Stationary Algorithmic Balancing For Dynamic Email Re-Ranking Problem
Pages 4527 - 4538
Abstract
Email platforms need to generate personalized rankings of emails that satisfy user preferences, which may vary over time. We approach this as a recommendation problem based on three criteria: closeness (how relevant the sender and topic are to the user), timeliness (how recent the email is), and conciseness (how brief the email is). We propose MOSR (Multi-Objective Stationary Recommender), a novel online algorithm that uses an adaptive control model to dynamically balance these criteria and adapt to preference changes. We evaluate MOSR on the Enron Email Dataset, a large collection of real emails, and compare it with other baselines. The results show that MOSR achieves better performance, especially under non-stationary preferences, where users value different criteria more or less over time. We also test MOSR's robustness on a smaller down-sampled dataset that exhibits high variance in email characteristics, and show that it maintains stable rankings across different samples. Our work offers novel insights into how to design email re-ranking systems that account for multiple objectives impacting user satisfaction.
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References
[1]
Douglas Aberdeen, Ondrey Pacovsky, and Andrew Slater. 2010. The learning behind gmail priority inbox. (2010).
[2]
Naresh Kumar Agarwal and Wenqing Lu. 2020. Response to non-response: How people react when their smartphone messages and calls are ignored. Proceedings of the Association for Information Science and Technology, Vol. 57, 1 (2020), e260.
[3]
Kumaripaba Athukorala, Alan Medlar, Antti Oulasvirta, Giulio Jacucci, and Dorota Glowacka. 2016. Beyond relevance: Adapting exploration/exploitation in information retrieval. In Proceedings of the 21st international conference on intelligent user interfaces. 359--369.
[4]
Manas Bedekar, Aditi Deodhar, and Kaustubh Sakhare. 2021. Context-Based Email Ranking System for Enterprise. In 2021 IEEE 2nd International Conference on Technology, Engineering, Management for Societal impact using Marketing, Entrepreneurship and Talent (TEMSMET). IEEE, 1--5.
[5]
Fabiano M Belém, Carolina S Batista, Rodrygo LT Santos, Jussara M Almeida, and Marcos A Goncc alves. 2016. Beyond relevance: explicitly promoting novelty and diversity in tag recommendation. ACM Transactions on Intelligent Systems and Technology (TIST), Vol. 7, 3 (2016), 1--34.
[6]
Victoria Bellotti, Nicolas Ducheneaut, Mark Howard, Ian Smith, and Rebecca E Grinter. 2005. Quality versus quantity: E-mail-centric task management and its relation with overload. Human-Computer Interaction, Vol. 20, 1--2 (2005), 89--138.
[7]
Christian Bird, Alex Gourley, Prem Devanbu, Michael Gertz, and Anand Swaminathan. 2006. Mining email social networks. In Proceedings of the 2006 international workshop on Mining software repositories. 137--143.
[8]
Fan-Kai Chou, Meng-Fen Chiang, Yi-Cheng Chen, and Wen-Chih Peng. 2014. Dynamic circle recommendation: A probabilistic model. In Advances in Knowledge Discovery and Data Mining: 18th Pacific-Asia Conference, PAKDD 2014, Tainan, Taiwan, May 13--16, 2014. Proceedings, Part II 18. Springer, 25--37.
[9]
Orsolya Csiszar. 2021. Ordered weighted averaging operators: A short review. IEEE Systems, Man, and Cybernetics Magazine, Vol. 7, 2 (2021), 4--12.
[10]
Laura Dabbish, Robert Kraut, Susan Fussell, and Sara Kiesler. 2004. To reply or not to reply: Predicting action on an email message. In Acm 2004 conference. citeseer.
[11]
Laura A Dabbish, Robert E Kraut, Susan Fussell, and Sara Kiesler. 2005. Understanding email use: predicting action on a message. In Proceedings of the SIGCHI conference on Human factors in computing systems. 691--700.
[12]
Mark Dredze, Tova Brooks, Josh Carroll, Joshua Magarick, John Blitzer, and Fernando Pereira. 2008. Intelligent email: Reply and attachment prediction. In Proceedings of the 13th international conference on Intelligent user interfaces. 321--324.
