, Lowe R, Henderson P, Charlin L, Pineau J. A survey of available corpora for building data-driven dialogue systems. 2017. http://arxiv.org/abs/1512.05742

[62]

Shuster K, Xu J, Komeili M, Ju D, Smith EM, Roller S, Ung M, Chen M, Arora K, Lane J, Behrooz M, Ngan W, Poff S, Goyal N, Szlam A, Boureau Y-L, Kambadur M, Weston J. BlenderBot 3: a deployed conversational agent that continually learns to responsibly engage. 2022. http://arxiv.org/abs/2208.03188.

[63]

Song H, Zhang W-N, Cui Y, Wang D, Liu T. Exploiting persona information for diverse generation of conversational responses. 2019. http://arxiv.org/abs/1905.12188

[64]

Sutskever I, Vinyals O, and Le QV Ghahramani Z, Welling M, Cortes C, Lawrence N, and Weinberger KQ Sequence to sequence learning with neural networks Advances in neural information processing systems 2014 Red Hook Curran Associates Inc

[65]

Symeonaki E, Arvanitis K, Piromalis D, and Papoutsidakis M Bi Y, Bhatia R, and Kapoor S Conversational user interface integration in controlling iot devices applied to smart agriculture: analysis of a chatbot system design Intelligent systems and applications 2020 Cham Springer International Publishing 1071-1088

[66]

White T, Brian M, Cox C. Intelligent conversational systems (Patent No. 10148600). 2018.

[67]

Tahami AV, Ghajar K, Shakery A. Distilling knowledge for fast retrieval-based chat-bots. 2020. http://arxiv.org/abs/2004.11045.

[68]

Vinciarelli A and Mohammadi G A survey of personality computing IEEE Trans Affect Comput 2014 5 3 273-291

[69]

Wang J, Wang X, Li F, Xu Z, Wang Z, Wang B. Group linguistic bias aware neural response generation. In: Proceedings of the 9th SIGHAN workshop on Chinese language processing. 2017. pp. 1–10. https://aclanthology.org/W17-6001

[70]

Wang X, Shi W, Kim R, Oh Y, Yang S, Zhang J, Yu Z. Persuasion for good: towards a personalized persuasive dialogue system for social good. In: Proceedings of the 57th annual meeting of the association for computational linguistics. 2019. pp. 5635–5649.

[71]

Wei H, Zhang F, Yuan NJ, Cao C, Fu H, Xie X, Rui Y, Ma W-Y. Beyond the words: predicting user personality from heterogeneous information. In: Proceedings of the tenth ACM international conference on web search and data mining. 2017. pp. 305–314.

[72]

Wolf T, Sanh V, Chaumond J, Delangue C. TransferTransfo: a transfer learning approach for neural network based conversational agents. 2019. http://arxiv.org/abs/1901.08149

[73]

Xue D, Wu L, Hong Z. Deep learning-based personality recognition from text posts of online social networks. 2018.

[74]

Yan H, Selker T. Context-aware office assistant. In: Proceedings of the 5th international conference on intelligent user interfaces. 2000. pp 276–279.

[75]

Yang M, Tu W, Qu Q, Zhao Z, Chen X, and Zhu J Personalized response generation by dual-learning based domain adaptation Neural Netw Off J Int Neural Netw Soc 2018 103 72-82

[76]

Zhang S, Dinan E, Urbanek J, Szlam A, Kiela D, Weston J. Personalizing dialogue agents: I have a dog, do you have pets too? In: Proceedings of the 56th annual meeting of the association for computational linguistics (volume 1: long papers). 2018. pp 2204–2213.

[77]

Zhang W, Liu T, Wang Y, Zhu Q. Neural personalized response generation as domain adaptation. 2019. http://arxiv.org/abs/1701.02073

[78]

Zhang Y, Sun S, Galley M, Chen Y-C, Brockett C, Gao X, Gao J, Liu J, Dolan B. DialoGPT: large-scale generative pre-training for conversational response generation. 2020. http://arxiv.org/abs/1911.00536.

[79]

Zheng Y, Chen G, Huang M, Liu S, Zhu X. Personalized dialogue generation with diversified traits. 2019. http://arxiv.org/abs/1901.09672

[80]

Zhou L, Gao J, Li D, Shum H-Y. The design and implementation of XiaoIce, an empathetic social chatbot. 2019. http://arxiv.org/abs/1812.08989

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cover image SN Computer Science

SN Computer Science Volume 4, Issue 5

Jun 2023

3596 pages

EISSN:2661-8907

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© The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

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Publication History

Published: 30 August 2023

Accepted: 26 June 2023

Received: 03 February 2023

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Sekulić ILu LBedi NCrestani FSakai TIshita EOhshima HHasibi FMao JJose J(2024)Simulating Conversational Search Users with Parameterized BehaviorProceedings of the 2024 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region10.1145/3673791.3698425(72-81)Online publication date: 8-Dec-2024
https://dl.acm.org/doi/10.1145/3673791.3698425
Zhao ZTheune MSrivastava SBraun D(2024)Exploring Lexical Alignment in a Price Bargain ChatbotProceedings of the 6th ACM Conference on Conversational User Interfaces10.1145/3640794.3665576(1-7)Online publication date: 8-Jul-2024
https://dl.acm.org/doi/10.1145/3640794.3665576
Kovacevic NBoschung THolz CGross MWampfler R(2024)Chatbots With Attitude: Enhancing Chatbot Interactions Through Dynamic Personality InfusionProceedings of the 6th ACM Conference on Conversational User Interfaces10.1145/3640794.3665543(1-16)Online publication date: 8-Jul-2024
https://dl.acm.org/doi/10.1145/3640794.3665543
He LBraggaar ABasar EKrahmer EAntheunis MWiers R(2024)Exploring User Engagement Through an Interaction Lens: What Textual Cues Can Tell Us about Human-Chatbot InteractionsProceedings of the 6th ACM Conference on Conversational User Interfaces10.1145/3640794.3665536(1-14)Online publication date: 8-Jul-2024
https://dl.acm.org/doi/10.1145/3640794.3665536
Cocchi ABosse Tvan Pinxteren M(2023)Should Conversational Agents Care About Our Gender Identity?Chatbot Research and Design10.1007/978-3-031-54975-5_9(149-163)Online publication date: 22-Nov-2023
https://dl.acm.org/doi/10.1007/978-3-031-54975-5_9

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