Number of co-authors:13
Number of publications with 3 favourite co-authors:Michael J. Cole:3Chang Liu:3Jacek Gwizdka:3
Xiangmin Zhang's 3 most productive colleagues in number of publications:Nicholas J. Belkin:45Ying Zhang:20Jun Zhang:18
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-- Paul Rand, 1997
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Publications by Xiangmin Zhang (bibliography)
Ho, Shuyuan Mary and Zhang, Xiangmin (2011): i-sensor inference model for assessing trustworthiness in computer-mediated communications. In: Proceedings of ACM CSCW11 Conference on Computer-Supported Cooperative Work 2011. pp. 645-648.
This paper presents a modeling strategy for an intelligence sensor system, or i-Sensor, which can comprehend human's virtual dialogues in Computer Mediated Communications (CMC) and process those dialogues to understand trustworthiness detected among humans in a virtual collaborative group. The model proposed here is built on research that demonstrated how human "sensors" can detect unusual or unexpected changes in a psychological construct, trustworthiness, based on observed virtual behavior.
© All rights reserved Ho and Zhang and/or their publisher
Cole, Michael J., Zhang, Xiangmin, Liu, Chang, Belkin, Nicholas J. and Gwizdka, Jacek (2011): Knowledge effects on document selection in search results pages. In: Proceedings of the 34th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2011. pp. 1219-1220.
Click through events in search results pages (SERPs) are not reliable implicit indicators of document relevance. A user's task and domain knowledge are key factors in recognition and link selection and the most useful SERP document links may be those that best match the user's domain knowledge. User study participants rated their knowledge of genomics MeSH terms before conducting 2004 TREC Genomics Track tasks. Each participant's document knowledge was represented by their knowledge of the indexing MeSH terms. Results show high, intermediate, and low domain knowledge groups had similar document selection SERP rank distributions. SERP link selection distribution varied when participant knowledge of the available documents was analyzed. High domain knowledge participants usually selected a document with the highest personal knowledge rating. Low domain knowledge participants were reasonably successful at selecting available documents of which they had the most knowledge, while intermediate knowledge participants often failed to do so. This evidence for knowledge effects on SERP link selection may contribute to understanding the potential for personalization of search results ranking based on user domain knowledge.
© All rights reserved Cole et al. and/or ACM Press
Zhang, Xiangmin, Cole, Michael and Belkin, Nicholas (2011): Predicting users' domain knowledge from search behaviors. In: Proceedings of the 34th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2011. pp. 1225-1226.
This study uses regression modeling to predict a user's domain knowledge level (DK) from implicit evidence provided by certain search behaviors. A user study (n=35) with recall-oriented search tasks in the genomic domain was conducted. A number of regression models of a person's DK, were generated using different behavior variable selection methods. The best model highlights three behavior variables as DK predictors: the number of documents saved, the average query length, and the average ranking position of documents opened. The model is validated using the split sampling method. Limitations and future research directions are discussed.
© All rights reserved Zhang et al. and/or ACM Press
Liu, Jingjing, Cole, Michael J., Liu, Chang, Bierig, Ralf, Gwizdka, Jacek, Belkin, Nicholas J., Zhang, Jun and Zhang, Xiangmin (2010): Search behaviors in different task types. In: JCDL10 Proceedings of the 2010 Joint International Conference on Digital Libraries 2010. pp. 69-78.
Personalization of information retrieval tailors search towards individual users to meet their particular information needs by taking into account information about users and their contexts, often through implicit sources of evidence such as user behaviors. Task types have been shown to influence search behaviors including usefulness judgments. This paper reports on an investigation of user behaviors associated with different task types. Twenty-two undergraduate journalism students participated in a controlled lab experiment, each searching on four tasks which varied on four dimensions: complexity, task product, task goal and task level. Results indicate regular differences associated with different task characteristics in several search behaviors, including task completion time, decision time (the time taken to decide whether a document is useful or not), and eye fixations, etc. We suggest these behaviors can be used as implicit indicators of the user's task type.
© All rights reserved Liu et al. and/or their publisher
Cole, Michael J., Gwizdka, Jacek, Bierig, Ralf, Belkin, Nicholas J., Liu, Jingjing, Liu, Chang and Zhang, Xiangmin (2010): Linking search tasks with low-level eye movement patterns. In: Proceedings of the 2010 Annual European Conference on Cognitive Ergonomics 2010. pp. 109-116.
Motivation -- On-the-task detection of the task type and task attributes can benefit personalization and adaptation of information systems. Research approach -- A web-based information search experiment was conducted with 32 participants using a multi-stream logging system. The realistic tasks were related directly to the backgrounds of the participants and were of distinct task types. Findings/Design -- We report on a relationship between task and individual reading behaviour. Specifically we show that transitions between scanning and reading behaviour in eye movement patterns are an implicit indicator of the current task. Research limitations/Implications -- This work suggests it is plausible to infer the type of information task from eye movement patterns. One limitation is a lack of knowledge about the general reading model differences across different types of tasks in the population. Although this is an experimental study we argue it can be generalized to real world text-oriented information search tasks. Originality/Value -- This research presents a new methodology to model user information search task behaviour. It suggests promise for detection of information task type based on patterns of eye movements. Take away message -- With increasingly complex computer interaction, knowledge about the type of information task can be valuable for system personalization. Modelling the reading/scanning patterns of eye movements can allow inference about the task type and task attributes.
© All rights reserved Cole et al. and/or their publisher
Li, Yuelin, Zhang, Xiangmin, Zhang, Ying and Liu, Jingjing (2006): A comparative study of the effect of search feature design on user experience in digital libraries (DLs). In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2006. pp. 669-670.
This study investigates the impact of different search feature designs in DLs on user search experience. The results indicate that the impact is significant in terms of the number of queries issued, search steps, zero-hits pages returned, and search errors.
© All rights reserved Li et al. and/or ACM Press
Zhang, Xiangmin and Li, Yuelin (2005): An Exploratory Study on Knowledge Sharing in Information Retrieval. In: HICSS 2005 - 38th Hawaii International Conference on System Sciences 3-6 January, 2005, Big Island, HI, USA. .
Zhang, Xiangmin (2004): Rejoinder: Quality of information. In JASIST - Journal of the American Society for Information Science and Technology, 55 (1) p. 92.
Zhang, Xiangmin (2002): Collaborative relevance judgment: A group consensus method for evaluating user search performance. In JASIST - Journal of the American Society for Information Science and Technology, 53 (3) pp. 220-231.
Zhang, Xiangmin and Chignell, Mark H. (2001): Assessment of the effects of user characteristics on mental models of information retrieval systems. In JASIST - Journal of the American Society for Information Science and Technology, 52 (6) pp. 445-459.
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