SE-PQA: Personalized Community Question Answering
Pranav Kasela, Marco Braga, Gabriella Pasi, Raffaele Perego

TL;DR
This paper introduces SE-PQA, a large, annotated dataset for personalized community question answering, demonstrating that personalization enhances model effectiveness and robustness across multiple communities.
Contribution
The paper presents SE-PQA, a new dataset with over 1 million queries and 2 million answers, enabling large-scale evaluation of personalized cQA models and baseline methods.
Findings
Personalization significantly improves model effectiveness.
Combining data from multiple communities enhances robustness.
SE-PQA is suitable for training effective cQA models.
Abstract
Personalization in Information Retrieval is a topic studied for a long time. Nevertheless, there is still a lack of high-quality, real-world datasets to conduct large-scale experiments and evaluate models for personalized search. This paper contributes to filling this gap by introducing SE-PQA (StackExchange - Personalized Question Answering), a new curated resource to design and evaluate personalized models related to the task of community Question Answering (cQA). The contributed dataset includes more than 1 million queries and 2 million answers, annotated with a rich set of features modeling the social interactions among the users of a popular cQA platform. We describe the characteristics of SE-PQA and detail the features associated with questions and answers. We also provide reproducible baseline methods for the cQA task based on the resource, including deep learning models and…
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Taxonomy
TopicsExpert finding and Q&A systems · Recommender Systems and Techniques · Topic Modeling
