Session-based Social and Dependency-aware Software Recommendation

TL;DR

This paper introduces SSDRec, a novel session-based recommendation model that integrates RNN and GAT to consider social influence and dependency constraints, improving software package recommendations.

Contribution

The paper proposes a unified RNN-GAT framework for modeling developer interests considering social and dependency factors, advancing software recommendation methods.

Findings

SSDRec outperforms baseline models in real-world datasets.

Incorporating social influence improves recommendation accuracy.

Dependency constraints enhance relevance of suggested packages.

Abstract

With the increase of complexity of modern software, social collaborative coding and reuse of open source software packages become more and more popular, which thus greatly enhances the development efficiency and software quality. However, the explosive growth of open source software packages exposes developers to the challenge of information overload. While this can be addressed by conventional recommender systems, they usually do not consider particular constraints of social coding such as social influence among developers and dependency relations among software packages. In this paper, we aim to model the dynamic interests of developers with both social influence and dependency constraints, and propose the Session-based Social and Dependency-aware software Recommendation (SSDRec) model. This model integrates recurrent neural network (RNN) and graph attention network (GAT) into a unified framework. An RNN is employed to model the short-term dynamic interests of developers in each session and two GATs are utilized to capture social influence from friends and dependency constraints from dependent software packages, respectively. Extensive experiments are conducted on real-world datasets and the results demonstrate that our model significantly outperforms the competitive baselines.