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Bo Thiesson

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Publication statistics

Pub. period:2007-2009
Pub. count:4
Number of co-authors:7

Co-authors

Number of publications with 3 favourite co-authors:

Tim Paek:3
Bongshin Lee:2
Yun-Cheng Ju:1

Productive colleagues

Bo Thiesson's 3 most productive colleagues in number of publications:

Bongshin Lee:25
Tim Paek:11
Christopher Meek:6

Calendar

May 27

13th Intl. Conf. on New Interfaces for Musical Expression

May 28

Graphics Interface 2013

May 29

Boston UXPA Usability and UX Conf. 2013

May 25

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Publications by Bo Thiesson (bibliography)

2009

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Shani, Guy, Meek, Christopher, Paek, Tim, Thiesson, Bo and Venolia, Gina Danielle (2009): Searching large indexes on tiny devices: optimizing binary search with character pinning. In: Proceedings of the 2009 International Conference on Intelligent User Interfaces 2009. pp. 257-266.

“The small physical size of mobile devices imposes dramatic restrictions on the user interface (UI). With the ever increasing capacity of these devices as well as access to large online stores it becomes increasingly important to help the user select a particular item efficiently. Thus, we propose binary search with character pinning, where users can constrain their search to match selected prefix characters while making simple binary decisions about the position of their intended item in the lexicographic order. The underlying index for our method is based on a ternary search tree that is optimal under certain user-oriented constraints. To better scale to larger indexes, we analyze several heuristics that rapidly construct good trees. A user study demonstrates that our method helps users conduct rapid searches, using less keystrokes, compared to other methods.”

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Paek, Tim, Lee, Bongshin and Thiesson, Bo (2009): Designing phrase builder: a mobile real-time query expansion interface. In: Proceedings of 11th Conference on Human-computer interaction with mobile devices and services 2009. p. 7.

“As users enter web queries, real-time query expansion (RTQE) interfaces offer suggestions based on an index garnered from query logs. In selecting a suggestion, users can potentially reduce keystrokes, which can be very beneficial on mobile devices with deficient input means. Unfortunately, RTQE interfaces typically provide little assistance when only parts of an intended query appear among the suggestion choices. In this paper, we introduce Phrase Builder, an RTQE interface that reduces keystrokes by facilitating the selection of individual query words and by leveraging back-off query techniques to offer completions for out-of-index queries. We describe how we implemented a small memory footprint index and retrieval algorithm, and discuss lessons learned from three versions of the user interface, which was iteratively designed through user studies. Compared to standard auto-completion and typing, the last version of Phrase Builder reduced more keystrokes-per-character, was perceived to be faster, and was overall preferred by users.”

2008

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Paek, Tim, Thiesson, Bo, Ju, Yun-Cheng and Lee, Bongshin (2008): Search Vox: leveraging multimodal refinement and partial knowledge for mobile voice search. In: Cousins, Steve B. and Beaudouin-Lafon, Michel (eds.) Proceedings of the 21st Annual ACM Symposium on User Interface Software and Technology October 19-22, 2008, Monterey, CA, USA. pp. 141-150.

2007

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Church, Kenneth Ward and Thiesson, Bo (2007): The wild thing goes local. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2007. p. 901.

“Suppose you are on a mobile device with no keyboard (e.g., a cell phone) and you want to perform a "near me" search. Where is the nearest pizza? How do you enter queries quickly? T9? The Wild Thing encourages users to enter patterns with implicit and explicit wild cards (regular expressions). The search engine uses Microsoft Local Live logs to find the most likely queries for a particular location. For example, 7#6 is short-hand for the regular expression: /^[PQRS].*[ ][MNO].*/, which matches "post office" in many places (but "Space Needle" in Seattle). Some queries are more local than others. Pizza is likely everywhere, whereas "Boeing Company," is very likely in Seattle and Chicago, moderately likely nearby, and somewhat likely elsewhere. Smoothing is important. Not every query is observed everywhere.”

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