嵌入隐式相似群的深度协同过滤算法

Deep Collaborative Filtering Algorithm with Embedding Implicit Similarity Groups

【目的】构建能捕获局部关联和表达用户显隐式偏好的深度协同过滤模型。【方法】在显式推荐任务中嵌入利用隐式反馈查找的相似群,通过多层感知机分别同时对用户-项目、用户-相似项目群、项目-相似用户群进行建模。【结果】在MovieLens两个数据集上的实验表明,该模型较各类协同过滤推荐算法的MAE和RMSE降低幅度分别最高达10.94%和11.79%。【局限】使模型达到最佳性能的近邻数在不同数据集存在差异,最佳近邻数的确认问题有待探索。【结论】该模型通过嵌入隐式相似群能有效弥补局限,使推荐结果更准确。

[Objective] This paper tries to construct a deep collaborative filtering model that can capture local relevance as well as explicit/implicit feedbacks. [Methods] In the explicit recommendation tasks, we embedded similar groups found by implicit feedback search. Then, we create models for user-item group, user-similar-item group, and item-similar-user group with Multi-Layer Perceptron. [Results] We examined the new algorithm with MovieLens datasets. Compared with existing methods, the MAE and RMSE of our model were reduced by10.94% and 11.79% respectively. [Limitations] More research is needed to identify the optimal number of the nearest neighbors for different datasets. [Conclusions] The new model could more effectively generate the recommendation results.