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- short-paperJuly 2023
Behavior Modeling for Point of Interest Search
- Haitian Chen,
- Qingyao Ai,
- Zhijing Wu,
- Zhihong Wang,
- Yiqun Liu,
- Min Zhang,
- Shaoping Ma,
- Juan Hu,
- Naiqiang Tan,
- Hua Chai
SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information RetrievalJuly 2023, Pages 1843–1847https://doi.org/10.1145/3539618.3591955With the increasing popularity of location-based services, the point-of-interest (POI) search has received considerable attention in recent years. Existing studies on POI search mostly focus on how to construct better retrieval models to retrieve the ...
- research-articleApril 2023
A Passage-Level Reading Behavior Model for Mobile Search
WWW '23: Proceedings of the ACM Web Conference 2023April 2023, Pages 3236–3246https://doi.org/10.1145/3543507.3583343Reading is a vital and complex cognitive activity during users’ information-seeking process. Several studies have focused on understanding users’ reading behavior in desktop search. Their findings greatly contribute to the design of information retrieval ...
- demonstrationMarch 2021
Supporting Cross-Session Cross-Device Search in an Academic Digital Library
CHIIR '21: Proceedings of the 2021 Conference on Human Information Interaction and RetrievalMarch 2021, Pages 337–341https://doi.org/10.1145/3406522.3446055Academic search tasks that are complex may require that a searcher undertake the task over the course of multiple search sessions, and possibly using multiple devices. One of the key challenges in cross-session search is resuming the previously started ...
- short-paperMarch 2021
Impact of Response Latency on User Behaviour in Mobile Web Search
CHIIR '21: Proceedings of the 2021 Conference on Human Information Interaction and RetrievalMarch 2021, Pages 279–283https://doi.org/10.1145/3406522.3446038Traditionally, the efficiency and effectiveness of search systems have both been of great interest to the information retrieval community. However, an in-depth analysis of the interaction between the response latency and users' subjective search ...
- short-paperMarch 2020
The Influence of Device Type on Querying Behavior and Learning Outcomes in a Searching as Learning Task with a Laptop or Smartphone
CHIIR '20: Proceedings of the 2020 Conference on Human Information Interaction and RetrievalMarch 2020, Pages 373–377https://doi.org/10.1145/3343413.3378000In this study we examined the influence of device type (i.e., laptop vs. smartphone) on N = 49 secondary-school students' search query length and number of queries used in a Searching as Learning (SAL) task addressing a controversial socio-scientific ...
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- research-articleMarch 2020
A Tool for Conducting User Studies on Mobile Devices
CHIIR '20: Proceedings of the 2020 Conference on Human Information Interaction and RetrievalMarch 2020, Pages 462–466https://doi.org/10.1145/3343413.3377985With the ever-growing interest in the area of mobile information retrieval and the ongoing fast development of mobile devices and, as a consequence, mobile apps, an active research area lies in studying users' behavior and search queries users submit on ...
- research-articleJanuary 2020
Investigating Examination Behavior in Mobile Search
WSDM '20: Proceedings of the 13th International Conference on Web Search and Data MiningJanuary 2020, Pages 771–779https://doi.org/10.1145/3336191.3371797Examination is one of the most important user interactions in Web search. A number of works studied examination behavior in Web search and helped researchers better understand how users allocate their attention on search engine result pages (SERPs). ...
- research-articleSeptember 2019
Constructing Click Model for Mobile Search with Viewport Time
ACM Transactions on Information Systems (TOIS), Volume 37, Issue 4Article No.: 43, Pages 1–34https://doi.org/10.1145/3360486A series of click models has been proposed to extract accurate and unbiased relevance feedback from valuable yet noisy click-through data in search logs. Previous works have shown that users search behavior in mobile and desktop scenarios are rather ...
- short-paperJuly 2019
Evaluating Variable-Length Multiple-Option Lists in Chatbots and Mobile Search
SIGIR'19: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information RetrievalJuly 2019, Pages 997–1000https://doi.org/10.1145/3331184.3331308In recent years, the proliferation of smart mobile devices has lead to the gradual integration of search functionality within mobile platforms. This has created an incentive to move away from the "ten blue links" metaphor, as mobile users are less ...
- research-articleMarch 2019
Understanding Mobile Search Task Relevance and User Behaviour in Context
CHIIR '19: Proceedings of the 2019 Conference on Human Information Interaction and RetrievalMarch 2019, Pages 143–151https://doi.org/10.1145/3295750.3298923Improvements in mobile technologies have led to a dramatic change in how and when people access and use information, and is having a profound impact on how users address their daily information needs. Smart phones are rapidly becoming our main method of ...
