Deep Learning for Recommender Systems: Literature Review and Perspectives

Benkessirat, Selma; Boustia, Narhimene; Nachida, Rezoug

doi:10.1109/icrami52622.2021.9585931

Cited by 3 publications

(1 citation statement)

References 31 publications

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“…The current mainstream technical solutions are based on collaborative filtering and deep learning algorithms [ [1]]. Collaborative filtering algorithms are currently mainly applied in the field of e-commerce recommendations [ [2]].…”

Section: Introductionmentioning

confidence: 99%

The News Delivery Channel Recommendation Based on Granular Neural Network

Liu

2023

Preprint

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With the continuous maturation and expansion of neural network technology, deep neural networks have been widely utilized as the fundamental building blocks of deep learning in a variety of applications, including speech recognition, machine translation, image processing, and the creation of recommendation systems. Therefore, many re-al-world complex problems can be solved by the deep learning techniques. As is known, traditional news recommendation systems mostly employ techniques based on collaborative filtering and deep learning, but the performance of these algorithms is constrained by the sparsity of the data and the scalability of the approaches. In this paper, we propose a recommendation model using granular neural network model to recommend news to appropriate channels by analyzing the properties of news. Specifically, a specified neural network serves as the foundation for the granular neural network that the model is considered to be build. Different information granularities are attributed to various types of news material, and different information granularities are released between networks in various ways. When processing data, granular output is created, which is compared to the interval values pre-set on various plat-forms and used to quantify the analysis's effectiveness. The analysis results could help the media to match the proper news in depth, maximize the public attention of the news and the utilization of media resources.

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Section: Introductionmentioning

confidence: 99%

The News Delivery Channel Recommendation Based on Granular Neural Network

Liu

2023

Preprint

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Not All Memories Created Equal: Dynamic User Representations for Collaborative Filtering

et al. 2023

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Collaborative filtering methods for recommender systems tend to represent users as a single static latent vector. However, user behavior and interests may dynamically change in the context of the recommended item being presented to the user. For example, in the case of movie recommendations, it is usually true that movies that the user watched more recently are more informative than movies that were watched a long time ago. However, it is possible that a particular movie from the past may become suddenly more relevant for prediction in the presence of a recommendation for its sequel movie. In response to this issue, we introduce the Attentive Item2Vec++ (AI2V++) model, a neural attentive collaborative filtering approach in which the user representation adapts dynamically in the presence of the recommended item. AI2V++ employs a novel context-target attention mechanism in order to learn and capture different characteristics of the user's historical behavior with respect to a potential recommended item. Furthermore, analysis of the neural-attentive scores allows for improved interpretability and explainability of the model. We evaluate our proposed approach on five publicly available datasets and demonstrate its superior performance in comparison to state-of-the-art baselines across multiple accuracy metrics.INDEX TERMS artificial neural networks, collaborative filtering, neural attention, recommender systems.

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GTFP: Network Fault Prediction Based on Graph and Time Series

Li,

Ding,

2023

Int. J. Soft. Eng. Knowl. Eng.

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With the explosion of 5G network scale, the network structure becomes increasingly complex. During the operation of the network devices, the probability of anomalies or faults increases accordingly. Network faults may lead to the disappearance of important information and cause unpredictable losses. The prediction of network faults can enhance the quality of network services and reduce economic loss. In this paper, we propose the concept of 4D features and use the BERT algorithm to extract semantic features, the graph neural network algorithm to extract network topology information, and the Temporal Convolutional Network (TCN) algorithm to extract time series. Based on this, we propose Fault Prediction based on GraphSage and TCN (GTFP), an end-to-end solution of network fault alarm prediction, which is based on GraphSage and TCN (GTCN), a hybrid algorithm of a graph neural network and the TCN model. Our solution takes the historical alarm data as input. First, we filter out the alarm noises irrelevant to the faults through data cleaning. Then, we employ feature engineering to extract the valid alarm features, including the statistical features of the network alarm information, the semantic features of the alarm texts, the sequential features of the alarms and the network topology features of the nodes where the alarms are located. Finally, we use GTCN to predict future fault alarms based on the extracted features. Experiments on the alarm data of a real service system show that GTFP performs better than the state-of-the-art algorithms of fault alarm prediction.

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Deep Learning for Recommender Systems: Literature Review and Perspectives

Cited by 3 publications

References 31 publications

The News Delivery Channel Recommendation Based on Granular Neural Network

The News Delivery Channel Recommendation Based on Granular Neural Network

Not All Memories Created Equal: Dynamic User Representations for Collaborative Filtering

GTFP: Network Fault Prediction Based on Graph and Time Series

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