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Improving performance of tensor-based context-aware recommenders using Bias Tensor Factorization with context feature auto-encoding

机译：使用具有上下文特征自动编码的Bias Tensor Factorization改进基于张量的上下文感知推荐器的性能

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摘要

In this paper, we focus on the problem of context-aware recommendation using tensor factorization. Traditional tensor-based models in context-aware recommendation scenario only consider user-item-context interactions. In this paper, we argue that rating can't be totally explained by the interactions and the rating also influenced by the combined impact of overall mean, user bias, item bias and context bias. Based on this hypothesis, we propose a novel context-aware recommendation model named Bias Tensor Factorization, which take all this factors into account. Additionally, traditional context-aware recominenders with tensor factorization still have three main drawbacks: (1) the model complexity of those models increase exponentially with the number of context features, (2) those models can only handle context features with categorical values and (3) the models fail to select effective features from available context features. To address those problems, we propose a context features auto-encoding algorithm based on regression tree which can both handle numerical features and select effective features. Then we integrate this algorithm with Bias Tensor Factorization. Experiments on a real world contextual dataset and Movielens show that our proposed algorithms outperform the state-of-art context-aware recommendation algorithms, namely tensor factorization and factorization machine. (C) 2017 The Author(s). Published by Elsevier B.V.
机译：在本文中，我们集中在使用张量分解的上下文感知推荐问题。上下文感知推荐场景中的传统基于张量的模型仅考虑用户-项目-上下文的交互。在本文中，我们认为，评分不能完全由交互作用来解释，而评分还受总体均值，用户偏见，项目偏见和上下文偏见的综合影响。基于此假设，我们提出了一种新颖的上下文感知推荐模型，称为Bias Tensor Factorization，该模型考虑了所有这些因素。此外，具有张量分解的传统上下文感知重组器仍然存在三个主要缺点：（1）这些模型的模型复杂度随上下文特征的数量呈指数增长;（2）这些模型只能处理具有分类值的上下文特征;（3） ）模型无法从可用的上下文特征中选择有效的特征。为了解决这些问题，我们提出了一种基于回归树的上下文特征自动编码算法，该算法既可以处理数值特征，又可以选择有效特征。然后，我们将该算法与偏置张量分解相结合。在现实世界中的上下文数据集和Movielens上进行的实验表明，我们提出的算法优于最新的上下文感知推荐算法，即张量分解和分解机器。 （C）2017作者。由Elsevier B.V.发布

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