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A Generalized Locally Linear Factorization Machine with Supervised Variational Encoding

机译:具有监督变分编码的通用局部线性分解机

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Factorization Machines (FMs) learn weights for feature interactions, and achieve great success in many data mining tasks. Recently, Locally Linear Factorization Machines (LLFMs) have been proposed to capture the underlying structures of data for better performance. However, one obvious drawback of LLFM is that the local coding is only operated in the original feature space, which limits the model to be applied to high-dimensional and sparse data. In this work, we present a generalized LLFM (GLLFM) which overcomes this limitation by modeling the local coding procedure in a latent space. Moreover, a novel Supervised Variational Encoding (SVE) technique is proposed such that the distance can effectively describe the similarity between data points. Specifically, the proposed GLLFM-SVE trains several local FMs in the original space to model the higher order feature interactions effectively, where each FM associates to an anchor point in the latent space induced by SVE. The prediction for a data point is computed by a weighted sum of several local FMs, where the weights are determined by local coding coordinates with anchor points. Actually, GLLFM-SVE is quite flexible and other Neural Network (NN) based FMs can be easily embedded into this framework. Experimental results show that GLLFM-SVE significantly improves the performance of LLFM. By using NN-based FMs as local predictors, our model outperforms all the state-of-the-art methods on large-scale real-world benchmarks with similar number of parameters and comparable training time.
机译:分解机(FMS)学习功能交互的重量,并在许多数据挖掘任务中取得巨大成功。最近,已经提出了局部线性分解机(LLFMS)以捕获数据的基础结构以获得更好的性能。然而,LLFM的一个明显缺点是本地编码仅在原始特征空间中运行,这将模型限制为高维和稀疏数据。在这项工作中,我们介绍了一种广泛的LLFM(GLLFM),通过在潜伏空间中建模本地编码过程来克服这种限制。此外,提出了一种新颖的监督变分编码(SVE)技术,使得距离可以有效地描述数据点之间的相似性。具体地,所提出的GLLFM-SVE在原始空间中列出了几个本地FMS以有效地模拟更高阶特征交互,其中每个FM将由SVE引起的潜空间中的锚点涉及到锚点。数据点的预测由若干本地FMS的加权和计算,其中权重由具有锚点的本地编码坐标确定。实际上,GLLFM-SVE是相当灵活的,并且可以轻松地将其他神经网络(NN)的FMS嵌入到该框架中。实验结果表明,GLLFM-SVE显着提高了LLF​​M的性能。通过使用基于NN的FMS作为本地预测因素,我们的模型在具有类似数量的参数和可比培训时间的大规模现实基准上表现出所有最先进的方法。

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