Effective Hybrid Content-Based Collaborative Filtering Approach for Requirements Engineering

Abstract

Requirements engineering (RE) is among the most valuable and critical processes in software development. The quality of this process significantly affects the success of a software project. An important step in RE is requirements elicitation, which involves collecting project-related requirements from different sources. Repositories of reusable requirements are typically important sources of an increasing number of reusable software requirements. However, the process of searching such repositories to collect valuable project-related requirements is time-consuming and difficult to perform accurately. Recommender systems have been widely recognized as an effective solution to such problem. Accordingly, this study proposes an effective hybrid content-based collaborative filtering recommendation approach. The proposed approach will support project stakeholders in mitigating the risk of missing requirements during requirements elicitation by identifying related requirements from software requirement repositories. The experimental results on the RALIC dataset demonstrate that the proposed approach considerably outperforms baseline collaborative filtering-based recommendation methods in terms of prediction accuracy and coverage in addition to mitigating the data sparsity and cold-start item problems.

Systematic Review of Nutritional Recommendation Systems

“…Answering question Q4 defined in Table 1, if we focus on the different filtering methods used by the analyzed systems, we can see a tendency in the use of the collaborative filter. This is not a common result; normally content-based filters are used as starting filtering methods [32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47], and once a considerable amount of data from the users are obtained, collaborative filters are launched. This might be because some studies were started with some initial data about the users.…”

A doctor recommender system based on collaborative and content filtering

“…The switching hybridization strategy is employed in this module to obtain the final predicted rating depending on certain conditions, as shown by (12). A weighted harmonic mean aggregation method is used to aggregate the predicted values.…”

“…Each recommendation approach has its advantages and drawbacks; for instance, CF has data sparsity and cold-start issues, whereas content-based has overspecialized recommendations. Hybrid recommendation approaches, which combine the best qualities of two or more recommendation approaches, have been developed to overcome their challenges [10]- [12].…”

A Hotel Recommender System Based on Multi-Criteria Collaborative Filtering

“…They are used to filter information from various sources and predict the output based on related information about the users, the items, and the interactions between them. These systems have been increasingly popular for a variety of real-world applications in e-government, e-commerce, e-business, e-learning, e-library, e-tourism, and e-health [21,25,[27][28][29][30]. Neighborhood-based Collaborative Filtering (CF) approaches, also referred to as memory-based approaches, were among the earliest algorithms developed for recommender systems.…”