Skip to main content

You Get What You Chat: Using Conversations to Personalize Search-Based Recommendations

Part of the Lecture Notes in Computer Science book series (LNISA,volume 12656)

Abstract

Prior work on personalized recommendations has focused on exploiting explicit signals from user-specific queries, clicks, likes and ratings. This paper investigates tapping into a different source of implicit signals of interests and tastes: online chats between users. The paper develops an expressive model and effective methods for personalizing search-based entity recommendations. User models derived from chats augment different methods for re-ranking entity answers for medium-grained queries. The paper presents specific techniques to enhance the user models by capturing domain-specific vocabularies and by entity-based expansion. Experiments are based on a collection of online chats from a controlled user study covering three domains: books, travel, food. We evaluate different configurations and compare chat-based user models against concise user profiles from questionnaires. Overall, these two variants perform on par in terms of NCDG@20, but each has advantages on certain domains.

Keywords

  • Search-based recommendation
  • User modeling
  • Personalization

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-72113-8_14
  • Chapter length: 17 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   109.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-72113-8
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   149.99
Price excludes VAT (USA)
Fig. 1.

References

  1. Agichtein, E., Brill, E., Dumais, S., Ragno, R.: Learning user interaction models for predicting web search result preferences. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM (2006)

    Google Scholar 

  2. Ai, Q., Zhang, Y., Bi, K., Chen, X., Croft, W.B.: Learning a hierarchical embedding model for personalized product search. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2017. ACM (2017)

    Google Scholar 

  3. Bai, X., Wang, M., Lee, I., Yang, Z., Kong, X., Xia, F.: Scientific paper recommendation: a survey. IEEE Access 7, 9324–9339 (2019)

    CrossRef  Google Scholar 

  4. Balog, K.: Entity-Oriented Search. INRE, vol. 39. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93935-3

    CrossRef  Google Scholar 

  5. Balog, K., Kenter, T.: Personal knowledge graphs: a research agenda. In: Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval, ICTIR (2019)

    Google Scholar 

  6. Balog, K., Radlinski, F., Arakelyan, S.: Transparent, scrutable and explainable user models for personalized recommendation. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR (2019)

    Google Scholar 

  7. Bauman, K., Liu, B., Tuzhilin, A.: Aspect based recommendations: recommending items with the most valuable aspects based on user reviews. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM (2017)

    Google Scholar 

  8. Bennett, P.N., Radlinski, F., White, R.W., Yilmaz, E.: Inferring and using location metadata to personalize web search. In: Proceeding of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval (2011)

    Google Scholar 

  9. Bennett, P.N., Shokouhi, M., Caruana, R.: Implicit preference labels for learning highly selective personalized rankers. In: Proceedings of the 2015 International Conference on the Theory of Information Retrieval, ICTIR (2015)

    Google Scholar 

  10. Bennett, P.N., et al.: Modeling the impact of short- and long-term behavior on search personalization. In: The 35th International ACM SIGIR Conference on Research and Development in Information Retrieval (2012)

    Google Scholar 

  11. Biancalana, C., Gasparetti, F., Micarelli, A., Sansonetti, G.: Social semantic query expansion. ACM TIST 4 (2013)

    Google Scholar 

  12. Cai, F., de Rijke, M.: Selectively personalizing query auto-completion. In: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval (2016)

    Google Scholar 

  13. Carpineto, C., Romano, G.: A survey of automatic query expansion in information retrieval. ACM Comput. Surv. 44 (2012)

    Google Scholar 

  14. Chen, L., Chen, G., Wang, F.: Recommender systems based on user reviews: the state of the art. User Model. User-Adapted Interact. 25(2), 99–154 (2015). https://doi.org/10.1007/s11257-015-9155-5

    CrossRef  Google Scholar 

  15. Chirita, P., Firan, C.S., Nejdl, W.: Personalized query expansion for the web. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (2007)

    Google Scholar 

  16. Dietz, L.: ENT rank: retrieving entities for topical information needs through entity-neighbor-text relations. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (2019)

