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Ranking Task in RAS: A Comparative Study of Learning to Rank Algorithms and Interleaving Methods

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Digital Technologies and Applications (ICDTA 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 454))

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Abstract

Relational Aggregated Search (RAS) is a paradigm that refers to a set of techniques for retrieving and aggregating information nuggets based on their relationships. The performance of a relational aggregated search is dedicated to how to retrieve an information nugget, and, how to present this information to users to fulfill their information need. Recent RAS research has focused only on information retrieval strategies. As consequence, there is a critical need to research strategies for aggregating and ranking information nuggets. In this paper, we focus on the recent issue, namely answering the question of how information is shown to the users. In addition, what are the most effective approaches for the ranking task? We use the ranked results from RAS. These ranked results include different content types (images, videos, news, maps, blogs, groups, and books, etc.). Thus, the focal challenge in the vertical results aggregation task is how to rank these heterogeneous results to obtain pertinent information. Our experimental results of the application of learning to rank algorithms and interleaving methods are shown in this study. From the evaluation results, we can affirm that LambdaMART proves its performance compared with the other; LambdaRank, MART, ListNet, and RankNet. Finally, we apply the interleaving approaches to satisfy the user's need by providing appropriate, organized, and most relevant results. Our experimental study proves that VI-TDA performs better than TDA in all steps of our tests.

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References

  1. Kopliku, A., Pinel-Sauvagnat, K., Boughanem, M.: Aggregated search: a new information retrieval paradigm. ACM Comput. Surv. 46(3), 1–31 (2014). https://doi.org/10.1145/2523817

    Article  Google Scholar 

  2. Lalmas, M.: Aggregated search. In: Melucci, M., Baeza-Yates, R. (eds.) Advanced Topics in Information Retrieval. The Information Retrieval Series, vol. 33, pp. 109–123. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-20946-8_5

    Chapter  Google Scholar 

  3. Kopliku, A.: Approaches to implement and evaluate aggregated search. Ph.D. thesis, Université de Toulouse, Université Toulouse III-Paul Sabatier (2011)

    Google Scholar 

  4. Shokouhi, M., Si, L.: Federated search. FNT Inf. Retrieval 5(1), 1–102 (2011). https://doi.org/10.1561/1500000010

    Article  Google Scholar 

  5. Yan, L.T.: Learning to Rank for Information Retrieval. Microsoft Research Asia, Sigma Center, Beijing (2009)

    Google Scholar 

  6. Arguello, J., Diaz, F., Callan, J.: Learning to aggregate vertical results into web search results. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management - CIKM 2011, Glasgow, Scotland, UK, p. 201 (2011). https://doi.org/10.1145/2063576.2063611

  7. Li, H.: Learning to rank for information retrieval and natural language processing. Synth. Lect. Hum. Lang. Technol. 4(1), 1–113 (2011). https://doi.org/10.2200/S00348ED1V01Y201104HLT012

    Article  Google Scholar 

  8. Liu, T.-Y.: Learning to rank for information retrieval. FNT Inf. Retrieval 3(3), 225–331 (2007). https://doi.org/10.1561/1500000016

    Article  Google Scholar 

  9. Burges, C.J.C.: From RankNet to LambdaRank to LambdaMART: an overview, 19 (2010)

    Google Scholar 

  10. Burges, C., et al.: Learning to rank using gradient descent. In: Proceedings of the 22nd International Conference on Machine Learning - ICML 2005, Bonn, Germany, pp. 89–96 (2005). https://doi.org/10.1145/1102351.1102363

  11. Ding, R.: Literature survey for Learning to rank. Computer and Information Science Department University of Delaware, p. 6 (2009)

    Google Scholar 

  12. Marek Modrý, Bc.: Learning to rank algorithms. Ph.D. thesis, Faculty of Electrical Engineering, Department of Cybernetics, Czech Technical University in Prague (2014)

    Google Scholar 

  13. Lasri, S., Nfaoui, E.H.: Aggregated search and interleaving methods: a survey. In: Proceedings of the International Conference on Big Data and Advanced Wireless Technologies - BDAW 2016, Blagoevgrad, Bulgaria, pp. 1–9 (2016). https://doi.org/10.1145/3010089.3010098

  14. Kharitonov, E., Macdonald, C., Serdyukov, P., Ounis, I.: Generalized team draft interleaving. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, Melbourne Australia, October 2015, pp. 773–782 (2015). https://doi.org/10.1145/2806416.2806477

  15. Chuklin, A., Schuth, A., Hofmann, K., Serdyukov, P., de Rijke, M.: Evaluating aggregated search using interleaving. In: Proceedings of the 22nd ACM International Conference on Information and Knowledge Management - CIKM 2013, San Francisco, California, USA, pp. 669–678 (2013). https://doi.org/10.1145/2505515.2505698

  16. Achsas, S., Nfaoui, E.H.: Improving relational aggregated search from big data sources using stacked autoencoders. Cogn. Syst. Res. 51, 61–71 (2018). https://doi.org/10.1016/j.cogsys.2018.05.002

    Article  Google Scholar 

  17. Zhou, K., Lalmas, M., Sakai, T., Cummins, R., Jose, J.M.: On the reliability and intuitiveness of aggregated search metrics. In: Proceedings of the 22nd ACM International Conference on Information and Knowledge Management - CIKM 2013, San Francisco, California, USA, pp. 689–698 (2013). https://doi.org/10.1145/2505515.2505691

  18. Schuth, A., Hofmann, K., Radlinski, F.: Predicting search satisfaction metrics with interleaved comparisons. In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, Santiago Chile, August 2015, pp. 463–472 (2015). https://doi.org/10.1145/2766462.2767695

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Authors and Affiliations

  1. LISAC Laboratory, Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, Fez, Morocco

    Sara Lasri & El Habib Nfaoui

Authors
  1. Sara Lasri
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  2. El Habib Nfaoui
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Correspondence to Sara Lasri .

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Editors and Affiliations

  1. Ecole Nationale des Sciences Appliquées, Fez, Morocco

    Saad Motahhir

  2. Faculty of Sciences, Sidi Mohamed Ben Abdellah University, Fez, Morocco

    Badre Bossoufi

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Lasri, S., Nfaoui, E.H. (2022). Ranking Task in RAS: A Comparative Study of Learning to Rank Algorithms and Interleaving Methods. In: Motahhir, S., Bossoufi, B. (eds) Digital Technologies and Applications. ICDTA 2022. Lecture Notes in Networks and Systems, vol 454. Springer, Cham. https://doi.org/10.1007/978-3-031-01942-5_16

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