Journal of Innovations in Computer Science and Engineering (JICSE)

Abstract Developer expertise is a critical component of community-based question-answering platforms like Stack Overflow. However, expertise is not monolithic. Developers exhibit different "shapes" of expertise, such as deep specialists (I-shaped) or broad generalists with one specialty (T-shaped). While prior research has examined developer expertise and answer quality independently, how different expertise structures influence preferences for specific answer characteristics remains insufficiently understood. This paper investigates the relationship between a developer's expertise shape and their preference for specific answer characteristics.

We present a large-scale empirical study of over 48,000 Stack Overflow users, classifying them into I-shaped, T-shaped, Pi-shaped, and Comb-shaped profiles based on the distribution of tag-level reputation, following established expertise-shape modeling approaches. We then analyze the types of answers these user profiles tend to upvote and accept as solutions, focusing on characteristics such as answer length, inclusion of code snippets, use of images, and citation of external references.

Using separate analyses for community-level (upvotes) and task-resolution-level (accepted answers) preference signals, our findings reveal distinct and systematic differences across expertise shapes. I-shaped specialists favor technically deep, code-heavy answers, while T-shaped and Comb-shaped experts show a preference for more summarized, conceptual answers that include diagrams or references. These patterns are consistent across robustness checks and sensitivity analyses.

The results highlight that answer usefulness is user-dependent rather than universal, and they can help improve expertise-aware answer recommendation systems and foster more effective knowledge sharing on collaborative platforms.

Abstract Abstract— Telecommunications companies rely on recommendation systems to deliver personalized services and enhance customer satisfaction. Traditional methods, such as Collaborative Filtering (CF) and Content-Based Filtering (CBF), often fall short in capturing the complex relationships and social influences inherent in large telecom networks. In this paper, we propose a novel Graph Neural Network (GNN)-based recommendation system that integrates customer profiles with graph data representing customer interactions (e.g., calls, messages). The system uses the GraphSAGE architecture to aggregate information from each customer’s network, enabling it to learn from both direct and indirect relationships. By combining customer demographic and usage data with interaction networks, our model provides more accurate and personalized service recommendations.

We evaluate the system on a real-world telecom dataset, comparing it with traditional models, including CF, CBF, and Matrix Factorization (MF). The GNN-based system achieves a significant performance boost, with a precision of 0.81 and an F1-score of 0.80, outperforming all baselines. These results highlight the ability of GNNs to capture social and communication patterns, making them highly effective for telecom recommendations. Future work will explore the scalability of the system and its application to real-time data, further enhancing its potential for customer retention and revenue growth.