    Google Scholar 

  17. Ghorab, M.R., Zhou, D., O’Connor, A., Wade, V.: Personalised information retrieval: survey and classification. User Model. User-Adapted Interact. 23, 381–443 (2013). https://doi.org/10.1007/s11257-012-9124-1

    CrossRef  Google Scholar 

  18. Guo, Q., et al.: A survey on knowledge graph-based recommender systems. CoRR (2020)

    Google Scholar 

  19. Jiang, J., et al.: End-to-end deep attentive personalized item retrieval for online content-sharing platforms. In: WWW 2020: The Web Conference 2020. ACM/IW3C2 (2020)

    Google Scholar 

  20. Klašnja-Milićević, A., Vesin, B., Ivanović, M., Budimac, Z., Jain, L.C.: E-Learning Systems. ISRL, vol. 112. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-41163-7

    CrossRef  Google Scholar 

  21. Koolen, M., et al.: Overview of the CLEF 2016 social book search lab. In: Fuhr, N., et al. (eds.) CLEF 2016. LNCS, vol. 9822, pp. 351–370. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-44564-9_29

    CrossRef  Google Scholar 

  22. Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42, 30–37 (2009)

    CrossRef  Google Scholar 

  23. Kuzi, S., Carmel, D., Libov, A., Raviv, A.: Query expansion for email search. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM (2017)

    Google Scholar 

  24. Kuzi, S., Shtok, A., Kurland, O.: Query expansion using word embeddings. In: Proceedings of the 25th ACM International Conference on Information and Knowledge Management, CIKM (2016)

    Google Scholar 

  25. Lalmas, M.: Personalizing the listening experience (invited talk), slides at https://prs2019.splashthat.com/

  26. Lei, W., He, X., de Rijke, M., Chua, T.: Conversational recommendation: formulation, methods, and evaluation. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM (2020)

    Google Scholar 

  27. Li, X., Chen, Y., Pettit, B., de Rijke, M.: Personalised reranking of paper recommendations using paper content and user behavior. ACM Trans. Inf. Syst. 37 (2019)

    Google Scholar 

  28. Liu, J., Dolan, P., Pedersen, E.R.: Personalized news recommendation based on click behavior. In: Proceedings of the 15th International Conference on Intelligent User Interfaces, IUI. ACM (2010)

    Google Scholar 

  29. Matthijs, N., Radlinski, F.: Personalizing web search using long term browsing history. In: Proceedings of the Forth International Conference on Web Search and Web Data Mining, WSDM (2011)

    Google Scholar 

  30. Menk, A., Sebastia, L., Ferreira, R.: Recommendation systems for tourism based on social networks: a survey. CoRR (2019)

    Google Scholar 

  31. Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems NIPS (2013)

    Google Scholar 

  32. Ricci, F., Rokach, L., Shapira, B. (eds.): Recommender Systems Handbook. Springer, Boston (2015). https://doi.org/10.1007/978-1-4899-7637-6

    CrossRef  MATH  Google Scholar 

  33. Robertson, S.E., Zaragoza, H.: The probabilistic relevance framework: BM25 and beyond. Found. Trends Inf. Retrievel 3, 333–389 (2009)

    CrossRef  Google Scholar 

  34. Sachdeva, N., McAuley, J.: How useful are reviews for recommendation? A critical review and potential improvements. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2020. Association for Computing Machinery (2020)

    Google Scholar 

  35. Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web (2001)

    Google Scholar 

  36. Schnabel, T., Amershi, S., Bennett, P.N., Bailey, P., Joachims, T.: The impact of more transparent interfaces on behavior in personalized recommendation. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM (2020)

    Google Scholar 

  37. Shen, X., Tan, B., Zhai, C.: Implicit user modeling for personalized search. In: Proceedings of the 2005 ACM CIKM International Conference on Information and Knowledge Management (2005)

    Google Scholar 

  38. Shokouhi, M.: Learning to personalize query auto-completion. In: The 36th International ACM SIGIR Conference on Research and Development in Information Retrieval (2013)

