深度学习驱动的推荐系统：挑战与解决方案综述

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本文档是一篇深入探讨深度学习在推荐系统中的应用的综述文章，标题为《深度学习在推荐系统中的挑战与解决方案：一个全面的审查》(A review on deep learning for recommender systems: challenges and remedies)。随着互联网的普及、个性化趋势的增强以及用户电脑使用习惯的变化，推荐系统已经成为信息过滤的重要工具，但它们仍面临准确性、可扩展性和冷启动问题等挑战。近年来，作为许多复杂任务中先进的机器学习技术，深度学习已被引入推荐系统，旨在提升推荐的质量。作者Zeynep Batmaz、Ali Yurekli、Alper Bilge和Cihan Kaleli在这份综述中，对基于深度学习的推荐方法进行了全面的分析。他们从四个方面梳理了相关研究，分别是： 1. **深度学习模型在推荐系统中的应用**：作者详细介绍了不同类型的深度学习模型，如神经网络、深度强化学习、深度信念网络和协同过滤等，这些模型如何通过学习用户行为模式和物品特征来生成个性化的推荐。 2. **解决推荐系统挑战的方法**：针对推荐系统面临的准确性问题，可能通过改进模型架构（如深度学习的层次结构）、引入更复杂的表示学习方法，或者结合其他数据源来提高预测精度。对于可扩展性，文章讨论了分布式计算、模型压缩和增量学习等策略。对于冷启动问题，即新用户或新物品如何获得推荐，文中可能会涉及利用社交网络信息、内容信息或基于迁移学习的解决方案。 3. **实证评估与比较**：综述中会包括现有的深度学习推荐系统在基准数据集上的性能评估，以及与其他传统方法（如矩阵分解、协同过滤）的比较，以展现深度学习的优势和局限。 4. **未来方向与建议**：最后，作者会展望深度学习在推荐系统领域的未来发展，提出可能的研究方向，如更高效的模型训练方法、更高级别的解释性推荐以及伦理和隐私问题的考虑。这篇综述为对推荐系统感兴趣的新人提供了宝贵的指导，帮助他们理解深度学习如何解决当前的问题，并为潜在的研究者指明了进一步探索的领域。阅读这份报告将有助于研究人员更好地设计和优化深度学习驱动的推荐系统。

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A review on deep learning for recommender systems: challenges…

CF systems rely on the rating history of the items given by the users of the system. Sparsity

appears as a major problem especially for CF since the users only rate a small fraction of the

available items, which makes it challenging to generate predictions (Su and Khoshgoftaar

2009; Bobadilla et al. 2013). When working on a sparse dataset, a CF algorithm may fail

to take advantage of beneﬁcial relationships among users and items. Data sparsity leads to

another severe challenge referred to as the cold-start problem. Producing predictions for

a new user having very few ratings is not possible due to insufﬁcient data to proﬁle them.

Likewise, presenting recently added items as recommendations to users is also not achievable

due to the lack of ratings for those items. However, unlike CF techniques, newly added users

and items can be managed in content-based recommender systems by utilizing their content

information.

Most of the recommender systems are deployed in a responsive environment. In a typical

recommendation scenario, a user is provided with a set of recommended items according

to her preferences while she is navigating through a web page. In order to carry out such

scenario efﬁciently, the recommendations should be provided in a reasonable amount of

time, which requires a highly-scalable system (Linden et al. 2003). With the growth of the

number of users and/or items in the system, many algorithms tend to slow down or require

more computational resources (Shani and Gunawardana 2011). Thus, scalability turns into a

signiﬁcant challenge that should be managed efﬁciently.

3.2 Deep learning

Deep learning is a ﬁeld of machine learning that is based on learning several layers of

representations, typically by using artiﬁcial neural networks. Through the layer hierarchy of

a deep learning model, the higher-level concepts are deﬁned from the lower-level concepts

(Deng and Yu 2014).

Since Hinton et al. (2006) introduced an efﬁcient way of training deep models and Bengio

(2009) showed the capabilities of deep architectures in complicated artiﬁcial intelligence

tasks, deep learning has become an emerging topic in computer science. Currently, deep

learning approaches produce the state-of-the-art solutions to many problems in computer

vision, natural language processing, and speech recognition (Deng and Yu 2014).