- research-articleJune 2018
Constructing Click Models for Mobile Search
SIGIR '18: The 41st International ACM SIGIR Conference on Research & Development in Information RetrievalJune 2018, Pages 775–784https://doi.org/10.1145/3209978.3210060Users' click-through behavior is considered as a valuable yet noisy source of implicit relevance feedback for web search engines. A series of click models have therefore been proposed to extract accurate and unbiased relevance feedback from click logs. ...
- research-articleMarch 2018
The Characteristics of Voice Search: Comparing Spoken with Typed-in Mobile Web Search Queries
ACM Transactions on Information Systems (TOIS), Volume 36, Issue 3Article No.: 30, Pages 1–28https://doi.org/10.1145/3182163The growing popularity of mobile search and the advancement in voice recognition technologies have opened the door for web search users to speak their queries rather than type them. While this kind of voice search is still in its infancy, it is ...
- short-paperMarch 2018
A Two-Stage Model for User's Examination Behavior in Mobile Search
CHIIR '18: Proceedings of the 2018 Conference on Human Information Interaction & RetrievalMarch 2018, Pages 273–276https://doi.org/10.1145/3176349.3176891With the rapid growth of mobile search, it is important to understand how users browse the mobile SERPs and allocate their limited attention to each result. To address this problem, we introduce a two-stage examination model that can separately capture ...
- short-paperMarch 2018
Noisy Signals: Understanding the Impact of Auditory Distraction on Web Search Tasks
CHIIR '18: Proceedings of the 2018 Conference on Human Information Interaction & RetrievalMarch 2018, Pages 241–244https://doi.org/10.1145/3176349.3176871More than half of all searches are now submitted on mobile devices, which can (and often are) used in various potentially distracting situations, such as travelling on a noisy train or when walking down a busy street. Research suggests that walking has ...
- short-paperNovember 2017
Ranking Rich Mobile Verticals based on Clicks and Abandonment
CIKM '17: Proceedings of the 2017 ACM on Conference on Information and Knowledge ManagementNovember 2017, Pages 2127–2130https://doi.org/10.1145/3132847.3133059We consider the problem of ranking rich verticals, which we call "cards," for a given mobile search query. Examples of card types include "SHOP" (showing access and contact information of a shop), "WEATHER" (showing a weather forecast for a particular ...
- short-paperOctober 2017
Mobile Vertical Ranking based on Preference Graphs
ICTIR '17: Proceedings of the ACM SIGIR International Conference on Theory of Information RetrievalOctober 2017, Pages 225–228https://doi.org/10.1145/3121050.3121082We consider the problem of ranking relevant verticals for a given mobile search query so as to satisfy the average user. To this end, we utilise real mobile search click logs, and apply a graph contruction algorithm proposed by Agrawal et al. who ...
- research-articleAugust 2017
Evaluating Mobile Search with Height-Biased Gain
SIGIR '17: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information RetrievalAugust 2017, Pages 435–444https://doi.org/10.1145/3077136.3080795Mobile search engine result pages (SERPs) are becoming highly visual and heterogenous. Unlike the traditional ten-blue-link SERPs for desktop search, different verticals and cards occupy different amounts of space within the small screen. Hence, ...
- research-articleAugust 2017
Searching on the Go: The Effects of Fragmented Attention on Mobile Web Search Tasks
SIGIR '17: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information RetrievalAugust 2017, Pages 155–164https://doi.org/10.1145/3077136.3080770Smart phones and tablets are rapidly becoming our main method of accessing information and are frequently used to perform on-the-go search tasks. Mobile devices are commonly used in situations where attention must be divided, such as when walking down a ...
- short-paperMarch 2017
Perceptions of the Effect of Fragmented Attention on Mobile Web Search Tasks
CHIIR '17: Proceedings of the 2017 Conference on Conference Human Information Interaction and RetrievalMarch 2017, Pages 293–296https://doi.org/10.1145/3020165.3022136Mobile devices are rapidly becoming our main method of accessing the Internet and are frequently used to perform on-the-go search tasks. The use of such devices in situations where attention must be divided, such as when walking, are common and research ...
- research-articleOctober 2016
A Comparative Study of Query-biased and Non-redundant Snippets for Structured Search on Mobile Devices
CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge ManagementOctober 2016, Pages 2389–2394https://doi.org/10.1145/2983323.2983699To investigate what kind of snippets are better suited for structured search on mobile devices, we built an experimental mobile search application and conducted a task-oriented interactive user study with 36 participants. Four different versions of a ...