    Google Scholar 

  39. Sontag, D., Collins-Thompson, K., Bennett, P.N., White, R.W., Dumais, S.T., Billerbeck, B.: Probabilistic models for personalizing web search. In: Proceedings of the Fifth International Conference on Web Search and Web Data Mining, WSDM (2012)

    Google Scholar 

  40. Stratigi, M., Li, X., Stefanidis, K., Zhang, Z.: Ratings vs. reviews in recommender systems: a case study on the amazon movies dataset. ADBIS 2019. CCIS, vol. 1064, pp. 68–76. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30278-8_9

    CrossRef  Google Scholar 

  41. Teevan, J., Dumais, S.T., Horvitz, E.: Personalizing search via automated analysis of interests and activities (SIGIR test-of-time award 2017). In: Proceedings of the 28th Annual International ACM SIGIR (2005)

    Google Scholar 

  42. Teevan, J., Dumais, S.T., Liebling, D.J.: To personalize or not to personalize: modeling queries with variation in user intent. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (2008)

    Google Scholar 

  43. Torbati, G.H., Yates, A., Weikum, G.: Personalized entity search by sparse and scrutable user profiles. In: Proceedings of the 2020 Conference on Human Information Interaction and Retrieval, CHIIR 2020. Association for Computing Machinery (2020)

    Google Scholar 

  44. Wu, F., et al.: MIND: a large-scale dataset for news recommendation. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL. Association for Computational Linguistics (2020)

    Google Scholar 

  45. Wu, L., Grbovic, M.: How Airbnb tells you will enjoy sunset sailing in Barcelona? Recommendation in a two-sided travel marketplace. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM (2020)

    Google Scholar 

  46. Xiong, C., Dai, Z., Callan, J., Liu, Z., Power, R.: End-to-end neural ad-hoc ranking with kernel pooling. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM (2017)

    Google Scholar 

  47. Yamada, I., Asai, A., Shindo, H., Takeda, H., Takefuji, Y.: Wikipedia2Vec: an optimized tool for learning embeddings of words and entities from Wikipedia. CoRR (2018)

    Google Scholar 

  48. Yamada, I., Shindo, H., Takeda, H., Takefuji, Y.: Joint learning of the embedding of words and entities for named entity disambiguation. In: Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning, CoNLL. ACL (2016)

    Google Scholar 

  49. Yao, J., Dou, Z., Wen, J.: Employing personal word embeddings for personalized search. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM (2020)

    Google Scholar 

  50. Zhai, C.: Statistical language models for information retrieval: a critical review. Found. Trends Inf. Retrieval (2008)

    Google Scholar 

  51. Zhang, S., Yao, L., Sun, A., Tay, Y.: Deep learning based recommender system: a survey and new perspectives. ACM Comput. Surv. 52, 1–38 (2019)

    Google Scholar 

  52. Zhao, Z., et al.: Recommending what video to watch next: a multitask ranking system. In: Proceedings of the 13th ACM Conference on Recommender Systems, RecSys. ACM (2019)

    Google Scholar 

  53. Zhou, D., Wu, X., Zhao, W., Lawless, S., Liu, J.: Query expansion with enriched user profiles for personalized search utilizing folksonomy data. IEEE Trans. Knowl. Data Eng. 29, 1536–1548 (2017)

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ghazaleh H. Torbati .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Torbati, G.H., Yates, A., Weikum, G. (2021). You Get What You Chat: Using Conversations to Personalize Search-Based Recommendations. In: Hiemstra, D., Moens, MF., Mothe, J., Perego, R., Potthast, M., Sebastiani, F. (eds) Advances in Information Retrieval. ECIR 2021. Lecture Notes in Computer Science(), vol 12656. Springer, Cham. https://doi.org/10.1007/978-3-030-72113-8_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-72113-8_14

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-72112-1

  • Online ISBN: 978-3-030-72113-8

  • eBook Packages: Computer ScienceComputer Science (R0)