Although neural networks and the science behind deep models have been around for more

than 50 years, the power of deep learning techniques has started revealing during the last

decade. The main factors that promote deep learning as the state-of-the-art machine learning

technique can be listed as follows:

Big data: A deep learning model learns better representations as it is provided with more

amount of data.

Computational power: Graphical processing units (GPU) meet the processing power

required for complex computations in deep learning models.

Throughout this section, we brieﬂy introduce the deep learning models that have been

widely utilized in recommendation tasks.

3.2.1 Restricted Boltzmann machines

A restricted Boltzmann machine (RBM) is a particular type of a Boltzmann machine, which

has two layers of units. As illustrated in Fig. 2, the ﬁrst layer consists of visible units, and the

second layer includes hidden units. In this restricted architecture, there are no connections

between the units in a layer (Salakhutdinov and Hinton 2009).

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Z. Batmaz et al.

Fig. 2 A restricted Boltzmann

machine

The visible units in the model correspond to the components of observation, and the hidden

units represent the dependencies between the components of the observations. For instance,

in case of the famous handwritten digit recognition problem (Cire¸san et al. 2010), a visible

unit becomes a pixel of a digital image, and a hidden unit represents a dependency between

pixels in the image.

3.2.2 Deep belief networks

A deep belief network (DBN) is a multi-layer learning architecture that uses a stack of RBMs

to extract a deep hierarchical representation of the training data. In such design, the hidden

layer of each sub-network serves as the visible layer for the upcoming sub-network (Hinton

2009).

When learning through a DBN, ﬁrstly the RBM in the bottom layer is trained by inputting

the original data into the visible units. Then, the parameters are ﬁxed up, and the hidden units

of the RBM are used as the input into the RBM in the second layer. The learning process

continues until reaching the top of the stacked sub-networks, and ﬁnally, a suitable model is

obtained to extract features from the input. Since the learning process is unsupervised, it is

common to add a new network of supervised learning to the end of the DBN to use it in a

supervised learning task such as classiﬁcation or regression.

3.2.3 Autoencoders

An autoencoder is a type of feedforward neural network, which is trained to encode the input

into some representation, such that the input can be reconstructed from such representation

(Hinton and Salakhutdinov 2006). Typically, an autoencoder consists of three layers, namely,

the input layer, the hidden layer, and the output layer. The number of neurons in the input

layer is equal to the number of neurons in the output layer.

An autoencoder reconstructs the input layer at the output layer b y using the representation

obtained in the hidden layer. During the learning process, the network uses two mappings,

which are referred to as encoder and decoder. While the encoder maps the data from the input

layer to the hidden layer, the decoder maps the encoded data from the hidden layer to the

output layer. An illustration of an autoencoder is given in Fig. 3.

Reconstruction strategy in autoencoders may fail to extract useful features. The resulting

model may result in uninteresting solutions, or it may provide a direct copy of the original

input. In order to avoid such kind of problems, a denoising factor is used on the original data.

A denoising autoencoder (DAE) is a variant of an autoencoder that is trained to reconstruct

the original input from the corrupted form. Denoising factor makes autoencoders more stable

and robust since they can deal with data corruptions (Vincent et al. 2010).

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深度学习驱动的推荐系统：挑战与解决方案综述

Deep learning REVIEW。深度学习三大牛在《Nature》杂志首次合作的发表综述文章《Deep Learning》。

Wide Deep Learning for Recommender Systems.pdf

给我推荐20个比较流行的推荐算法模型

帮我讲解总结一下Self-Supervised Learning for Recommender Systems: A Survey这篇论文

fatal: unable to access 'https://github.com/Drew19980118/Recommender_System_FM_NFM_AFM.git/': HTTP/2 stream 1 was not closed cleanly before end of the underlying stream

给我推荐20个比较流行的用户画像算法模型源码地址

_COMMON_META = { "min_size": (1, 1), "categories": _IMAGENET_CATEGORIES, }

我想了解基于keras的推荐系统项目

tensorflow.python.framework.errors_impl.NotFoundError: ./Checkpoint/pre-recommender; No such file or directory [[{{node save/SaveV2}}]]

a survey on knowledge graph-based recommender systems

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python协同过滤推荐代码详细

metric learning

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