一种基于旁信息增强的协同过滤自动编码器模型

针对用户评分预测不准确的问题，提出了一种基于旁信息(side-information)对用户兴趣进行预测的协同过滤自动编码器推荐模型，给出了模型的设计原理、损失函数以及具体结构.模型使用单隐藏层自动编码器实现，用户评分与旁信息同为模型的输入/输出数据，旁信息也直接参加模型的训练，这种设计不仅降低了模型的规模和复杂度，而且旁信息可以直接对用户兴趣进行修正.同时，通过对训练数据集合的合理划分与扩充，使得训练的网络模型增加了表达能力.在真实数据集上的对比实验表明，本文提出的方法提高了评分预测的准确度，具有一定的实用价值.

Auto-Encoder for Collaborative Filtering Based on Side-Information Enhancement

In view of inaccurate rating prediction of traditional recommendation model， a model based on side-information enhanced collaborative filtering auto-encoder was proposed， and the design principle， loss function and structure of the model were discussed in detail. The model was implemented by a single hidden layer auto-encoder network with ratings and side-information as input/output data. The model was directly trained by ratings and side-information. The proposed framework not only reduces the scale and complexity of the model， but also amends the user’s interest with side-information. At the same time， the representation ability of the trained model is increased by reasonable division and expansion of training datasets. The comparisons on real datasets show that the proposed model with side-information can effectively ameliorate the user’s interest predicted by the model without side-information， and improve the accuracy of rating prediction， which has a certain practicality.