Immersive Search: Comparing Conventional and Spatially Arranged Search Engine Result Pages in Immersive Virtual Environments

Advances in immersive technologies (e.g., virtual reality head-mounted displays) have brought a new dimension into user interfaces to increasingly more people in the recent years. However, little prior work has explored how people could use the extra dimension afforded by VR HMDs to aid in the information retrieval process. My dissertation research investigated how different task types and layouts of search engine result pages (displays) in immersive virtual environments impact the information retrieval process. In this dissertation, I present results from a within-subjects user study to investigate users' search behaviors, system interactions, perceptions, and eye-tracking behaviors for four different spatial arrangements of search results (``list'' - a 2D list; ``curve3'' - a 3x3 grid; ``curve4'' - a 4x4 grid; and ``sphere'' - a 4x4 sphere) in a VR HMD across two different task types (Find All relevant, Pick 3 best). Thirty-two (32) participants completed 5 search trials in 8 experimental conditions (4 displays x 2 task types). Results show that: (1) participants were accepting of and performed well in the spatial displays (curve3, curve4, and sphere); (2) participants had a positional bias for the top or top left of SERPs; (3) the angle of search results and layouts influenced the navigation patterns used; (4) participants had a preference for physical navigation (e.g., head movement) over virtual navigation (e.g., scrolling) to view and compare search results, and (5) participants were less likely to perceive a rank order in the spatial displays where a clear scan path was not obvious to them.Doctor of Philosoph

UNDERSTANDING, MODELING AND SUPPORTING CROSS-DEVICE WEB SEARCH

Recent studies have witnessed an increasing popularity of cross-device web search, in which users resume their previously-started search tasks from one device to later sessions on another. This novel search mode brings new user behaviors such as cross-device information transfer; however, they are rarely studied in recent research. Existing studies on this topic mainly focused on automatic cross-device search task extraction and/or task continuation prediction; whereas it lacks sufficient understanding of user behaviors and ways of supporting cross-device search tasks. Building an automated search support system requires proper models that can quantify user behaviors in the whole cross-device search process. This motivates me to focus on understanding, modeling and supporting cross-device search processes in this dissertation. To understand the cross-device search process, I examine the main cross-device search topics, the major triggers, the information transfer approaches, and users’ behavioral patterns within each device and across multiple devices. These are obtained through an on-line survey and a lab-controlled user study with fine-grained user behavior logs. Then, I work on two quantitative models to automatically capture users' behavioral patterns. Both models assume that user behaviors are driven by hidden factors, and the identified behavioral patterns are either the hidden factors or a reflection of hidden factors. Following prior studies, I consider two types of hidden factors --- search tactic (e.g., the tactic of information re-finding/finding would drive to click/skip previously-accessed documents) and user knowledge (e.g., knowing the knowledge within a document would drive users to skip the document). Finally, to create a real-world cross-device search support use case, I design two supporting functions: one to assist information re-finding and the other to support information finding. The effectiveness of different support functions are further examined through both off-line and on-line experiments. The dissertation has several contributions. First, this is the first comprehensive investigation of cross-device web search behaviors. Second, two novel computational models are proposed to automatically quantify cross-device search processes, which are rarely studied in existing researches. Third, I identify two important cross-device search support tasks and implement effective algorithms to support both of them, which can beneficial future studies for this topic

Deep Predictive Policy Training using Reinforcement Learning

Skilled robot task learning is best implemented by predictive action policies due to the inherent latency of sensorimotor processes. However, training such predictive policies is challenging as it involves finding a trajectory of motor activations for the full duration of the action. We propose a data-efficient deep predictive policy training (DPPT) framework with a deep neural network policy architecture which maps an image observation to a sequence of motor activations. The architecture consists of three sub-networks referred to as the perception, policy and behavior super-layers. The perception and behavior super-layers force an abstraction of visual and motor data trained with synthetic and simulated training samples, respectively. The policy super-layer is a small sub-network with fewer parameters that maps data in-between the abstracted manifolds. It is trained for each task using methods for policy search reinforcement learning. We demonstrate the suitability of the proposed architecture and learning framework by training predictive policies for skilled object grasping and ball throwing on a PR2 robot. The effectiveness of the method is illustrated by the fact that these tasks are trained using only about 180 real robot attempts with qualitative terminal rewards.Comment: This work is submitted to IEEE/RSJ International Conference on Intelligent Robots and Systems 2017 (IROS2017

Understanding “influence”: An exploratory study of academics’ process of knowledge construction through iterative and interactive information seeking

The motivation for this study is to better understand the searching and sensemaking processes undertaken to solve exploratory tasks for which people lack pre-existing frames. To investigate people’s strategies for that type of task, we focused on “influence” tasks because, although they appear to be unfamiliar, they arise in much academic discourse, at least tacitly. This qualitative study reports the process undertaken by academics of different levels of seniority to complete exploratory search tasks that involved identifying influential members of their academic community and “rising stars, ” and to identify similar roles in an unfamiliar academic community. 11 think-aloud sessions followed by semi-structured interviews were conducted to investigate the role of specific and general domain expertise in the process of information seeking and knowledge construction. Academics defined and completed the task through an iterative and interactive process of seeking and sensemaking, during which they constructed an understanding of their communities and determined qualities of “being influential”. Elements of the Data/Frame Theory of Sensemaking (Klein et al., 2007) were used as sensitising theoretical constructs. The study shows that both external and internal knowledge resources are essential to define a starting point or frame, make and support decisions, and experience satisfaction. Ill-defined or non-existent initial frames may cause unsubstantial or arbitrary decisions, and feelings of uncertainty and lack of confidence