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Investigating meta-intents: user interaction preferences in conversational recommender systems

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Abstract

We propose the concept of meta-intents (MI) which represent high-level user preferences related to the interaction styles and decision-making support in conversational recommender systems (CRS). For determining meta-intent factors, we conduct an exploratory study with 212 participants, and a confirmatory study with 394 participants, from this, we obtain a reliable and stable MI questionnaire with 22 items corresponding to seven concepts. These seven factors cover important interaction preferences. We find that MI can be linked to users’ general decision-making style and can thus be instrumental in translating general psychological user characteristics into more concrete design guidance for CRS. We further explore the correlations between MI and user interactions in real CRS scenarios. For this purpose, we propose a CRS framework and implement a chatbot in the smartphone domain to collect real interaction data. We conduct an online study with 99 participants and an interview study in the laboratory with 19 participants. Regarding the impact of MI on interaction behavior, we observe that dialog-initiation, efficiency-orientation and interest in details have a significant and direct impact on interaction behavior. Based on the findings, we provide some heuristic suggestions for leveraging MI in the design and adaptation of CRS. Our studies show the usefulness of the meta-intents concept for bridging the gap between general user characteristics and the concrete design of CRS and indicate their potential for personalizing the interaction in real-time conversations.

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Notes

  1. https://github.com/YuanMa2018/chatbot-for-meta-intents.

  2. https://www.prolific.co.

  3. The dataset will be made available for noncommercial research purposes upon request.

  4. https://cloud.google.com/dialogflow.

References

  • Atas, M., Felfernig, A., Polat-Erdeniz, S., Popescu, A., Tran, T.N.T., Uta, M.: Towards psychology-aware preference construction in recommender systems: overview and research issues. J. Intell. Inf. Syst. 57, 1–23 (2021)

    Article  Google Scholar 

  • Bollen, D., Knijnenburg, B.P., Willemsen, M.C., Graus, M.: Understanding choice overload in recommender systems. In: Proceedings of the Fourth ACM Conference on Recommender Systems, pp. 63–70 (2010)

  • Bondarenko, A., Ajjour, Y., Dittmar, V., Homann, N., Braslavski, P., Hagen, M.: Towards understanding and answering comparative questions. In: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, pp. 66–74 (2022)

  • Bondarenko, A., Braslavski, P., Völske, M., Aly, R., Fröbe, M., Panchenko, A., Biemann, C., Stein, B., Hagen, M.: Comparative web search questions. In: Proceedings of the 13th International Conference on Web Search and Data Mining, pp. 52–60 (2020)

  • Büttner, O.B., Florack, A., Göritz, A.S.: How shopping orientation influences the effectiveness of monetary and nonmonetary promotions. Eur. J. Mark. (2015)

  • Büttner, O.B., Florack, A., Göritz, A.S.: Shopping orientation as a stable consumer disposition and its influence on consumers’ evaluations of retailer communication. Eur. J. Mark. (2014)

  • Büttner, O.B., Florack, A., Göritz, A.S.: Shopping orientation and mindsets: How motivation influences consumer information processing during shopping. Psychol. Mark. 30(9), 779–793 (2013)

    Article  Google Scholar 

  • Cai, W., Chen, L.: Predicting user intents and satisfaction with dialogue-based conversational recommendations. In: Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization. UMAP ’20, pp. 33–42. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3340631.3394856

  • Cai, W., Chen, L.: Towards a taxonomy of user feedback intents for conversational recommendations. In: RecSys (Late-Breaking Results), pp. 51–55 (2019)

  • Cai, W., Jin, Y., Chen, L.: Critiquing for music exploration in conversational recommender systems. In: 26th International Conference on Intelligent User Interfaces, pp. 480–490 (2021)

  • Chen, L., Pu, P.: Critiquing-based recommenders: survey and emerging trends. User Model. User-Adap. Inter. 22(1), 125–150 (2012)

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Christakopoulou, K., Radlinski, F., Hofmann, K.: Towards conversational recommender systems. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 815–824 (2016)

  • Dean, S., Rich, S., Recht, B.: Recommendations and user agency: the reachability of collaboratively-filtered information. In: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. FAT* ’20, pp. 436–445. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3351095.3372866

  • Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)

  • Edwards, J., Janssen, C., Gould, S., Cowan, B.R.: Eliciting spoken interruptions to inform proactive speech agent design. In: Proceedings of the 3rd Conference on Conversational User Interfaces, pp. 1–12 (2021)

  • Fu, Z., Xian, Y., Zhang, Y., Zhang, Y.: Tutorial on conversational recommendation systems. In: Fourteenth ACM Conference on Recommender Systems. RecSys ’20, pp. 751–753. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3383313.3411548

