图特征的属性感知非线性共嵌入

Ahmed Rashed, Josif Grabocka, L. Schmidt-Thieme
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引用次数: 27

摘要

在非常稀疏的推荐数据集中,用户的属性,如年龄、性别和家庭位置,以及项目的属性,如电影、类型、发行年份和导演,可以提高推荐的准确性,特别是对于评分很少的用户和项目。虽然大多数推荐模型可以扩展到考虑用户和项目的属性,但它们的体系结构通常会变得更加复杂。虽然项目的属性通常很容易提供,但出于隐私原因或仅仅因为它们与手头的操作流程无关,用户的属性通常很少。在本文中,我们通过提出一个简单的模型来解决属性感知推荐系统的这两个问题,该模型以类似于普通矩阵分解的方式将用户和项目共同嵌入到联合潜在空间中,但具有非线性潜在特征构建,可以无缝地摄取用户或项目属性或两者(GraphRec)。为了解决稀缺属性的问题,该模型将用户-物品关系视为二部图,并通过用户-物品共现图的拉普拉斯算子构建通用的用户和物品属性,该图不需要进一步的外部侧信息,仅需要评级矩阵。在三个推荐数据集的实验中,我们发现GraphRec在不使用任何辅助信息的情况下显著优于现有的最先进的属性感知和内容感知推荐系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Attribute-aware non-linear co-embeddings of graph features
In very sparse recommender data sets, attributes of users such as age, gender and home location and attributes of items such as, in the case of movies, genre, release year, and director can improve the recommendation accuracy, especially for users and items that have few ratings. While most recommendation models can be extended to take attributes of users and items into account, their architectures usually become more complicated. While attributes for items are often easy to be provided, attributes for users are often scarce for reasons of privacy or simply because they are not relevant to the operational process at hand. In this paper, we address these two problems for attribute-aware recommender systems by proposing a simple model that co-embeds users and items into a joint latent space in a similar way as a vanilla matrix factorization, but with non-linear latent features construction that seamlessly can ingest user or item attributes or both (GraphRec). To address the second problem, scarce attributes, the proposed model treats the user-item relation as a bipartite graph and constructs generic user and item attributes via the Laplacian of the user-item co-occurrence graph that requires no further external side information but the mere rating matrix. In experiments on three recommender datasets, we show that GraphRec significantly outperforms existing state-of-the-art attribute-aware and content-aware recommender systems even without using any side information.
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