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

Abstract

Abstract. The wide adoption of smartphones eliminates the time and location barriers for people's daily information access, but also limits users' information exploration activities due to the small mobile screen size. Thus, cross-device web search, where people initialize information needs on one device but complete them on another device, is frequently observed in modern search engines, especially for exploratory information needs. This paper aims to support the cross-device web search, on top of the commonly used context-sensitive retrieval framework, for exploratory tasks. 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. Our results show that the social navigation-based MTIs outperformed both baselines. A further analysis shows that the performance improvements are related to several factors, including the quality and quantity of click-through documents, task types and users' search conditions.

Television Advertising in the Multiscreen and Multitasking Age: Does it Work for Millennials?

“…The term cross-sessional search is used to refer to search endeavours longer than one session (Kotov et al, 2011;Wang et al, 2013). Cross-device search refers to consumers using multiple devices in the information research process (Han et al, 2015;Wu et al, 2018). Trial and error behaviours that lead to accomplishment of the research task, or the intent shift directing the consumer towards an unexpected and unintended territory, are important for understanding the search patterns and behaviours (Chen et al, 2018).…”

Predicting Academic Digital Library OPAC Users' Cross-device Transitions

“…The prediction of cross-devices is also an important aspect in this field. The user's network history, touch interaction on the mobile device, and time interval of device transition can support to predict user's search behavior after the device transition (Han et al, 2015a;Han et al, 2015b). Wang et al (2013) explored how to predict task resumption after switching devices; others learned a model based on the cross-device search characteristics to predict various aspects of cross-device transition, including the next device that the user might use (Han, et al, 2017).…”

“…In addition, the probability of device transitions changes over time, and predictions of user's behavior across device settings have recently become a hot topic (Montanez et al, 2014). The users' network history, such as initial queries and time intervals, helps predict and support cross-device searches for users (Han et al, 2015a).…”

Social Search

“…Browsing trails can offer several types of social wisdom that can be used to improve rankings. First, browsing trails provide some evidence of page value and importance through various implicit indicators, such as reading-time, or, with appropriate instrumentation, within-page scrolling, and and mousing (for desktops) or "fingering" for mobile browsing [93]. Second, assuming that users have coherent goals when browsing, the sequence of pages followed can help to create associations between pages.…”