[13]
Yiwei Eva Feng. 2019. A Novel E-mail Reply Approach for E-mail Management System. Ph.,D. Dissertation. Auckland University of Technology.
[14]
Dietmar Jannach, Pearl Pu, Francesco Ricci, and Markus Zanker. 2021. Recommender systems: Past, present, future. Ai Magazine, Vol. 42, 3 (2021), 3--6.
[15]
Yuanchun Jiang, Jennifer Shang, and Yezheng Liu. 2010. Maximizing customer satisfaction through an online recommendation system: A novel associative classification model. Decision Support Systems, Vol. 48, 3 (2010), 470--479.
[16]
Bryan Klimt and Yiming Yang. 2004. The enron corpus: A new dataset for email classification research. In European conference on machine learning. Springer, 217--226.
[17]
Abdullah Konak, David W Coit, and Alice E Smith. 2006. Multi-objective optimization using genetic algorithms: A tutorial. Reliability engineering & system safety, Vol. 91, 9 (2006), 992--1007.
[18]
Jiayi Liu, Quan Yuan, Carl Yang, He Huang, Chaorui Zhang, and Philip S. Yu. 2019. DAPred: Dynamic Attention Location Prediction with Long-Short Term Movement Regularity.
[19]
Andrew McCallum, Xuerui Wang, and Andrés Corrada-Emmanuel. 2007. Topic and role discovery in social networks with experiments on enron and academic email. Journal of artificial intelligence research, Vol. 30 (2007), 249--272.
[20]
Rishabh Mehrotra. 2021. Algorithmic Balancing of Familiarity, Similarity, & Discovery in Music Recommendations. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 3996--4005.
[21]
Kumpati S Narendra and Jeyendran Balakrishnan. 1997. Adaptive control using multiple models. IEEE transactions on automatic control, Vol. 42, 2 (1997), 171--187.
[22]
Kumpati S Narendra and Cheng Xiang. 2000. Adaptive control of discrete-time systems using multiple models. IEEE Trans. Automat. Control, Vol. 45, 9 (2000), 1669--1686.
[23]
Rohit Sharma, Anjali Shishodia, Sachin Kamble, Angappa Gunasekaran, and Amine Belhadi. 2020. Agriculture supply chain risks and COVID-19: mitigation strategies and implications for the practitioners. International Journal of Logistics Research and Applications (2020), 1--27.
[24]
Ayodele Taiwo and Zhou Shikun. 2009. Applying machine learning techniques for e-mail management: solution with intelligent e-mail reply prediction. Journal of Engineering and Technology Research, Vol. 1, 7 (2009), 143--151.
[25]
David Tuckett, Robert Elliot Smith, and Rickard Nyman. 2014. Tracking phantastic objects: A computer algorithmic investigation of narrative evolution in unstructured data sources. Social Networks, Vol. 38 (2014), 121--133.
[26]
Steve Whittaker and Candace Sidner. 1996. Email overload: exploring personal information management of email. In Proceedings of the SIGCHI conference on Human factors in computing systems. 276--283.
[27]
Ryan Williams. 2009. Finding paths of length k in O?(2k) time. Inform. Process. Lett., Vol. 109, 6 (2009), 315--318.
[28]
Ronald R Yager. 1988. On ordered weighted averaging aggregation operators in multicriteria decisionmaking. IEEE Transactions on systems, Man, and Cybernetics, Vol. 18, 1 (1988), 183--190.
[29]
Ronald R Yager. 1996. Quantifier guided aggregation using OWA operators. International Journal of Intelligent Systems, Vol. 11, 1 (1996), 49--73.
[30]
Liu Yang, Susan T Dumais, Paul N Bennett, and Ahmed Hassan Awadallah. 2017. Characterizing and predicting enterprise email reply behavior. In Proceedings of the 40th international acm sigir conference on research and development in information retrieval. 235--244.
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August 2023
5996 pages
ISBN:9798400701030
DOI:10.1145/3580305
- General Chairs:
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- Yizhou Sun,
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- Jieping Ye
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Published: 04 August 2023
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