An Automated Recommendation System for Crowdsourcing Data Using Improved Heuristic-Aided Residual Long Short-Term Memory

Abstract

In recent years, crowdsourcing has developed into a business production paradigm and a distributed problem-solving platform. However, the conventional machine learning models failed to assist both requesters and workers in finding the proper jobs that affect better quality outputs. The traditional large-scale crowdsourcing systems typically involve a lot of microtasks, and it requires more time for a crowdworker to search a work on this platform. Thus, task suggestion methods are more useful. Yet, the traditional approaches do not consider the cold-start issue. To tackle these issues, in this paper, a new recommendation system for crowdsourcing data is implemented utilizing deep learning. Initially, from the standard online sources, the crowdsourced data are accumulated. The novelty of the model is to propose an adaptive residual long short-term memory (ARes-LSTM) that learns the task's latent factor via the task features rather than the task ID. Here, this network's parameters are optimized by the fitness-based drawer algorithm (F-DA) to improve the efficacy rates. Further, the suggested ARes-LSTM is adopted to detect the user's preference score based on the user's historical behaviors. According to the historical behavior records of the users and task features, the ARes-LSTM provides personalized task recommendations and rectifies the issue of cold-start. From the outcomes, the better accuracy rate of the implemented model is 91.42857. Consequently, the accuracy rate of the traditional techniques such as AOA, TSA, BBRO, and DA is attained as 84.07, 85.42, 87.07, and 90.07. Finally, the simulation of the implemented recommendation system is conducted with various traditional techniques with standard efficiency metrics to show the supremacy of the designed recommendation system. Thus, it is proved that the developed recommendation system for the crowdsourcing data model chooses intended tasks based on individual preferences that can help to enlarge the number of chances to engage in crowdsourcing efforts across a broad range of platforms.