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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

You Get What You Chat: Using Conversations to Personalize Search-Based Recommendations

verfasst von : Ghazaleh H. Torbati, Andrew Yates, Gerhard Weikum

Erschienen in: Advances in Information Retrieval

Verlag: Springer International Publishing

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Abstract

Prior work on personalized recommendations has focused on exploiting explicit signals from user-specific queries, clicks, likes and ratings. This paper investigates tapping into a different source of implicit signals of interests and tastes: online chats between users. The paper develops an expressive model and effective methods for personalizing search-based entity recommendations. User models derived from chats augment different methods for re-ranking entity answers for medium-grained queries. The paper presents specific techniques to enhance the user models by capturing domain-specific vocabularies and by entity-based expansion. Experiments are based on a collection of online chats from a controlled user study covering three domains: books, travel, food. We evaluate different configurations and compare chat-based user models against concise user profiles from questionnaires. Overall, these two variants perform on par in terms of NCDG@20, but each has advantages on certain domains.

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Vorheriges Kapitel Disparate Impact in Item Recommendation: A Case of Geographic Imbalance
Nächstes Kapitel Joint Autoregressive and Graph Models for Software and Developer Social Networks
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Metadaten
Titel
You Get What You Chat: Using Conversations to Personalize Search-Based Recommendations
verfasst von
Ghazaleh H. Torbati
Andrew Yates
Gerhard Weikum
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_14

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