Abstract
Many studies have been done to investigate good abandonment, but only a few have utilized it to improve search engine performance. In this paper, we aim at inferring user preference in good abandonment. Particularly, we use eye movement data to infer which search result has satisfied user’s information need in each good abandonment instance. An eye-tracking experiment was conducted to capture user’s eye movement data in good abandonment search tasks. These data were transformed into histograms and sequences on which we applied popular machine learning algorithms for the inference. Our results show that the approach can infer user preference with reasonable accuracy.
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References
Stamou, S., Efthimiadis, E.N.: Queries without clicks: successful or failed searches?. In: Proceedings of the 2009 SIGIR Workshop on the Future of Information Retrieval Evaluation, pp. 13–14 (2009)
Li, J., Huffman, S.B., Tokuda, A.: Good abandonment in mobile and PC internet search. In: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 43–50 (2009)
Chuklin, A., Serdyukov, P.: Good abandonments in factoid queryies. In: Proceedings of the 21st International Conference Companion on World Wide Web, pp. 483–484 (2012)
Chuklin, A., Serdyukov, P.: Potential good abandonment prediction. In: Proceedings of the 21st International Conference Companion on World Wide Web, pp. 485–486 (2012)
Chuklin, A., Serdyukov, P.: How query extensions reflect search result abandonments. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1087–1088 (2012)
Diriye, A., White, R.W., Buscher, G., Dumais, S.T.: Leaving so soon? understanding and predicting web search abandonment rationales. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management, pp. 1025–1034 (2012)
Song, Y., Shi, X., White, R.W., Hassan, A.: Context-aware web search abandonment prediction. In: Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 93–102 (2014)
Arkhipova, O., Grauer, L.: Evaluating mobile web search performance by taking good abandonment into account. In: Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1043–1046 (2014)
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Lu, W., Jia, Y. (2015). Inferring User Preference in Good Abandonment from Eye Movements. In: Dong, X., Yu, X., Li, J., Sun, Y. (eds) Web-Age Information Management. WAIM 2015. Lecture Notes in Computer Science(), vol 9098. Springer, Cham. https://doi.org/10.1007/978-3-319-21042-1_40
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DOI: https://doi.org/10.1007/978-3-319-21042-1_40
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