Log In Sign Up

User Engagement Prediction for Clarification in Search

by   Ivan Sekulić, et al.

Clarification is increasingly becoming a vital factor in various topics of information retrieval, such as conversational search and modern Web search engines. Prompting the user for clarification in a search session can be very beneficial to the system as the user's explicit feedback helps the system improve retrieval massively. However, it comes with a very high risk of frustrating the user in case the system fails in asking decent clarifying questions. Therefore, it is of great importance to determine when and how to ask for clarification. To this aim, in this work, we model search clarification prediction as user engagement problem. We assume that the better a clarification is, the higher user engagement with it would be. We propose a Transformer-based model to tackle the task. The comparison with competitive baselines on large-scale real-life clarification engagement data proves the effectiveness of our model. Also, we analyse the effect of all result page elements on the performance and find that, among others, the ranked list of the search engine leads to considerable improvements. Our extensive analysis of task-specific features guides future research.


page 1

page 2

page 3

page 4


Ranking Clarifying Questions Based on Predicted User Engagement

To improve online search results, clarification questions can be used to...

Predicting User Engagement Status for Online Evaluation of Intelligent Assistants

Evaluation of intelligent assistants in large-scale and online settings ...

Feature Selection and Model Comparison on Microsoft Learning-to-Rank Data Sets

With the rapid advance of the Internet, search engines (e.g., Google, Bi...

Learning to Truncate Ranked Lists for Information Retrieval

Ranked list truncation is of critical importance in a variety of profess...

Towards User Engagement Dynamics in Social Networks

The engagement of each user in a social network is an essential indicato...

Engagement Detection with Multi-Task Training in E-Learning Environments

Recognition of user interaction, in particular engagement detection, bec...

Behavior-based evaluation of session satisfaction

Nowadays, web search becomes more and more popular all over the world. M...