计算机科学 ›› 2022, Vol. 49 ›› Issue (6A): 272-279.doi: 10.11896/jsjkx.210600159
何亦琛1, 毛宜军1, 谢贤芬2, 古万荣1
HE Yi-chen1, MAO Yi-jun1, XIE Xian-fen2, GU Wan-rong1
摘要： 基于模型的协同过滤算法通过矩阵分解来将用户偏好以及物品属性用隐变量来表示,但现有的矩阵分解算法很难应对个性化推荐系统中严重的数据稀疏性以及数据变化性所带来的问题。针对上述问题,提出了基于双边块对角矩阵的矩阵分解算法。首先通过基于社区发现的点割集图分割算法将原始的稀疏矩阵转换成双边块对角矩阵,将具有相同偏好的用户以及相似特征的物品归并到同一个对角块中,然后将结构中的对角块和双边拼接成数个密度较高的子矩阵。实验结果表明,对这几个密度有提高的子矩阵进行并行分解,基于其分解结果进行原始矩阵的预测,能够有效缓解矩阵分解中数据稀疏性所带来的问题,既能提升预测的精度,又能提高推荐结果的可解释性。同时,每个子对角块都能并行独立分解,能达到提高算法效率的目的。
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