A Survey on Modern Recommendation System based on Big Data

Peng, Yuanzhe

doi:10.48550/arxiv.2206.02631

Cited by 7 publications

(10 citation statements)

References 49 publications

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“…In recent times, recommendation systems have gained immense popularity and are extensively utilized across a range of fields, such as entertainment, e-commerce, social media etc. For example, popular platforms like YouTube and Netflix use recommendation systems to suggest relevant videos and platforms like Amazon use recommendation systems to suggest relevant products to the user [237]. The commonly used approaches for recommendation systems are based on collaborative filtering [238], content-based [239] and knowledge-based [240].…”

Section: Recommendation Systemsmentioning

confidence: 99%

A Survey of GPT-3 Family Large Language Models Including ChatGPT and GPT-4

Kalyan

2023

SSRN Journal

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Section: Recommendation Systemsmentioning

confidence: 99%

A Survey of GPT-3 Family Large Language Models Including ChatGPT and GPT-4

Kalyan

2023

SSRN Journal

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“…Content-based recommendation algorithms are proposed to alleviate the data sparsity and cold-start problems of collaborative filtering [11]. The core idea of a content-based recommendation algorithm is to obtain the items that users have interacted with through implicit feedback or explicit, and then use the content information of the interaction to capture the user's preference settings and make recommendations by the similarity between the preference settings and the items that have not been interacted with.…”

Section: A Traditional Recommendation Algorithmsmentioning

confidence: 99%

RTN-GNNR: Fusing Review Text Features and Node Features for Graph Neural Network Recommendation

et al. 2022

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Recent recommendation systems have achieved good results by applying Graph Neural Network (GNN) to user-item interaction graphs. However, these recommendation systems can only handle structured interaction data and cannot handle unstructured review text data well. Based on the user-item interaction graph, combining review text can effectively solve the problem of data sparsity and improve recommendation quality. Most of the current recommendation methods combining review texts stitch the data from different modalities, leading to insufficient interactions and degrading the recommendations' performance. A model called RTN-GNNR to fuse Review Text feature and Node feature for Graph Neural Network Recommendation is proposed to solve these problems and get better item recommendations. RTN-GNNR consists of four modules. The review text feature extraction module proposes a Bi-directional Gated Recurrent Unit (Bi-GRU) text analysis method that combines Bidirectional Encoder Representation from Transformers (BERT) and attention mechanism to enable the model to focus on more valuable reviews. The node feature extraction module proposes a GNN combined with the attention mechanism for the interactive node extraction method, which enables the model to have better higher-order feature extraction capability. The feature fusion module proposes the method of tandem Factorization Machine (FM) and Multilayer Perceptron (MLP) to realize interactive learning among multi-source features. The prediction module innerproducts the fused higher-order features to achieve recommendation effect. We conducted experiments on five publicly available datasets from Amazon, showing that RTN-GNNR outperforms state-of-the-art personalized recommendation methods in both RMSE and MSE, especially in the sparser two datasets. The effectiveness of each module of the model is also demonstrated by a comparison of the ablation experiments.

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“…Deep learning-based recommendation systems usually take data related to various kinds of users or items as model input, represent the hidden mapping relationship between the users and items, and generate the recommendation results. Three models named Generalized Matrix Factorization (GMF), Multilayer Perceptron (MLP), and Neural Matrix Factorization (NeuMF) are proposed [1]. One of the most common forms for constructing deep learning recommendation is MLP [2].…”

Section: Introductionmentioning

confidence: 99%

“…Horizontal federated learning is used in NCF to aggregate user privacy data on distributed sides and integrate the model on the server side. A round of federated learning training includes four steps: (1)…”

Section: Introduction Of Locality Sensitive Hashing (Lsh)mentioning

confidence: 99%

LSH Models in Federated Recommendation

Dai,

Zhu,

Gui

2024

Applied Sciences

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Given the challenges in recommendation effectiveness, communication costs, and privacy issues associated with federated learning, the current algorithm amalgamates locality sensitive hash (LSH) with three federated recommendation models: Generalized Matrix Factorization, Multilayer Perceptions, and Neural Matrix Factorization. First, the participation weights of the model are determined based on the participation degree of the federated learning clients to improve the efficiency of joint learning. Second, the local parameters of the federated aggregation model are divided into two groups to protect user embedding. Finally, rapid mapping and similarity retrieval of the upload parameters are performed using LSH to protect user privacy and shorten training time. We conducted experiments to compare the performance differences between LSH-based and Laplace noise-based differential privacy methods in terms of recommendation effectiveness, communication costs, and privacy preservation. Experimental results demonstrate that LSH models achieved a favorable balance between recommendation effectiveness and privacy protection, with improved time performance.

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A Survey on Modern Recommendation System based on Big Data

Cited by 7 publications

References 49 publications

A Survey of GPT-3 Family Large Language Models Including ChatGPT and GPT-4

A Survey of GPT-3 Family Large Language Models Including ChatGPT and GPT-4

RTN-GNNR: Fusing Review Text Features and Node Features for Graph Neural Network Recommendation

LSH Models in Federated Recommendation

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