Contextual queries and situated information needs for mobile users

The users of mobile devices increasingly use networked services to address their information needs. Questions asked by mobile users are strongly influenced by contextual factors such as location, conversation and activity. We report on a diary study performed to better understand mobile information needs. Participants’ diary entries are used as a basis for discussing the geographical and situational context in which mobile information behaviour occurs. The suitability of user queries to be answered by a portable knowledge collection and web search are also considered. We find that the type of questions recorded by participants varies across their locations, with differences between home, shopping and in-car contexts. These variations occur both in the query terms and in the form of desired answers. Both the location of queries and the participants’ activities affected participants’ questions. When information needs were affected by both location and activity, they tended to be strongly affected by both factors. The overall picture that emerges is one of multiple contextual influences interacting to shape mobile information needs. Mobile devices that attempt to adapt to users’ context will need to account for a rich variety of situational factors

Dynamically Personalizing Search Results for Mobile Users

International audienceWe introduce a novel situation-aware approach to personalize search results for mobile users. By providing a mobile user with appropriate information that dynamically satisfies his interests according to his situation, we tackle the problem of information overload. To build situation-aware user profile we rely on evidence issued from retrieval situations. A retrieval situation refers to the spatio-temporal context of the user when submitting a query to the search engine. A situation is represented as a combination of geographical and temporal concepts inferred from concrete time and location information by some ontological knowledge. User's interests are inferred from past search activities related to the identified situations. They are represented using concepts issued from a thematic ontology. We also involve a method to maintain the user's interests over his ongoing search activity and to personalize the search results

Searching on the go : the effects of fragmented attention on mobile web search tasks

Smart 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 street. Research suggests that this increases cognitive load and, therefore, may have an impact on performance. In this work we conducted a laboratory experiment with both device types in which we simulated everyday, common mobile situations that may cause fragmented attention, impact search performance and affect user perception. Our results showed that the fragmented attention induced by the simulated conditions significantly affected both participants' objective and perceived search performance, as well as how hurried they felt and how engaged they were in the tasks. Furthermore, the type of device used also impacted how users felt about the search tasks, how well they performed and the amount of time they spent engaged in the tasks. These novel insights provide useful information to inform the design of future interfaces for mobile search and give us a greater understanding of how context and device size affect search behaviour and user experience

Searching on the Go: The Effects of Fragmented Attention on Mobile Web Search Tasks

Smart 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 street. Research suggests that this increases cognitive load and, therefore, may have an impact on performance. In this work we conducted a laboratory experiment with both device types in which we simulated everyday, common mobile situations that may cause fragmented attention, impact search performance and affect user perception. Our results showed that the fragmented attention induced by the simulated conditions significantly affected both participants' objective and perceived search performance, as well as how hurried they felt and how engaged they were in the tasks. Furthermore, the type of device used also impacted how users felt about the search tasks, how well they performed and the amount of time they spent engaged in the tasks. These novel insights provide useful information to inform the design of future interfaces for mobile search and give us a greater understanding of how context and device size affect search behaviour and user experience

Exploring the logic of mobile search

After more than a decade of development work and hopes, the usage of mobile Internet has finally taken off. Now, we are witnessing the first signs of evidence of what might become the explosion of mobile content and applications that will be shaping the (mobile) Internet of the future. Similar to the wired Internet, search will become very relevant for the usage of mobile Internet. Current research on mobile search has applied a limited set of methodologies and has also generated a narrow outcome of meaningful results. This article covers new ground, exploring the use and visions of mobile search with a users' interview-based qualitative study. Its main conclusion builds upon the hypothesis that mobile search is sensitive to a mobile logic different than today's one. First, (advanced) users ask for accessing with their mobile devices the entire Internet, rather than subsections of it. Second, success is based on new added-value applications that exploit unique mobile functionalities. The authors interpret that such mobile logic involves fundamentally the use of personalised and context-based services

Supporting cross-device web search with social navigation-based mobile touch interactions

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

Mining, analyzing, and modeling text written on mobile devices

AbstractWe present a method for mining the web for text entered on mobile devices. Using searching, crawling, and parsing techniques, we locate text that can be reliably identified as originating from 300 mobile devices. This includes 341,000 sentences written on iPhones alone. Our data enables a richer understanding of how users type “in the wild” on their mobile devices. We compare text and error characteristics of different device types, such as touchscreen phones, phones with physical keyboards, and tablet computers. Using our mined data, we train language models and evaluate these models on mobile test data. A mixture model trained on our mined data, Twitter, blog, and forum data predicts mobile text better than baseline models. Using phone and smartwatch typing data from 135 users, we demonstrate our models improve the recognition accuracy and word predictions of a state-of-the-art touchscreen virtual keyboard decoder. Finally, we make our language models and mined dataset available to other researchers.</jats:p