Kumaripaba Athukorala

Directing exploratory search: reinforcement learning from user interactions with keywords

Techniques for both exploratory and known item search tend to direct only to more specific subtopics or individual documents, as opposed to allowing directing the exploration of the information space. We present an interactive information retrieval system that combines Reinforcement Learning techniques along with a novel user interface design to allow active engagement of users in directing the search. Users can directly manipulate document features (keywords) to indicate their interests and Reinforcement Learning is used to model the user by allowing the system to trade off between exploration and exploitation. This gives users the opportunity to more effectively direct their search nearer, further and following a direction. A task-based user study conducted with 20 participants comparing our system to a traditional query-based baseline indicates that our system significantly improves the effectiveness of information retrieval by providing access to more relevant and novel information without having to spend more time acquiring the information.

Is exploratory search different? A comparison of information search behavior for exploratory and lookup tasks

Exploratory search is an increasingly important activity yet challenging for users. Although there exists an ample amount of research into understanding exploration, most of the major information retrieval (IR) systems do not provide tailored and adaptive support for such tasks. One reason is the lack of empirical knowledge on how to distinguish exploratory and lookup search behaviors in IR systems. The goal of this article is to investigate how to separate the 2 types of tasks in an IR system using easily measurable behaviors. In this article, we first review characteristics of exploratory search behavior. We then report on a controlled study of 6 search tasks with 3 exploratory—comparison, knowledge acquisition, planning—and 3 lookup tasks—fact‐finding, navigational, question answering. The results are encouraging, showing that IR systems can distinguish the 2 search categories in the course of a search session. The most distinctive indicators that characterize exploratory search behaviors are query length, maximum scroll depth, and task completion time. However, 2 tasks are borderline and exhibit mixed characteristics. We assess the applicability of this finding by reporting on several classification experiments. Our results have valuable implications for designing tailored and adaptive IR systems.

How carat affects user behavior: implications for mobile battery awareness applications

Mobile devices have limited battery life, and numerous battery management applications are available that aim to improve it. This paper examines a large-scale mobile battery awareness application, called Carat, to see how it changes user behavior with long-term use. We conducted a survey of current Carat Android users and analyzed their interaction logs. The results show that long-term Carat users save more battery, charge their devices less often, learn to manage their battery with less help from Carat, have a better understanding of how Carat works, and may enjoy competing against other users. Based on these findings, we propose a set of guidelines for mobile battery awareness applications: battery awareness applications should make the reasoning behind their recommendations understandable to the user, be tailored to retain long-term users, take the audience into account when formulating feedback, and distinguish third-party and system applications.

Pervasive Social Computing: augmenting five facets of human intelligence

Pervasive Social Computing is a novel collective paradigm, derived from pervasive computing, social media, social networking, social signal processing, etc. This paper reviews Pervasive Social Computing as an integrated computing environment, which promises to augment five facets of human intelligence: physical environment awareness, behavior awareness, community awareness, interaction awareness, and content awareness. Reviews of related studies are given, and their generic architectures are designed. The resulting architecture for Pervasive Social Computing is presented. A prototype is developed and examined, in order to investigate the characteristics exhibited by Pervasive Social Computing.

Beyond Relevance: Adapting Exploration/Exploitation in Information Retrieval

We present a novel adaptation technique for search engines to better support information-seeking activities that include both lookup and exploratory tasks. Building on previous findings, we describe (1) a classifier that recognizes task type (lookup vs. exploratory) as a user is searching and (2) a reinforcement learning based search engine that adapts accordingly the balance of exploration/exploitation in ranking the documents. This allows supporting both task types surreptitiously without changing the familiar list-based interface. Search results include more diverse results when users are exploring and more precise results for lookup tasks. Users found more useful results in exploratory tasks when compared to a base-line system, which is specifically tuned for lookup tasks.

Narrow or Broad?: Estimating Subjective Specificity in Exploratory Search

Supporting exploratory search is a very challenging problem, not least because of the dynamic nature of the exercise: both the knowledge and interests of the user are subject to constant change. Moreover, whether the results for a query are informative is strongly subjective. What is informative to one user, is too specific for the other; specificity differs between users depending on their intent and accumulated knowledge about the domain. We propose a formal model - motivated by Information Foraging Theory - for predicting the subjective specificity of search results based on simple observables such as result-clicks. Through two studies including both controlled and free-form exploratory search we show our model allows us to differentiate between levels of subjective result specificity with regard to the current information need of the user.

Pervasive Social Computing: Augmenting Five Facets of Human Intelligence

Pervasive Social Computing is a novel collective paradigm, derived from pervasive computing, social media, social networking, social signal processing, etc. This paper reviews Pervasive Social Computing as an integrated computing environment, which promises to augment five facets of human intelligence in physical environment awareness, behavior awareness, community awareness, interaction awareness, and content awareness. Reviews of related studies are given and their generic architectures are derived. The resulting architecture for Pervasive Social Computing is presented. A prototype is developed and examined in order to investigate the characteristics exhibited by Pervasive Social Computing.

Inferring Cognitive Models from Data using Approximate Bayesian Computation

An important problem for HCI researchers is to estimate the parameter values of a cognitive model from behavioral data. This is a difficult problem, because of the substantial complexity and variety in human behavioral strategies. We report an investigation into a new approach using approximate Bayesian computation (ABC) to condition model parameters to data and prior knowledge. As the case study we examine menu interaction, where we have click time data only to infer a cognitive model that implements a search behaviour with parameters such as fixation duration and recall probability. Our results demonstrate that ABC (i) improves estimates of model parameter values, (ii) enables meaningful comparisons between model variants, and (iii) supports fitting models to individual users. ABC provides ample opportunities for theoretical HCI research by allowing principled inference of model parameter values and their uncertainty.

What's with the free images?: a study of Flickr's creative commons attribution images

Our survey of the Flickr photo sites Creative Commons attribution licensed images reveals that there is a wide variety of high quality of relevant stock images available. However, searching the images can be demanding since the image metadata is inconsistent. The main problem of finding open images is that the search tools are mostly based on user generated tags. The search results would benefit from human sorting and simple machine vision analysis. These steps might be able to close the gap between commercial stock photo and open image collections.

Groupsourcing: nudging users away from unsafe content

We present a system for aggregating feedback from social groups to deliver warnings about unsafe content, and describe our laboratory study to verify the effectiveness of such warnings.