  • Fu, Z., Xian, Y., Zhang, Y., Zhang, Y.: WSDM 2021 Tutorial on conversational recommendation systems. WSDM ’21, pp. 1134–1136. Association for Computing Machinery, New York, NY, USA (2021). https://doi.org/10.1145/3437963.3441661

  • Gao, C., Lei, W., He, X., de Rijke, M., Chua, T.-S.: Advances and challenges in conversational recommender systems: a survey. AI Open 2, 100–126 (2021). https://doi.org/10.1016/j.aiopen.2021.06.002

    Article  Google Scholar 

  • Ghafurian, M., Budnarain, N., Hoey, J.: Role of emotions in perception of humanness of virtual agents. In: Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, pp. 1979–1981 (2019)

  • Gizaw, T.Z., Dong Jun, H., Oad, A.: Solving cold-start problem by combining personality traits and demographic attributes in a user based recommender system. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 7(5), 231–239 (2017)

    Article  Google Scholar 

  • Hamilton, K., Shih, S.-I., Mohammed, S.: The development and validation of the rational and intuitive decision styles scale. J. Pers. Assess. 98(5), 523–535 (2016). https://doi.org/10.1080/00223891.2015.1132426

    Article  Google Scholar 

  • Horvitz, E.: Principles of mixed-initiative user interfaces. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 159–166 (1999)

  • Jameson, A., Willemsen, M.C., Felfernig, A., Gemmis, M.d., Lops, P., Semeraro, G., Chen, L.: Human decision making and recommender systems. In: Recommender Systems Handbook, pp. 611–648. Springer, Boston, MA (2015). https://doi.org/10.1007/978-1-4899-7637-6_18

  • Jannach, D., Manzoor, A., Cai, W., Chen, L.: A survey on conversational recommender systems. ACM Comput. Surv. (2021). https://doi.org/10.1145/3453154

    Article  Google Scholar 

  • Jin, X., Zhou, Y., Mobasher, B.: A maximum entropy web recommendation system: combining collaborative and content features. In: Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, pp. 612–617 (2005)

  • Kaminskas, M., Bridge, D.: Diversity, serendipity, novelty, and coverage: a survey and empirical analysis of beyond-accuracy objectives in recommender systems. ACM Trans. Interact. Intell. Syst. 7(1), 1–42 (2016)

    Article  Google Scholar 

  • Kleemann, T., Loepp, B., Ziegler, J.: Towards multi-method support for product search and recommending. In: Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization, pp. 74–79 (2022). https://doi.org/10.1145/3511047.3536408

  • Kostric, I., Balog, K., Radlinski, F.: Soliciting user preferences in conversational recommender systems via usage-related questions. In: 15th ACM Conference on Recommender Systems. RecSys ’21, pp. 724–729 (2021)

  • Kulms, P., Kopp, S.: More human-likeness, more trust? the effect of anthropomorphism on self-reported and behavioral trust in continued and interdependent human-agent cooperation. In: Proceedings of Mensch und Computer 2019, pp. 31–42 (2019)

  • Kunaver, M., Požrl, T.: Diversity in recommender systems-a survey. Knowl.-Based Syst. 123, 154–162 (2017)

    Article  Google Scholar 

  • Lee, S., Moore, R., Ren, G.-J., Arar, R., Jiang, S.: Making personalized recommendation through conversation: Architecture design and recommendation methods. In: Workshops at the Thirty-Second AAAI Conference on Artificial Intelligence (2018)

  • Lei, W., He, X., Miao, Y., Wu, Q., Hong, R., Kan, M.-Y., Chua, T.-S.: Estimation-action-reflection: Towards deep interaction between conversational and recommender systems. In: Proceedings of the 13th International Conference on Web Search and Data Mining, pp. 304–312 (2020). https://doi.org/10.1145/3336191.3371769

  • Lex, E., Kowald, D., Seitlinger, P., Tran, T.N.T., Felfernig, A., Schedl, M.: Psychology-informed recommender systems. Found. Trends® Inf. Retr. 15(2), 134–242 (2021). https://doi.org/10.1561/1500000090

    Article  Google Scholar 

  • Li, Y., Pan, Q., Wang, S., Yang, T., Cambria, E.: A generative model for category text generation. Inf. Sci. 450, 301–315 (2018)

    Article  MathSciNet  Google Scholar 

  • Li, S., Lei, W., Wu, Q., He, X., Jiang, P., Chua, T.-S.: Seamlessly unifying attributes and items: Conversational recommendation for cold-start users. ACM Trans. Inf. Syst. (2021). https://doi.org/10.1145/3446427

