Does the Long Tail of Context Exist and Matter? The Case of Dialogue-based Recommender Systems
Pages 273 - 278
Abstract
Context has been an important topic in recommender systems over the past two decades. Most of the prior CARS papers manually selected and considered only a few crucial contextual variables in an application, such as time, location, and company of a person. This prior work demonstrated significant recommendation performance improvements when various CARS-based methods have been deployed in numerous applications. In this paper, we study “context-rich” applications dealing with a large variety of different types of contexts. We demonstrate that supporting only a few of the most important contextual variables that could be manually identified, although useful, is not sufficient. In particular, we develop an approach to extract a large number of contextual variables for the dialogue-based recommender systems. In our study, we processed dialogues of bank managers with their clients and managed to identify over two hundred types of contextual variables forming the Long Tail of Context (LTC). We empirically demonstrate that LTC matters, and using all these contextual variables from the Long Tail leads to better recommendation performance.
Supplemental Material
PDF File
Additional plots with the results for all considered bank products.
- Download
- 637.75 KB
References
[1]
Silvana Aciar. 2010. Mining Context Information from Consumer’s Reviews. In Workshop on Context-Aware Recommender System. ACM.
[2]
Gediminas Adomavicius, Konstantin Bauman, Alexander Tuzhilin, and Moshe Unger. 2022. Context-Aware Recommender Systems: From Foundations to Recent DevelopmentsContext-aware recommender systems. Springer US, New York, NY, 211–250. https://doi.org/10.1007/978-1-0716-2197-4_6
[3]
Gediminas Adomavicius, Ramesh Sankaranarayanan, Shahana Sen, and Alexander Tuzhilin. 2005. Incorporating Contextual Information in Recommender Systems Using a Multidimensional Approach. ACM Trans. Inf. Syst. 23, 1 (jan 2005), 103–145. https://doi.org/10.1145/1055709.1055714
[4]
Xavier Amatriain, Nuria Oliver, Josep M Pujol, 2011. Data mining methods for recommender systems. In Recommender systems handbook. Springer, 39–71.
[5]
Dmitrii Babaev, Nikita Ovsov, Ivan Kireev, Maria Ivanova, Gleb Gusev, Ivan Nazarov, and Alexander Tuzhilin. 2022. CoLES: Contrastive Learning for Event Sequences with Self-Supervision. In Proceedings of the 2022 International Conference on Management of Data (Philadelphia, PA, USA) (SIGMOD ’22). Association for Computing Machinery, New York, NY, USA, 1190–1199. https://doi.org/10.1145/3514221.3526129
[6]
Linas Baltrunas, Karen Church, Alexandros Karatzoglou, and Nuria Oliver. 2015. Frappe: Understanding the Usage and Perception of Mobile App Recommendations In-The-Wild. (2015). arxiv:1505.03014 [cs.IR]
[7]
Konstantin Bauman and Alexander Tuzhilin. 2022. Know Thy Context: Parsing Contextual Information from User Reviews for Recommendation Purposes. Information Systems Research 33, 1 (2022), 179–202.
[8]
Christopher M Bishop and Nasser M Nasrabadi. 2006. Pattern recognition and machine learning. Vol. 4. Springer.
[9]
Guanliang Chen and Li Chen. 2014. Recommendation Based on Contextual Opinions. In User Modeling, Adaptation, and Personalization, Vania Dimitrova, Tsvi Kuflik, David Chin, Francesco Ricci, Peter Dolog, and Geert-Jan Houben (Eds.). Springer International Publishing, Cham, 61–73.
[10]
Guanliang Chen and Li Chen. 2015. Augmenting Service Recommender Systems by Incorporating Contextual Opinions from User Reviews. User Modeling and User-Adapted Interaction 25, 3 (Aug. 2015), 295–329.
[11]
Ian Davidson and S. S. Ravi. 2005. Agglomerative Hierarchical Clustering with Constraints: Theoretical and Empirical Results(ECMLPKDD’05). Springer-Verlag, Berlin, Heidelberg, 59–70.
