Related papers: Recommender Systems for Software Project Managers
Recommender systems is set up to address the issue of information overload in traditional information retrieval systems, which is focused on recommending information that is of most interest to users from massive information. Generally,…
In the last decade we have observed a mass increase of information, in particular information that is shared through smartphones. Consequently, the amount of information that is available does not allow the average user to be aware of all…
Recommender systems aim to enhance the overall user experience by providing tailored recommendations for a variety of products and services. These systems help users make more informed decisions, leading to greater user engagement with the…
End users positive response is essential for the success of any software. This is true for both commercial and Open Source Software (OSS). OSS is popular not only because of its availability, which is usually free but due to the user…
Deep learning based recommender systems have been extensively explored in recent years. However, the large number of models proposed each year poses a big challenge for both researchers and practitioners in reproducing the results for…
Recommendation system is such a platform that helps people to easily find out the things they need within a few seconds. It is implemented based on the preferences of similar users or items. In this digital era, the internet has provided us…
With the development of recommender systems (RS), several promising systems have emerged, such as context-aware RS, multi-criteria RS, and group RS. However, the education domain may not benefit from these developments due to missing…
Strategic recommendations (SR) refer to the problem where an intelligent agent observes the sequential behaviors and activities of users and decides when and how to interact with them to optimize some long-term objectives, both for the user…
With the emergence of personality computing as a new research field related to artificial intelligence and personality psychology, we have witnessed an unprecedented proliferation of personality-aware recommendation systems. Unlike…
The use of packaged libraries can significantly shorten the software development cycle by improving the quality and readability of code. In this paper, we present a recommendation engine called Librarian for open source libraries. A…
Particle accelerator projects share many characteristics with industrial projects. However, experience has shown that best practice of industrial project management is not always well suited to particle accelerator projects. Major…
Developers often struggle to navigate an Open Source Software (OSS) project's issue-tracking system and find a suitable task. Proper issue labeling can aid task selection, but current tools are limited to classifying the issues according to…
Research Software Engineering is the application of professional software skills to research problems. Those who do this are called Research Software Engineers or RSEs for short. RSEs work closely with researchers in a collaborative fashion…
In the area of recommender systems, the vast majority of research efforts is spent on developing increasingly sophisticated recommendation models, also using increasingly more computational resources. Unfortunately, most of these research…
Beyond sharing datasets or simulations, we believe the Recommender Systems (RS) community should share Task Environments. In this work, we propose a high-level logical architecture that will help to reason about the core components of a RS…
A significant remaining challenge for existing recommender systems is that users may not trust the recommender systems for either lack of explanation or inaccurate recommendation results. Thus, it becomes critical to embrace a trustworthy…
With the advancement in the technology sector spanning over every field, a huge influx of information is inevitable. Among all the opportunities that the advancements in the technology have brought, one of them is to propose efficient…
Recent years have witnessed the rapid development of service-oriented computing technologies. The boom of Web services increases software developers' selection burden in developing new service-based systems such as mashups. Timely…
Sequential recommender systems (SRS) could capture dynamic user preferences by modeling historical behaviors ordered in time. Despite effectiveness, focusing only on the \textit{collaborative signals} from behaviors does not fully grasp…
Incorporating Machine Learning (ML) into existing systems is a demand that has grown among several organizations. However, the development of ML-enabled systems encompasses several social and technical challenges, which must be addressed by…