paper2repo: GitHub Repository Recommendation for Academic Papers

Abstract

GitHub has become a popular social application platform, where a large number of users post their open source projects. In particular, an increasing number of researchers release repositories of source code related to their research papers in order to attract more people to follow their work. Motivated by this trend, we describe a novel item-item cross-platform recommender system, $\text{paper2repo}$, that recommends relevant repositories on GitHub that match a given paper in an academic search system such as Microsoft Academic. The key challenge is to identify the similarity between an input paper and its related repositories across the two platforms, $\text{without the benefit of human labeling}$. Towards that end, paper2repo integrates text encoding and constrained graph convolutional networks (GCN) to automatically learn and map the embeddings of papers and repositories into the same space, where proximity offers the basis for recommendation. To make our method more practical in real life systems, labels used for model training are computed automatically from features of user actions on GitHub. In machine learning, such automatic labeling is often called {\em distant supervision\/}. To the authors' knowledge, this is the first distant-supervised cross-platform (paper to repository) matching system. We evaluate the performance of paper2repo on real-world data sets collected from GitHub and Microsoft Academic. Results demonstrate that it outperforms other state of the art recommendation methods.

References in corpus (10)

• LINE: Large-scale Information Network Embedding
• BPR: Bayesian Personalized Ranking from Implicit Feedback
• Graph Convolutional Neural Networks for Web-Scale Recommender Systems
• KGAT: Knowledge Graph Attention Network for Recommendation
• Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
• Knowledge Graph Convolutional Networks for Recommender Systems
• How Powerful are Graph Neural Networks?
• Item Silk Road: Recommending Items from Information Domains to Social Users
• Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks
• Cross-domain novelty seeking trait mining for sequential recommendation

Cited by in corpus (5)

• CATA++: A Collaborative Dual Attentive Autoencoder Method for Recommending Scientific Articles
• Code Recommendation for Open Source Software Developers
• CSSR: A Context-Aware Sequential Software Service Recommendation Model
• Benchmarking Recommendation, Classification, and Tracing Based on Hugging Face Knowledge Graph
• An Explanatory Query-Based Framework for Exploring Academic Expertise