Abstract
Relevance judgment has been studied in the information field for a long time. Eye movement data contains a large amount of user subjective information, and the way of its collection is becoming easier. With the rising penetration rate of mobile Internet, people are getting used to adopt the mobile search to solve problems. The higher the utilization rate of mobile search, the higher the user's requirements for the accuracy of mobile search results. In order to explore the user relevance judgment in the mobile reverse image search scenario, this paper combines eye movement data to figure out the relation between relevance judgment and users’ eye movement. With the help of the relation the user's relevance experience can be inferred through eye movement data, thereby optimizing the SERP page, so as to achieve the effect of reasonable search results ranking and recommendation accuracy.
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Chen, D.M., Tsai, S.S., Chandrasekhar, V.: Tree histogram coding for mobile image matching. In: Data Compression Conference. IEEE Computer Society (2009)
Girod, B., Chandrasekhar, V., Grzeszczuk, R.: Mobile visual search: architectures, technologies, and the emerging MPEG standard. IEEE Multimedia 18(3), 86–94 (2011)
Li, M.: Research on personalized mobile visual search mechanism in digital library. Lib. Theory and Pract. (02), 107–112(2019)
Chutel, P.M., Sakhare, A.: Evaluation of compact composite descriptor based reverse image search. In: International Conference on Communications and Signal Processing, pp. 1430–1434. IEEE (2014)
The State Council of the People’s Republic of China, The 46th China Statistical Report on Internet Development. https://www.gov.cn/xinwen/2020-09/29/content_5548176.htm
Saracevic, T.: Relevance: a review of and a framework for thinking on the notion in information science. J. Am. Soc. Inf. Sci. 26(6), 321–343 (1975)
Ingwersen, P., Jrvelin, K.: The Turn: Integration of Information Seeking and Retrieval in Context. Springer, Cham (2011)
Schamber L.: Users’ criteria for evaluation in a multimedia environment. In: ASIS Meeting, pp. 126–133 (1991)
Saracevic, T.: Information science. J. Am. Soc. Inf. Sci. (12), 1051–1063 (1999)
Schamber, L.: Relevance and Information behavior. Ann. Rev. Inf. Sci. Technol. 29, 3–48 (1994)
Froehlich, T.J.: Relevance reconsidered—towards an agenda for the 21st century: Introduction to special topic issue on relevance research. J. Am. Soc. Inf. Sci. 45(3), 124–133 (1994)
Ingwersen, P.: Information Retrieval Interaction. Taylor Graham Publishing, London (1992)
Belkin, H.J., Cool, C., Koenemann, J., et al.: Using relevance feedback and ranking in interactive searching. In: Harman, D. TREC-4. Washington, D. C.: Proceedings of the Fourth Text retrieval Conference (1996)
Saracevic T.: Modeling interaction in information retrieval (IR): a review and proposal. In: Proceedings of the American Society for Information Science and Technology, vol. 33 (1996)
Spink, A., Greisdorf, H., Batemain, J.: From highly relevance to not relevance examining different regions of relevance. Inf. Process. Manage. 34(5), 599–622 (1998)
Spink, A., Batemain, J., Greisdorf, H.: Successive searching behavior during information seeking an exploratory study. J. Inf. Sci. 25(6), 439–449 (1999)
Greisdorf, H., Spink, A.: A new way to evaluate IR systems performance median measure. In: Proceedings of NOM, New York (2000)
Spink, A., Greisdorf, H.: Regions and levels: measuring and mapping users ‘relevance judgements. J. Am. Soc. Inform. Sci. Technol. 52(2), 161–173 (2001)
Spink, A., Greisdorf, H., Bateman, J.: Examining different regions of relevance: From highly relevance to not relevant. In: Proceedings of the American Society for Information Science, Columbus, OH (1998)
Tang, R., Solomon, P.: Use of relevance criteria across stages of document evaluation: on the complementarity of experimental and naturalistic studies. J. Am. Soc. Inf. Sci. 52(8), 676–685 (2001)
Cool, C., Belkin, N.J., Kantor, P.B.: Characteristics of texts affecting relevance judgments. In: Proceedings of the 14th National Online Meeting, pp. 77–84 (1993)
