计算机科学 ›› 2016, Vol. 43 ›› Issue (9): 71-76.doi: 10.11896/j.issn.1002-137X.2016.09.013
• 2015 年第三届CCF 大数据学术会议 • 上一篇 下一篇
姚杏,朱福喜,阳小兰,郑麟,刘世超
YAO Xing, ZHU Fu-xi, YANG Xiao-lan, ZHENG Lin and LIU Shi-chao
摘要： 分解机模型已经被成功应用于上下文推荐系统。在分解机模型的学习算法中,交替最小二乘法是一种固定其他参数只求单一参数最优值的学习算法,其参数数目影响计算复杂度。然而当特征数目很大时,参数数目随着特征数目急剧增加,导致计算复杂度很高；即使有些参数已经达到了最优值,每次迭代仍更新所有的参数。因此,主要改进了交替最小二乘法的参数更新策略,为参数引入自适应误差指标,通过权重和参数绝对误差共同决定该参数更新与否,使得每次迭代时重点更新最近两次迭代取值变化较大的参数。这种仅更新自适应误差大于阈值的参数的策略不但减少了需要更新的参数数目,进而加快了算法收敛的速度和缩短了运行时间,而且参数权重由误差决定,又修正了误差。在Yahoo和Movielens数据集上的实验结果证明:改进的参数更新策略运行效率有明显提高。
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