个性化推荐的深度生成排序

Huafeng Liu, Jingxuan Wen, L. Jing, Jian Yu
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引用次数: 25

摘要

在海量信息时代,推荐系统提供了至关重要的服务。个性化排名对内容提供商和客户都很有吸引力,因为它能够在项目集上创建特定于用户的排名。虽然包括潜在因素模型和深度神经网络模型在内的强大的因素分析方法取得了可喜的成果,但它们仍然存在推荐数据稀疏性、优化不确定性等挑战性问题。为了提高推荐系统的准确率和泛化能力,本文提出了一种基于Wasserstein自编码器框架的深度生成排序(deep generative ranking, DGR)模型。具体来说,DGR同时生成逐点隐式反馈数据(通过β -伯努利分布),并通过充分利用每个用户的交互和非交互项来创建成对排名列表。通过最小化其惩罚证据下界,可以有效地推断出DGR。同时,从理论上分析了DGR模型的泛化误差范围,以保证其在极稀疏反馈数据中的性能。在电影、产品和商业领域的Movielens (20M)、Netflix、Epinions和Yelp四个大规模数据集上进行了一系列实验。通过与最先进的方法进行比较,实验结果表明,DGR在排序估计任务中始终有利于推荐系统,特别是对于近冷启动用户(交互项目少于5个)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep generative ranking for personalized recommendation
Recommender systems offer critical services in the age of mass information. Personalized ranking has been attractive both for content providers and customers due to its ability of creating a user-specific ranking on the item set. Although the powerful factor-analysis methods including latent factor models and deep neural network models have achieved promising results, they still suffer from the challenging issues, such as sparsity of recommendation data, uncertainty of optimization, and etc. To enhance the accuracy and generalization of recommender system, in this paper, we propose a deep generative ranking (DGR) model under the Wasserstein autoencoder framework. Specifically, DGR simultaneously generates the pointwise implicit feedback data (via a Beta-Bernoulli distribution) and creates the pairwise ranking list by sufficient exploiting both interacted and non-interacted items for each user. DGR can be efficiently inferred by minimizing its penalized evidence lower bound. Meanwhile, we theoretically analyze the generalization error bounds of DGR model to guarantee its performance in extremely sparse feedback data. A series of experiments on four large-scale datasets (Movielens (20M), Netflix, Epinions and Yelp in movie, product and business domains) have been conducted. By comparing with the state-of-the-art methods, the experimental results demonstrate that DGR consistently benefit the recommendation system in ranking estimation task, especially for the near-cold-start-users (with less than five interacted items).
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