Maglaughlin, K.L., Sonnenwald, D.H.: User perspectives on relevance criteria: a comparison among relevant, partially relevant and not-relevant judgements. J. Am. Soc. Inf. Sci. Technol. 53, 327–342 (2002)
Wang, P., White, M.D.: A cognitive model of document use during a research project. Study II: decisions at the reading and citing stages. J. Am. Soc. Inf. Sci. 50(2), 98–1114 (1999)
Taylor, A.R., Zhang, X., Amadio, W.J.: Examination of relevance criteria choices and the information search process. J. Documentation 65(5), 719–744 (2009)
Taylor, A.: User relevance criteria choices and the information search process. Inf. Process. Manage. 48(1), 136–153 (2012)
Goodrum, A., Pope, R., Godo, E., et al.: News blog relevance: applying relevance criteria to news related blogs. In: Proceedings of the American Society for Information Science and Technology, vol. 47, pp. 1–2 (2010)
Markkula, M., Sormunen, E.: End-user searching challenges indexing practice in the digital newspaper photo archive. Inf. Retrieval 1(4), 259–285 (2000)
Choi, Y., Rasmussen, E.M.: Users’ relevance criteria in image retrieval in American history. Inf. Process. Manage. 38(5), 695–726 (2002)
Sedghi, S., Sanderson, M., Clough, P.: A Study on the relevance criteria for medical images. Pattern Recogn. Lett. 29(15), 2046–2057 (2008)
Westman, S., Oittinen, P.: Image retrieval by end-users and intermediaries in a journalistic work context. In: Proceedings of the 1st International Conference on Information Interaction in Context, New York, NY, USA, pp. 102–110 (2006)
Sedghi, S., Sanderson, M., Clough, P.: How do health care professionals select medical images they need. ASLIB Proc. 64(4), 437–456 (2012)
Hung T Y, Zoeller C, Lyon S.: Relevance judgments for image retrieval in the field of journalism: a pilot study. 3815(3), 72–80 (2005)
Sedghi, S., Sanderson, M., Clough, P.: Medical image resources used by health care professionals. ASLIB Proc. 63(6), 570–585 (2013)
Buerger, T.: A model of relevance for reuse-driven media retrieval. In: Proceedings of the 12th International Workshop of the Multimedia Metadata Community, the 2nd Workshop Focusing on Semantic Multimedia Database Technologies (SMDT 2010), Saarbrucken, pp. 1–3 (2010)
Zhang, F., Zhou, K., Shao, Y., et al.: How well do offline and online evaluation metrics measure user satisfaction in web image search? In: Proceedings of the 41st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2018) , Ann Arbor, MI, USA, pp. 15–624. ACM (2018)
Hamid, R.A., Thom, J.A., Iskandar, D.A.: Effects of relevance criteria and subjective factors on web image searching behavior. J. Inf. Sci. 43(6) (2017)
Taneja, H., Gupta, R.: Web information retrieval using query independent page rank algorithm. In: International Conference on Advances in Computer Engineering, pp. 178–182 (2010)
Tobiipro Homepage. https://www.tobiipro.com/siteassets/tobii-pro/user-manuals/Tobii-Pro-Lab-User-Manual
Yeh, T., White, B., San Pedro, J., Katz, B., Davis, L.S.: A case for query by image and text content: searching computer help using screenshots and keywords. In: Proceedings of the 20th International Conference on World Wide Web, pp. 775–784. ACM, New York (2011)
Chutel, P.M., Sakhare, A.: Evaluation of compact composite descriptor based reverse image search. In: International Conference on Communications and Signal Processing, pp. 1430–434. IEEE (2014)
O’Neil, F.: Looking forward to reverse image search: measuring the effectiveness of reverse image searches in online help. In: International Conference on Applied Human Factors and Ergonomics, pp. 24–35. Springer, Cham (2017)
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This work is sponsored by Major Projects of the National Social Science Foundation: 19ZDA341.
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Wu, D., Zhang, C., Ainiwaer, A., Lv, S. (2021). Hybrid Research on Relevance Judgment and Eye Movement for Reverse Image Search. In: Toeppe, K., Yan, H., Chu, S.K.W. (eds) Diversity, Divergence, Dialogue. iConference 2021. Lecture Notes in Computer Science(), vol 12645. Springer, Cham. https://doi.org/10.1007/978-3-030-71292-1_19
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