The recommender system predicts user preferences by mining user historical behavior data. This paper proposes a social recommendation combining trust relationship and distance metric factorization. On the one hand, the recommender system has a cold start problem, which can be effectively alleviated by adding social relations. Simultaneously, to improve the problem of sparse trust matrix, we use the Jaccard similarity coefficient and the Dijkstra algorithm to reconstruct the trust matrix and explore the potential user trust relationship. On the other hand, the traditional matrix factorization algorithm is modeled by the user item potential factor dot product, however, it does not satisfy the triangle inequality property and affects the final recommender effect. The primary motivator behind our approach is to combine the best of both worlds, mitigate the inherent weaknesses of each paradigm. Combining the advantages of the two ideas, it has been demonstrated that our algorithm can enhance recommender performance and improve cold start in recommender systems.

中文翻译：

---

结合信任关系和距离度量分解的社交推荐

推荐系统通过挖掘用户历史行为数据来预测用户偏好。本文提出了一种结合信任关系和距离度量分解的社交推荐。一方面，推荐系统存在冷启动问题，可以通过添加社交关系来有效缓解。同时，针对稀疏信任矩阵问题，利用Jaccard相似系数和Dijkstra算法重构信任矩阵，挖掘潜在用户信任关系。另一方面，传统的矩阵分解算法是通过用户项潜在因子点积来建模的，但它不满足三角不等式性质，影响了最终的推荐效果。我们方法背后的主要动力是结合两全其美，减轻每个范式的固有弱点。结合两种思想的优点，已经证明我们的算法可以提高推荐系统的性能并改善推荐系统的冷启动。