Deep Generative Recommendation Based on List-Wise Ranking
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摘要: 变分自编码器(variational autoencoder, VAE)近年来在推荐领域有着很成功的应用.这种非线性概率模型的优势在于它可以突破线性模型有限的建模能力，而线性模型目前仍然在协同过滤研究中占主导地位.尽管基于变分自编码器的推荐方法已经取得了优越的表现，但仍存在一些未解决的问题，例如无法针对隐式反馈的推荐数据为用户生成个性化的推荐排序列表.因此，通过借助多项式似然对变分自编码器实施基于列表的排序策略，提出了一种深度生成推荐模型.该模型具有同时生成点级隐式反馈数据并为每个用户创建列表式偏好排序的能力.为了将排序损失与变分自编码器损失结合起来，采取归一化累计损失增益(normalized cumulative loss gain, NDCG)作为排名损失，并通过平滑函数进行近似.在3个真实世界数据集上(MovieLens-100k,XuetangX和Jester)进行了实验.实验结果表明：结合了列表级排序的变分自编码器在推荐个性化列表所有评价指标上,相比于其他基线模型拥有更出色的表现.Abstract: Variational autoencoders have been successfully applied in recommendation field in recent years. The advantage of this kind of nonlinear probabilistic model is that it can break through the limited modeling ability of linear model, which is still dominant in collaborative filtering research. Although the recommendation method based on variational autoencoder has achieved excellent performance, there are still some unresolved problems, such as the inability to generate personalized recommendation ranking lists for users based on the recommendation data of implicit feedback. Therefore, in this paper, we propose a depth generation recommendation model for variational autoencoder by using polynomial likelihood to implement list-based ranking strategies. The model has the ability to simultaneously generate point-wise implicit feedback data and create a list-like ranking list for each user. To seamlessly combine ranking loss with variational autoencoder loss, the normalized cumulative loss gain (NDCG) is adopted here and approximated with a smoothed function. A series of experiments on three real-world datasets (MovieLens-100k, XuetangX and Jester) have been conducted. Experimental results show that the variational autoencoder combined with list-wise ranking method has better performance in generate a personalized recommendation list.
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