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Tag: Complex search tasks

Developing a search system that knows what you are looking for before you do.

Developing a search system that knows what you are looking for before you do.

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Have you ever searched to plan a trip, a wedding, job hunting, or your next apartment? This kind of search can take hours, days, or even weeks. Inevitably, it would get interrupted by our daily life routine. The interrupted events can be a break for coffees, hopping into the restroom, dining, or sleeping. Therefore, doing the search would require us to pick up where we left off. These kinds of searches are called “Interrupted Search Tasks”. 

We, as well as many other scientists, are working on tackling this problem. Our approach is to try to identify and predict the sub-tasks of complex search tasks. Based on that, we provide solutions to easily complete the tasks. For example, planning a wedding. You need different information i.e., food, dress, venue. Maybe, in the search process, you forget about the food which is a subtask of wedding planning. The system proactively gives suggestions for food. 

And how do we know when to suggest these things to you? First, we try to identify whether or not you are encountering problems during a search. We found that the longer people take at the search result page the higher chance they are having problems. Making this more illustrative, imagine a person searching “Churches in Seattle”, they took a long time on the research result page, and without clicking through any of the results, the person inputs another search query, “places for a wedding”, and so on. The more queries the person puts in without clicking through any pages reflect the likelihood they are encountering problems. However, if the person interacts with the result page, i.e. click the see inside the page, we would look at the number of pages that the person bookmarked. The more subsequence pages got bookmarked, the more relevant results the person found and the fewer problems they encountered. This is how we can tell whether people can find what they are looking for. If we see you are having problems, we will recommend things that you might miss out.

So how do we know what things you missed out? In other words, how do we know that “food”, “dress”, “venue” is related to planning a wedding? We use what people have searched for in the past. The higher frequency the 2 topics are searched together, the stronger the relationship. Let’s say 1000 people searched for “wedding” along with “dress food” vs. 5 people searched for “wedding” along with “black dress”. We can tell that “dress food” has a stronger relationship to the topic “wedding” but not so much with “Black dress”. Therefore, we can recommend “dress food” when the next person searches for “wedding.”

If you are interested to know more detail about this topic. Here is the paper that we published recently, “Identifying and Predicting the States of Complex Search Tasks”.

Jiqun Liu successfully defends his dissertation

Jiqun Liu successfully defends his dissertation

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Jiqun Liu, Ph.D. student

Our Ph.D. student, Jiqun Liu, has successfully defended his dissertation titled “A State-Based Approach to Supporting Users in Complex Search Tasks”. The committee included  Chirag Shah (University of Washington, Chair), Nick Belkin (Rutgers University), Kaitlin Costello (Rutgers University), and Dan Russell (Google).

Liu’s study focuses on understanding the multi-round search processes of complex search tasks by using computational models of interactive IR and develop personalized recommendations to support task completion and search satisfaction. From the study, the team built a search recommendation model based on Q-learning algorithm. The results demonstrated that the simulated search episodes can improve search efficiency to many extents.

Abstract

Previous work on task-based interactive information retrieval (IR) has mainly focused on what users found along the search process and the predefined, static aspects of complex search tasks (e.g., task goal, task product, cognitive task complexity), rather than how complex search tasks of different types can be better understood, examined, and disambiguated within the associated multi-round search processes. Also, it is believed that the knowledge about users’ cognitive variations in task-based search process can help tailor search paths and experiences to support task completion and search satisfaction. To adaptively support users engaging in complex search tasks, it is critical to connect theoretical, descriptive frameworks of search process with computational models of interactive IR and develop personalized recommendations for users according to their task states. Based on the data collected from two laboratory user studies, in this dissertation we sought to understand the states and state transition patterns in complex search tasks of different types and predict the identified task states using Machine Learning (ML) classifiers built upon observable search behavioral features. Moreover, through running Q-learning-based simulation of adaptive search recommendations, we also explored how the state-based framework could be applied in building computational models and supporting users with timely recommendations.

Based on the results from the dissertation study, we identified four intention-based task states and six problem-help-based task states, which depict the active, planned dimension and situational, unanticipated dimension of search tasks respectively. We also found that 1) task state transition patterns as features extracted from interaction process could be useful for disambiguating different types of search tasks; 2) the implicit task states can be inferred and predicted using behavioral-feature-based ML classifiers. With respect to application, we built a search recommendation model based on Q-learning algorithm and the knowledge we learned about task states. Then we apply the model in simulating search sessions consisting of potentially useful query segments with high rewards from different users. Our results demonstrated that the simulated search episodes can improve search efficiency to varying extents in different task scenarios. However, in many task contexts, this improvement often comes with the price of hurting the diversity and fairness in information coverage.

This dissertation presents a comprehensive study on state-based approach to understanding and supporting complex search tasks: from task state and state transition pattern identification, task state prediction, all the way to the application of computational state-based model in simulating dynamic search recommendations. Our process-oriented, state-based framework can be further extended with studies in a variety of contexts (e.g., multi-session search, collaborative search, conversational search) and deeper knowledge about users’ cognitive limits and search decision-making.

Tackling Complex Search Tasks

Tackling Complex Search Tasks

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To tackle the complex search tasks, we try to identify task’ states and study the connection between states and search behaviors. We found that the task state can be predicted from the user’s search behaviors. Read about our study in this article by Jiqun Liu, Shawon Sarkar, and Chirag Shah:

Liu, J., Sarkar, S., & Shah, C. (2020, March). Identifying and Predicting the States of Complex Search Tasks. In Proceedings of the 2020 Conference on Human Information Interaction and Retrieval (pp. 193-202).

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.