[12]
María del Carmen Rodríguez-Hernández and Sergio Ilarri. 2021. AI-based mobile context-aware recommender systems from an information management perspective: Progress and directions. Knowledge-Based Systems 215 (2021), 106740.
[13]
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2-7, 2019, Volume 1 (Long and Short Papers), Jill Burstein, Christy Doran, and Thamar Solorio (Eds.). Association for Computational Linguistics, 4171–4186. https://doi.org/10.18653/v1/n19-1423
[14]
Paul Dourish. 2004. What We Talk About when We Talk About Context. Personal Ubiquitous Comput. 8, 1 (Feb. 2004), 19–30.
[15]
Paolo Dragone, Rishabh Mehrotra, and Mounia Lalmas. 2019. Deriving User- and Content-Specific Rewards for Contextual Bandits. In The World Wide Web Conference (San Francisco, CA, USA) (WWW-19). Association for Computing Machinery, New York, NY, USA, 2680–2686.
[16]
Casper Hansen, Christian Hansen, Lucas Maystre, Rishabh Mehrotra, Brian Brost, Federico Tomasi, and Mounia Lalmas. 2020. Contextual and sequential user embeddings for large-scale music recommendation. In Fourteenth ACM Conference on Recommender Systems. 53–62.
[17]
Negar Hariri, Bamshad Mobasher, Robin Burke, and Yong Zheng. 2011. Context-Aware Recommendation Based On Review Mining. In Proceedings of the 9th Workshop on Intelligent Techniques for Web Personalization & Recommender Systems, ITWP@IJCAI 2011, Barcelona, Spain, July 16, 2011.
[18]
Dietmar Jannach, Ahtsham Manzoor, Wanling Cai, and Li Chen. 2021. A Survey on Conversational Recommender Systems. ACM Comput. Surv. 54, 5, Article 105 (may 2021), 36 pages. https://doi.org/10.1145/3453154
[19]
Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. 2017. Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems 30 (2017).
[20]
Yehuda Koren, Steffen Rendle, and Robert Bell. 2022. Advances in Collaborative Filtering. Springer US, New York, NY, 91–142. https://doi.org/10.1007/978-1-0716-2197-4_3
[21]
Yize Li, Jiazhong Nie, Yi Zhang, Bingqing Wang, Baoshi Yan, and Fuliang Weng. 2010. Contextual Recommendation Based on Text Mining. In Proceedings of the 23rd International Conference on Computational Linguistics: Posters (Beijing, China) (COLING ’10). Association for Computational Linguistics, Stroudsburg, PA, USA, 692–700.
[22]
Tariq Mahmood and Francesco Ricci. 2009. Improving Recommender Systems with Adaptive Conversational Strategies. In Proceedings of the 20th ACM Conference on Hypertext and Hypermedia (Torino, Italy) (HT ’09). Association for Computing Machinery, New York, NY, USA, 73–82. https://doi.org/10.1145/1557914.1557930
[23]
James McInerney, Benjamin Lacker, Samantha Hansen, Karl Higley, Hugues Bouchard, Alois Gruson, and Rishabh Mehrotra. 2018. Explore, Exploit, and Explain: Personalizing Explainable Recommendations with Bandits. In Proceedings of the 12th ACM Conference on Recommender Systems (Vancouver, British Columbia, Canada) (RecSys–18). Association for Computing Machinery, New York, NY, USA, 31–39.
[24]
Cataldo Musto, Marco de Gemmis, Pasquale Lops, Fedelucio Narducci, and Giovanni Semeraro. 2022. Semantics and content-based recommendations. In Recommender systems handbook. Springer, 251–298.
[25]
Ante Odic, Marko Tkalcic, Jurij F. Tasic, and Andrej Kosir. 2013. Predicting and Detecting the Relevant Contextual Information in a Movie-Recommender System. Interacting with Computers 25 (2013), 74–90.
