A recommender system is tasked with effectively analyzing a user’s preferences and interactions to provide personalized recommendations. This calls for extracting and connecting various heterogeneous data while preserving their temporal relations. Graph neural networks (GNNs) have proven to be highly suitable in recommendation systems for connecting different types of user behavioral signals. However, they inherently lack ability to capture temporal aspects of underlying data. This shortcoming prevents them from explicating and utilizing task information, which is shown to be instrumental in many information retrieval applications. To overcome this limitation, we propose a new Task-based Graph Neural Network model (TGNN) focusing on identifying users’ underlying tasks within their temporal multi-behavior, specifically in each session. The model consists of three modules: (1) a sequential meta-path module that captures a temporal sequence of users’ behaviors; (2) a graph neural network layer that models the relationships between different information items and users into task representations; and (3) a recommendation layer that utilizes a collaborative filtering method to generate top-N recommendations based on the model’s comprehension of users’ tasks. The novelty of our approach lies in understanding users’ tasks through their temporal behavior, enabling more accurate personalization. The results of evaluative experiments on three publicly available datasets demonstrate the effectiveness of our task-based recommendation model compared to 10 baselines and indicate a promising research direction for task-oriented recommender systems.

Despite the considerable advancements in modern search systems for assisting users in search tasks of varying types, support for complex tasks that call for multi-round interactions remains challenging. Identifying users’ tasks is essential to understanding their evolving information needs and search goals during search sessions to simulate and achieve real-time adaptive search retrievals; thus, it is a crucial research thrust in interactive information retrieval (IIR). While a series of descriptive and formal models have been proposed to characterize complex information search sessions, only a few focus on leveraging dynamic task features in search personalizations to support users in different task stages in an adaptive fashion. This preliminary study presents a heterogeneous graph neural network model for extracting and representing tasks to better understand users’ interactive search processes by connecting tasks with search interactions. Our approach’s novelty lies in our application of task representation learning, which enables systems to extract hidden task information from users’ search behaviors. The results of our evaluative experiments on TREC Session track data highlight the value of our proposed task representation model and illustrate a promising research direction on task-oriented intelligent systems.

The importance of tasks in information retrieval (IR) has been long argued for, addressed in different ways, often ignored, and frequently revisited. For decades, scholars made a case for the role that a user’s task plays in how and why that user engages in search and what a search system should do to assist. But for the most part, the IR community has been too focused on query processing and assuming a search task to be a collection of user queries, often ignoring if or how such an assumption addresses the users accomplishing their tasks. With emerging areas of conversational agents and proactive IR, understanding and addressing users’ tasks has become more important than ever before. In this paper, we provide various perspectives on where the state-of-the-art is with regard to tasks in IR, what are some of the bottlenecks in deriving and using task information, and how do we go forward from here. In addition to covering relevant literature, the paper provides a synthesis of historical and current perspectives on understanding, extracting, and addressing task-focused search. To ground ongoing and future research in this area, we present a new framing device for tasks using a tree-like structure and various moves on that structure that allow different interpretations and applications. Presented as a combination of synthesis of ideas and past works, proposals for future research, and our perspectives on technical, social, and ethical considerations, this paper is meant to help revitalize the interest and future work in task-based IR.

For users working on a complex search task, it is common to address different goals at various stages of the task through query iterations. While addressing these goals, users go through different task states as well. Understanding these task states latent under users' interactions is crucial in identifying users' changing intents and search behaviors to simulate and achieve real-time adaptive search recommendations and retrievals. However, the availability of sizeable real-world web search logs is scarce due to various ethical and privacy concerns, thus often challenging to develop generalizable task-aware computation models. Furthermore, session logs with task state labels are rarer. For many researchers who lack the resources to directly and at scale collect data from users and conduct a time-consuming data annotation process, this becomes a considerable bottleneck to furthering their research. Synthetic search sessions have the potential to address this gap. This paper shares a parsimonious model to simulate synthetic web search sessions with task state information, which interactive information retrieval (IIR) and search personalization studies could utilize to develop and evaluate task-based search and retrieval systems.

Most of the existing search and recommender systems do not adequately support complex search tasks. However, according to the theories and empirical evidence from interactive information seeking and retrieval studies, in people's search behaviors, actions, and outcomes, tasks that trigger the information search process in the first place generate different search actions. Also, defined successes and failures to find information and accomplish tasks are relative to the motivating task end goals, rather than the fulfillment of subtasks and goals of query segments along the search process. Based on this idea, the primary purpose of the proposed research is to explore how to leverage users' search behaviors and actions in developing methods to construct task representations that can be applied in all search contexts by conducting field studies. Then based on the data collected from the user studies, evaluate the task representations in designing search and recommendation techniques in naturalistic settings to examine the extent to which the context-independent task approach can approximate users' actual tasks and provide task-specific recommendations. The research will adopt a multi-disciplinary, multi-method approach with human-centered ML-based techniques in the process. The outcomes can help us better understand users' tasks and thus have implications for the task, user modeling, and system recommendations design.

Complex search tasks that involve uncertain solution space and multi-round search iterations are integral to everyday life and information-intensive workplace practices, affecting how people learn, work, and resolve problematic situations. However, current search systems still face plenty of challenges when applied in supporting users engaging in complex search tasks. To address this issue, we seek to explore the dynamic nature of complex search tasks from process-oriented perspective by identifying and predicting implicit task states. Specifically, based upon the Web search logs and user annotation data (regarding information seeking intentions in local search steps, in-situ search problems, and help needed) collected from 132 search sessions in two controlled lab studies, we developed two task state frameworks based on intention state and problem-help state respectively and examined the connection between task states and search behaviors. We report that (1) complex search tasks of different types can be deconstructed and disambiguated based on the associated nonlinear state transition patterns; and (2) the identified task states that cover multiple subtle factors of user cognition can be predicted from search behavioral signals using supervised learning algorithms. This study reveals the way in which complex search tasks are unfolded and manifested in users' search interactions and paves the way for developing state-aware adaptive search supports and system evaluation frameworks.

This paper examines how individuals judge the accuracy, adequacy, relevance, and trustworthiness of different types of impersonal and interpersonal information sources and how task type influences their evaluation process. 53 participants from diverse backgrounds recruited via Amazon»s Mechanical Turk performed four simulated information seeking tasks. This study analyzed the data collected from participants» self-reported information seeking experiences in online logbooks and follow-up semi-structured interviews with 23 participants by applying both qualitative and quantitative methods. The findings suggest that task type and information source type affect individuals» information quality judgment, and they perceive websites are more accurate than interpersonal sources, though the latter can be trustworthy. Moreover, their understanding of the type of information also affects their quality judgment. For example, they prefer factual information to opinions in some situations.

Information source selection is essential to individuals' information seeking behaviors. Existing studies have focused on the criteria seekers employ when choosing information sources, such as sources' accessibility and quality, as well as the contextual factors that shape a seeker's selection. However, existing findings are somewhat conflicting and lack in-depth understanding of the reasons behind individuals' choices. The study reported here invited 53 participants from diverse backgrounds to perform simulated information seeking tasks over a two-day period and to report their experiences and findings in an online logbook. Semi-structured interviews with 23 of them were also conducted in order to examine the issues that arose from the logbooks. Our preliminary findings present several factors associated with participants' choices between impersonal and interpersonal sources. While interpersonal sources are deemed to be more suitable in capturing the context of an information query and providing personalized information, impersonal sources are found to be more accessible under a time constraint while depicting no emotion towards sensitive issues.