Best-Answer Prediction in Q&A Sites Using User Information
This work addresses the limitation of underutilizing user background in best-answer prediction for users of platforms like Stack Exchange, though it is incremental by building on existing methods.
The paper tackles the problem of predicting the best answers in Community Question Answering sites by incorporating the questioner's background information and user relationships, achieving results that complement previous methods as validated on the Stack Exchange dataset using AUC metrics.
Community Question Answering (CQA) sites have spread and multiplied significantly in recent years. Sites like Reddit, Quora, and Stack Exchange are becoming popular amongst people interested in finding answers to diverse questions. One practical way of finding such answers is automatically predicting the best candidate given existing answers and comments. Many studies were conducted on answer prediction in CQA but with limited focus on using the background information of the questionnaires. We address this limitation using a novel method for predicting the best answers using the questioner's background information and other features, such as the textual content or the relationships with other participants. Our answer classification model was trained using the Stack Exchange dataset and validated using the Area Under the Curve (AUC) metric. The experimental results show that the proposed method complements previous methods by pointing out the importance of the relationships between users, particularly throughout the level of involvement in different communities on Stack Exchange. Furthermore, we point out that there is little overlap between user-relation information and the information represented by the shallow text features and the meta-features, such as time differences.