[26]
Steffen Rendle. 2010. Factorization machines. In 2010 IEEE International conference on data mining. IEEE, 995–1000.
[27]
Peter J Rousseeuw. 1987. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of computational and applied mathematics 20 (1987), 53–65.
[28]
Erich Schubert, Jörg Sander, Martin Ester, Hans Peter Kriegel, and Xiaowei Xu. 2017. DBSCAN Revisited, Revisited: Why and How You Should (Still) Use DBSCAN. ACM Trans. Database Syst. 42, 3, Article 19 (jul 2017), 21 pages. https://doi.org/10.1145/3068335
[29]
C. Selvi and E. Sivasankar. 2019. An Efficient Context-Aware Music Recommendation Based on Emotion and Time Context. In Data Science and Big Data Analytics, Durgesh Kumar Mishra, Xin-She Yang, and Aynur Unal (Eds.). 215–228.
[30]
Cynthia A. Thompson, Mehmet H. Göker, and Pat Langley. 2004. A Personalized System for Conversational Recommendations. J. Artif. Int. Res. 21, 1 (mar 2004), 393–428.
[31]
Anton Vakhrushev, Alexander Ryzhkov, Maxim Savchenko, Dmitry Simakov, Rinchin Damdinov, and Alexander Tuzhilin. 2021. LightAutoML: AutoML Solution for a Large Financial Services Ecosystem. https://doi.org/10.48550/ARXIV.2109.01528
[32]
Norha M Villegas, Cristian Sánchez, Javier Díaz-Cely, and Gabriel Tamura. 2018. Characterizing context-aware recommender systems: A systematic literature review. Knowledge-Based Systems 140 (2018), 173–200.
[33]
Wenmin Wu, Jianli Zhao, Chunsheng Zhang, Fang Meng, Zeli Zhang, Yang Zhang, and Qiuxia Sun. 2017. Improving Performance of Tensor-based Context-aware Recommenders Using Bias Tensor Factorization with Context Feature Auto-encoding. Know.-Based Syst. 128, C (July 2017), 71–77.
[34]
Zhao Yan, Nan Duan, Peng Chen, Ming Zhou, Jianshe Zhou, and Zhoujun Li. 2017. Building Task-Oriented Dialogue Systems for Online Shopping. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (San Francisco, California, USA) (AAAI’17). AAAI Press, 4618–4625.
Index Terms
- Does the Long Tail of Context Exist and Matter? The Case of Dialogue-based Recommender Systems
Recommendations
Characterizing context-aware recommender systems
Context-aware recommender systems leverage the value of recommendations by exploiting context information that affects user preferences and situations, with the goal of recommending items that are really relevant to changing user needs. Despite the ...
Comparing context-aware recommender systems in terms of accuracy and diversity
Although the area of context-aware recommender systems (CARS) has made a significant progress over the last several years, the problem of comparing various contextual pre-filtering, post-filtering and contextual modeling methods remained fairly ...
Augmenting service recommender systems by incorporating contextual opinions from user reviews
Context-aware recommender systems have been widely investigated in both academia and industry because they can make recommendations based on a user's current context (e.g., location, time). However, most existing context-aware techniques only use ...
Comments
Information & Contributors
Information
Published In
June 2024
338 pages
ISBN:9798400704338
DOI:10.1145/3627043
Copyright © 2024 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 22 June 2024
Check for updates
Author Tags
Qualifiers
- Short-paper
- Research
- Refereed limited
Conference
UMAP '24: 32nd ACM Conference on User Modeling, Adaptation and Personalization
July 1 - 4, 2024
Cagliari, Italy
Acceptance Rates
Overall Acceptance Rate 162 of 633 submissions, 26%
Upcoming Conference
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 65Total Downloads
- Downloads (Last 12 months)65
- Downloads (Last 6 weeks)8
Reflects downloads up to 10 Nov 2024
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format