Volume no :
6 |
Issue no :
1
Article Type :
Scholarly Article
Author :
Mrs.M.Sasikala
Published Date :
May, 2025
Publisher :
JournalSTAR
Page No: 1 - 12
Abstract : Community Question Answering (CQA) platforms have become critical knowledge-sharing hubs where users post questions and receive answers from the community. However, the overwhelming volume of content poses significant challenges in surfacing high-quality responses. To address this, we propose an advanced scalable learning algorithm that leverages post voting prediction and user behavior analysis to enhance answer ranking and quality assessment in CQA environments. The model integrates multi-modal features, including textual semantics, temporal activity, user credibility, and historical voting trends, to predict the likelihood of an answer receiving positive votes. A hybrid deep learning framework is employed, combining Graph Neural Networks (GNNs) to model user–post interactions and attention-based mechanisms to weigh feature importance. The system is designed to scale efficiently across large datasets and dynamically adapt to changing user behavior patterns. Experimental results on real-world CQA datasets such as Stack Overflow and Quora demonstrate significant improvements in post quality prediction accuracy and user trust modeling, thereby fostering more effective and reliable content curation on CQA platforms.
Keyword: Community Question Answering (CQA), Post Voting Prediction, User Behavior Analysis, Scalable Learning Algorithm, Graph Neural Networks, Answer Quality Prediction, Deep Learning, User Credibility Modeling, Content Ranking, Social Computing.
Reference:

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Advanced Scalable Learning Algorithm

One of the core challenges in CQA systems is the lack of real-time, scalable methods for
predicting which answers are most likely to be useful or upvoted by the community. Traditional
ranking algorithms based primarily on textual similarity or heuristic-based metrics often fail to
capture the nuanced factors that influence voting behavior, such as the credibility of the user,
answer timing, linguistic quality, and engagement patterns. Moreover, many existing methods
treat posts in isolation and ignore the complex graph of interactions between users, questions,
and answers, limiting their capacity to generalize in large-scale, dynamic environments Advanced Scalable Learning Algorithm.

Introduction


To address these limitations, this paper proposes an Advanced Scalable Learning Algorithm
that fuses post voting prediction and user behavior analysis to improve the ranking and
recommendation of answers in CQA platforms Advanced Scalable Learning Algorithm. The core hypothesis of this research is that by
learning from both semantic content features and social-behavioral patterns, the model can
more accurately predict the likelihood of a post receiving positive community feedback. This, in
turn, can be used to proactively surface high-quality answers even before a substantial
number of votes accumulate Advanced Scalable Learning Algorithm.

Advanced Scalable Learning


Our proposed model introduces several innovations. First, we extract a rich set of features from
both the content and the context of answers—these include textual embeddings, user activity
metrics, historical voting trends, and question-answer pair relationships. Second, we employ
Graph Neural Networks (GNNs) to model the relational structure of the community,
representing users and posts as nodes and their interactions as edges. This allows the model to
exploit topological signals that are otherwise inaccessible to conventional neural networks.
Third, an attention-based mechanism is integrated into the learning architecture to
dynamically weigh different features based on their contribution to voting outcomes, improving
interpretability and performance. Scalability is a central focus of this work. To ensure the system remains effective in real-time
environments, the model architecture is optimized for distributed processing and incremental
updates, making it suitable for deployment in large-scale CQA platforms. The algorithm also
adapts to evolving user behavior by incorporating temporal dynamics and behavior drift
modeling, ensuring sustained accuracy over time.

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Advanced Scalable Learning Algorithm Fig. 1: Medical Textual Question Answering Systems Based on Deep Learning Approaches.