    Article  Google Scholar 

  • Lika, B., Kolomvatsos, K., Hadjiefthymiades, S.: Facing the cold start problem in recommender systems. Expert Syst. Appl. 41(4), 2065–2073 (2014). https://doi.org/10.1016/j.eswa.2013.09.005

    Article  Google Scholar 

  • Luger, E., Sellen, A.: “ like having a really bad pa” the gulf between user expectation and experience of conversational agents. In: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, pp. 5286–5297 (2016)

  • Luo, K., Sanner, S., Wu, G., Li, H., Yang, H.: Latent linear critiquing for conversational recommender systems. In: Proceedings of The Web Conference 2020, pp. 2535–2541 (2020)

  • Ma, Y., Donkers, T., Kleemann, T., Ziegler, J.: An instrument for measuring users’ meta-intents. In: Proceedings of the 2023 Conference on Human Information Interaction and Retrieval. CHIIR ’23, pp. 290–302. Association for Computing Machinery, New York, NY, USA (2023). https://doi.org/10.1145/3576840.3578317

  • Ma, Y., Donkers, T., Kleemann, T., Ziegler, J.: Meta-intents in conversational recommender systems. In: Proceedings of the 4th Edition of Knowledge-aware and Conversational Recommender Systems Workshop Co-located with 16th ACM Conference on Recommender Systems (RecSys 2022), pp. 81–90 (2022)

  • Ma, Y., Kleemann, T., Ziegler, J.: Mixed-modality interaction in conversational recommender systems. In: Interfaces and Human Decision Making for Recommender Systems 2021: Proceedings of the 8th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems, pp. 21–37 (2021)

  • Ma, Y., Kleemann, T., Ziegler, J.: Psychological user characteristics and meta-intents in a conversational product advisor. In: Proceedings of the 9th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems Co-located with 16th ACM Conference on Recommender Systems (RecSys 2022), pp. 18–32 (2022)

  • Ma, Y., Ziegler, J.: Initiative transfer in conversational recommender systems. In: Proceedings of the 17th ACM Conference on Recommender Systems, pp. 978–984 (2023). https://doi.org/10.1145/3604915.3608858

  • Ma, Y., Ziegler, J.: The effect of proactive cues on the use of decision aids in conversational recommender systems. In: Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization (2024). https://doi.org/10.1145/3631700.3665186

  • Millecamp, M., Htun, N.N., Jin, Y., Verbert, K.: Controlling spotify recommendations: effects of personal characteristics on music recommender user interfaces. In: Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization. UMAP ’18, pp. 101–109. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3209219.3209223

  • Narducci, F., Basile, P., Iovine, A., Gemmis, M., Lops, P., Semeraro, G., et al.: A domain-independent framework for building conversational recommender systems. In: KaRS@ RecSys, pp. 29–34 (2018)

  • Oshikawa, R., Pindur, K., Iba, H.: Evolutionary generative adversarial networks for sentence generation from keyword. In: 2022 IEEE World Conference on Applied Intelligence and Computing (AIC), pp. 201–209 (2022). IEEE

  • Peer, E., Brandimarte, L., Samat, S., Acquisti, A.: Beyond the Turk: alternative platforms for crowdsourcing behavioral research. J. Exp. Soc. Psychol. 70, 153–163 (2017). https://doi.org/10.1016/j.jesp.2017.01.006

    Article  Google Scholar 

  • Pu, P., Faltings, B.: Decision tradeoff using example-critiquing and constraint programming. Constraints 9(4), 289–310 (2004)

    Article  Google Scholar 

  • Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. OpenAI blog 1(8), 9 (2019)

    Google Scholar 

  • Radlinski, F., Craswell, N.: A theoretical framework for conversational search. In: Proceedings of the 2017 Conference on Conference Human Information Interaction and Retrieval, pp. 117–126 (2017)

  • Reicherts, L., Zargham, N., Bonfert, M., Rogers, Y., Malaka, R.: May i interrupt? Diverging opinions on proactive smart speakers. In: Proceedings of the 3rd Conference on Conversational User Interfaces, pp. 1–10 (2021)

  • Rosa, G.H., Papa, J.P.: A survey on text generation using generative adversarial networks. Pattern Recogn. 119, 108098 (2021)

    Article  Google Scholar 

  • Schemmer, M., Hemmer, P., Nitsche, M., Kühl, N., Vössing, M.: A meta-analysis on the utility of explainable artificial intelligence in human-AI decision-making. arXiv preprint arXiv:2205.05126 (2022)

  • Schildwächter, M., Bondarenko, A., Zenker, J., Hagen, M., Biemann, C., Panchenko, A.: Answering comparative questions: Better than ten-blue-links? In: Proceedings of the 2019 Conference on Human Information Interaction and Retrieval, pp. 361–365 (2019)

