A Developer Recommendation Algorithm Based on Multi-Relationship Knowledge Enhancement

In recent years, collaborative software development has gradually become an emerging software development model. However, with the continuous development of crowdsourcing platforms, the problem of information overload has become increasingly serious, and it has become crucial to recommend suitable developers for tasks. Traditional recommendation methods face two major challenges: first, the text features of tasks and developers are highly concise and contain a large number of entities; second, tasks are one-time and explicit interactive data is sparse. To solve these challenges, this paper proposes a developer recommendation algorithm based on multi-relationship knowledge enhancement. We identify entities and contextual entities from text features and link the relationship between tasks and developers from a knowledge perspective. In addition, we treat developers' participation in registering, submitting, and winning tasks as different preferences, and assign different weights to each relationship. Finally, we enhance developers' feature representation using multi-relationship neighborhood aggregation. We conducted extensive experiments on a real Topcoder dataset, and the results show that our method is significantly better than the other four comparison methods in accuracy and MRR.