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Erschienen in:
Stay on Topic, Please: Aligning User Comments to the Content of a News Article Kapitel lesen Erstes Kapitel lesen

2021 | OriginalPaper | Buchkapitel

User Engagement Prediction for Clarification in Search

verfasst von : Ivan Sekulić, Mohammad Aliannejadi, Fabio Crestani

Erschienen in: Advances in Information Retrieval

Verlag: Springer International Publishing

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Abstract

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.

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Vorheriges Kapitel Causality-Aware Neighborhood Methods for Recommender Systems
Nächstes Kapitel Sentiment-Oriented Metric Learning for Text-to-Image Retrieval
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Metadaten
Titel
User Engagement Prediction for Clarification in Search
verfasst von
Ivan Sekulić
Mohammad Aliannejadi
Fabio Crestani
Copyright-Jahr
2021
Buch
Advances in Information Retrieval

Print ISBN: 978-3-030-72112-1

Electronic ISBN: 978-3-030-72113-8

Copyright-Jahr: 2021

DOI
https://doi.org/10.1007/978-3-030-72113-8_41