  • Skjuve, M., Haugstveit, I.M., Følstad, A., Brandtzaeg, P.: Help! is my chatbot falling into the uncanny valley? an empirical study of user experience in human-chatbot interaction. Hum. Technol. 15(1), 30 (2019)

    Article  Google Scholar 

  • Sun, Y., Zhang, Y.: Conversational recommender system. In: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. SIGIR ’18, pp. 235–244. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3209978.3210002

  • Thompson, C.A., Goker, M.H., Langley, P.: A personalized system for conversational recommendations. J. Artif. Intell. Res. 21, 393–428 (2004)

    Article  Google Scholar 

  • Tkalcic, M., Chen, L.: Personality and Recommender Systems, pp. 715–739. Springer, Boston (2015). https://doi.org/10.1007/978-1-4899-7637-6_21

    Book  Google Scholar 

  • Wärnestål, P.: Modeling a dialogue strategy for personalized movie recommendations. In: Beyond Personalization Workshop, pp. 77–82 (2005)

  • Wei, J., Dingler, T., Kostakos, V.: Developing the proactive speaker prototype based on google home. In: Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems, pp. 1–6 (2021)

  • Widiyaningtyas, T., Hidayah, I., Adji, T.B.: User profile correlation-based similarity (UPCSim) algorithm in movie recommendation system. J. Big Data 8, 1–21 (2021)

    Article  Google Scholar 

  • Xiao, B., Benbasat, I.: E-commerce product recommendation agents: use, characteristics, and impact. MIS Q. 31(1), 137–209 (2007)

    Article  Google Scholar 

  • Xie, Z., Yu, T., Zhao, C., Li, S.: Comparison-based conversational recommender system with relative bandit feedback. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1400–1409 (2021)

  • Yan, Z., Duan, N., Chen, P., Zhou, M., Zhou, J., Li, Z.: Building task-oriented dialogue systems for online shopping. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017)

  • Yan, R., Li, J., Yu, Z., et al.: Deep learning for dialogue systems: Chit-chat and beyond. Found. Trends® Inf. Retr. 15(5), 417–589 (2022)

    Article  Google Scholar 

  • Zanker, M., Jessenitschnig, M., Schmid, W.: Preference reasoning with soft constraints in constraint-based recommender systems. Constraints 15(4), 574–595 (2010)

    Article  MathSciNet  Google Scholar 

  • Zargham, N., Reicherts, L., Bonfert, M., Völkel, S.T., Schöning, J., Malaka, R., Rogers, Y.: Understanding circumstances for desirable proactive behaviour of voice assistants: The proactivity dilemma. In: Proceedings of the 4th Conference on Conversational User Interfaces, pp. 1–14 (2022)

  • Zhang, Y., Chen, X., Ai, Q., Yang, L., Croft, W.B.: Towards conversational search and recommendation: System ask, user respond. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management. CIKM ’18, pp. 177–186. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3269206.3271776

  • Zhang, X., Xie, H., Li, H., CS Lui, J.: Conversational contextual bandit: algorithm and application. In: Proceedings of the Web Conference 2020, pp. 662–672 (2020)

  • Zhang, X., Xie, H., Li, H., Lui, J.C.S.: Toward building conversational recommender systems: a contextual bandit approach. CoRR abs/1906.01219 (2019) arXiv:1906.01219

  • Zhang, J.: Anchoring effects of recommender systems. In: Proceedings of the 5th ACM Conference on Recommender Systems. RecSys ’11, pp. 375–378. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2043932.2044010

  • Zhang, Y., Chen, X.: Explainable recommendation: a survey and new perspectives. Found. Trends Inf. Retr. 14(1), 1–101 (2020)

    Article  Google Scholar 

  • Zhou, K., Zhao, W.X., Bian, S., Zhou, Y., Wen, J.-R., Yu, J.: Improving conversational recommender systems via knowledge graph based semantic fusion. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1006–1014 (2020). https://doi.org/10.1145/3394486.3403143

  • Zhou, K., Zhou, Y., Zhao, W.X., Wang, X., Wen, J.-R.: Towards topic-guided conversational recommender system. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 4128–4139. International Committee on Computational Linguistics, Barcelona, Spain (Online) (2020). https://doi.org/10.18653/v1/2020.coling-main.365

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  1. Interactive System Group, University of Duisburg-Essen, Forsthausweg 2, 47057, Duisburg, NRW, Germany

    Yuan Ma & Jürgen Ziegler

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Yuan Ma wrote the main manuscript text and Jürgen Ziegler reviewed and updated the manuscript.

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Ma, Y., Ziegler, J. Investigating meta-intents: user interaction preferences in conversational recommender systems. User Model User-Adap Inter (2024). https://doi.org/10.1007/s11257-024-09411-3

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