Publication statistics
Pub. period:2005-2012
Pub. count:9
Number of co-authors:13
Co-authors
Number of publications with 3 favourite co-authors:
Nicholas J. Belkin:5Michael J. Cole:4Jacek Gwizdka:4 Productive colleagues
Chang Liu's 3 most productive colleagues in number of publications:
Nicholas J. Belkin:45Jun Zhang:18Jacek Gwizdka:16
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Chang Liu
Publications by Chang Liu (bibliography)
Liu, Chang, Belkin, Nicholas J. and Cole, Michael J. (2012): Personalization of search results using interaction behaviors in search sessions. In: Proceedings of the 35th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2012. pp. 205-214.
“Personalization of search results offers the potential for significant improvement in information retrieval performance. User interactions with the system and documents during information-seeking sessions provide a wealth of information about user preferences and their task goals. In this paper, we propose methods for analyzing and modeling user search behavior in search sessions to predict document usefulness and then using information to personalize search results. We generate prediction models of document usefulness from behavior data collected in a controlled lab experiment with 32 participants, each completing uncontrolled searching for 4 tasks in the Web. The generated models are then tested with another data set of user search sessions in radically different search tasks and constrains. The documents predicted useful and not useful by the models are used to modify the queries in each search session using a standard relevance feedback technique. The results show that application of the models led to consistently improved performance over a baseline that did not take account of user interaction information. These findings have implications for designing systems for personalized search and improving user search experience.”
© All rights reserved Liu et al. and/or ACM Press
Asgar, Zain, Chan, Joshua, Liu, Chang and Blikstein, Paulo (2011): LightUp: a low-cost, multi-age toolkit for learning and prototyping electronics. In: Proceedings of ACM IDC11 Interaction Design and Children 2011. pp. 225-226.
“LightUp is a constructionist platform to teach novices about electronics, and also a low-cost rapid-prototyping platform for more advanced users. The LightUp kit contains many basic electronic components attached to magnetic building blocks and a connection base. Various project-based educational materials are also included. Initially designed as an interactive and transparent learning tool, the concept behind LightUp is to provide a "low threshold, high ceiling" learning experience for self-motivated individuals who want to better understand the complex electronics inside the devices they rely on every day. In addition, LightUp also serves as a user friendly, low-cost prototyping tool for people who do not have a strong engineering background but still want to build electronic circuits. This paper gives an overview of the LightUp platform, the construction process and future developments and implementations.”
© All rights reserved Asgar et al. and/or ACM Press
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
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
Liu, Jingjing, Liu, Chang, Gwizdka, Jacek and Belkin, Nicholas J. (2010): Can search systems detect users' task difficulty?: some behavioral signals. In: Proceedings of the 33rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2010. pp. 845-846.
“In this paper, we report findings on how user behaviors vary in tasks with different difficulty levels as well as of different types. Two behavioral signals: document dwell time and number of content pages viewed per query, were found to be able to help the system detect when users are working with difficult tasks.”
© 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
Liu, Chang (2008): Exploring and measuring dependency trees for informationretrieval. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2008. p. 892.
“Natural language processing techniques are believed to hold a tremendous potential to supplement the purely quantitative methods of text information retrieval. This has led to the emergence of a large number of NLP-based IR research projects over the last few years, even though the empirical evidence to support this has often been inadequate. Most contributions of NLP to IR mainly concentrate on document representation and compound term matching strategies. Researchers have noted that the simple term-based representation of document content such as vector representation is usually inadequate for accurate discrimination. The "bag of words" representation does not invoke linguistic considerations and allow modelling of relationships between subsets of words. However, even though a variety of content indicator such as syntactic phrase have been tried and investigated for representing documents rather than single terms in IR systems, the matching strategy over those representation still cannot go beyond traditional statistical techniques that measure term co-occurrence characteristics and proximity in analyzing text structure. In this paper, we propose a novel IR strategy (SIR) with NLP techniques involved at the syntactic level. Within SIR, documents and query representation are built on the basis of a syntactic data structure of the natural language text -- the dependency tree, in which syntactic relationships between words are identified and structured in the form of a tree. In order to capture the syntactic relations between words in their hierarchical structural representation, the matching strategy in SIR upgrades from the traditional statistical techniques by introducing a similarity measure method executing on the graph representation level as the key determiner. A basic IR experiment is designed and implemented on the TREC data to evaluate if this novel IR model is feasible. Experimental results indicate that this approach has the potential to outperform the standard bag of words IR model, especially in response to syntactical structured queries.”
© All rights reserved Liu and/or ACM Press
Wang, Haofen, Tran, Thanh and Liu, Chang (2008): CE2: towards a large scale hybrid search engine with integrated ranking support. In: Shanahan, James G., Amer-Yahia, Sihem, Manolescu, Ioana, Zhang, Yi, Evans, David A., Kolcz, Aleksander, Choi, Key-Sun and Chowdhury, Abdur (eds.) Proceedings of the 17th ACM Conference on Information and Knowledge Management - CIKM 2008 October 26-30, 2008, Napa Valley, California, USA. pp. 1323-1324.
Li, Xiaodong and Liu, Chang (2005): Towards a Reliable and Efficient Distributed Storage System. In: HICSS 2005 - 38th Hawaii International Conference on System Sciences 3-6 January, 2005, Big Island, HI, USA. .
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