Understanding and Supporting Cross-Device Web Search for Exploratory Tasks with Mobile Touch Interactions

Abstract

Mobile devices enable people to look for information at the moment when their information needs are triggered. While experiencing complex information needs that require multiple search sessions, users may utilize desktop computers to fulfill information needs started on mobile devices. Under the context of mobileto-desktop web search, this article analyzes users' behavioral patterns and compares them to the patterns in desktop-to-desktop web search. Then, we examine several approaches of using Mobile Touch Interactions (MTIs) to infer relevant content so that such content can be used for supporting subsequent search queries on desktop computers. The experimental data used in this article was collected through a user study involving 24 participants and six properly designed cross-device web search tasks. Our experimental results show that (1) users' mobile-to-desktop search behaviors do significantly differ from desktop-to-desktop search behaviors in terms of information exploration, sense-making and repeated behaviors. (2) MTIs can be employed to predict the relevance of click-through documents, but applying document-level relevant content based on the predicted relevance does not improve search performance. (3) MTIs can also be used to identify the relevant text chunks at a fine-grained subdocument level. Such relevant information can achieve better search performance than the document-level relevant content. In addition, such subdocument relevant information can be combined with document-level relevance to further improve the search performance. However, the effectiveness of these methods relies on the sufficiency of click-through documents. (4) MTIs can also be obtained from the Search Engine Results Pages (SERPs). The subdocument feedbacks inferred from this set of MTIs even outperform the MTI-based subdocument feedback from the click-through documents. ACM Reference Format:Shuguang Han, Zhen Yue, and Daqing He. 2015. Understanding and supporting cross-device web search for exploratory tasks with mobile touch interactions.

Supporting Cross-Device Web Search with Social Navigation-Based Mobile Touch Interactions

“…These approaches require acquiring user information which may be hard to obtain. Other information such as users' MTI behaviors can also help to identify a fine-grained user interest, and thus can be used to filter out the noise [10]. In this paper, we try to combine the advantages of social navigation and MTIs, and expect further improvement on search performance.…”

“…Including additional information increases the risk of having noise. Previous studies found that applying subdocument relevant information can help to reduce noise [4,10]. Such information can be inferred either from gaze attention [4] or from mobile touch interactions (MTI) [10].…”

“…Previous studies found that applying subdocument relevant information can help to reduce noise [4,10]. Such information can be inferred either from gaze attention [4] or from mobile touch interactions (MTI) [10]. The latter does not require additional resources to obtain eye-gaze information, and thus is easier to scale up in live systems.…”

“…Thus, we followed the approach used in Farzan [8] that performed lab-based user studies and preset the same task goals for all participants. To make our search tasks resemble real-world cross-device web searches, our tasks were directly adopted from Han et al [10], where tasks were designed based on the results of an online survey. Among the collected information needs, we selected the ones that seek for multi-facet answers to simulate the intrinsic diversity (ID) tasks, which was identified as a popular type of information need in modern search engines [17].…”

“…To better model users' search context, our method not only utilizes the search history (query history and click-through) but also employs the mobile touch interactions (MTI) on mobile devices. To be more specific, we combine MTI's ability of locating relevant subdocument content [10] with the idea of social navigation that aggregates MTIs from other users who visit the same page. To demonstrate the effectiveness of our proposed approach, we designed a user study to collect cross-device web search logs on three different types of tasks from 24 participants and then compared our approach with two baselines: a traditional full text based relevance feedback approach and a self-MTI based subdocument relevance feedback approach.…”