Session-based social and dependency-aware software recommendation
Session-based social and dependency-aware software recommendation
复制标题
基于会话的社交和依赖感知软件推荐
DOI：
10.1016/j.asoc.2022.108463
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发表时间：
2021-03
影响因子：
8.7
通讯作者：
Qiang He
中科院分区：
文献类型：
--
作者：
Dengcheng Yan;Tianyi Tang;Wenxin Xie;Yiwen Zhang;Qiang He
随着现代软件复杂性的增加，社会化协作编码和开源软件包复用越来越普遍，从而大大提高了开发效率和软件质量。然而，开源软件包的爆炸性增长使开发人员面临信息过载的挑战。虽然这可以通过传统的推荐系统来解决，但它们通常不考虑社会编码的特定约束，例如开发人员之间的社会影响和软件包之间的依赖关系。在本文中，我们的目标是建立模型的动态利益的开发者与社会影响和依赖约束，并提出了基于会话的社会和依赖意识的软件推荐（SSDRec）模型。该模型将递归神经网络（RNN）和图注意力网络（GAT）集成到一个统一的框架中。RNN被用来模拟开发人员在每个会话中的短期动态兴趣，两个GAT被用来分别捕获来自朋友的社会影响和来自依赖软件包的依赖约束。在真实世界的数据集上进行了大量的实验，结果表明，我们的模型显着优于竞争对手的基线。
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.
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DOI：
10.1145/3132847.3133036
发表时间：
2017-11
期刊：
Proceedings of the 2017 ACM on Conference on Information and Knowledge Management
影响因子：
--
作者：
Jarana Manotumruksa;Craig Macdonald;I. Ounis
通讯作者：
Jarana Manotumruksa;Craig Macdonald;I. Ounis
DOI：
10.1109/suite.2009.5070014
发表时间：
2009-05
期刊：
2009 ICSE Workshop on Search-Driven Development-Users, Infrastructure, Tools and Evaluation
影响因子：
--
作者：
Makoto Ichii;Yasuhiro Hayase;Reishi Yokomori;Tetsuo Yamamoto;Katsuro Inoue
通讯作者：
Makoto Ichii;Yasuhiro Hayase;Reishi Yokomori;Tetsuo Yamamoto;Katsuro Inoue
DOI：
10.1145/3077136.3080753
发表时间：
2017-08
期刊：
Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval
影响因子：
--
作者：
Jyun-Yu Jiang;Pu-Jen Cheng;Wei Wang
通讯作者：
Jyun-Yu Jiang;Pu-Jen Cheng;Wei Wang
DOI：
10.1109/icnp.2012.6459977
发表时间：
2012-10
期刊：
2012 20th IEEE International Conference on Network Protocols (ICNP)
影响因子：
--
作者：
Hanhua Chen;Hai Jin;Ning Jin;Tao Gu
通讯作者：
Hanhua Chen;Hai Jin;Ning Jin;Tao Gu
DOI：
10.1109/tnnls.2016.2514368
发表时间：
2017-05-01
影响因子：
10.4
作者：
Deng, Shuiguang;Huang, Longtao;Wu, Zhaohui
通讯作者：
Wu, Zhaohui