A Hybrid Recommender System Based on Autoencoder and Latent Feature Analysis
A recommender system (RS) is highly efficient in extracting valuable information from a deluge of big data. The key issue of implementing an RS lies in uncovering users’ latent preferences on different items. Latent Feature Analysis (LFA) and deep neural networks (DNNs) are two of the most popular a...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/25/7/1062 |
| _version_ | 1797589349637816320 |
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| author | Shangzhi Guo Xiaofeng Liao Gang Li Kaiyi Xian Yuhang Li Cheng Liang |
| author_facet | Shangzhi Guo Xiaofeng Liao Gang Li Kaiyi Xian Yuhang Li Cheng Liang |
| author_sort | Shangzhi Guo |
| collection | DOAJ |
| description | A recommender system (RS) is highly efficient in extracting valuable information from a deluge of big data. The key issue of implementing an RS lies in uncovering users’ latent preferences on different items. Latent Feature Analysis (LFA) and deep neural networks (DNNs) are two of the most popular and successful approaches to addressing this issue. However, both the LFA-based and the DNNs-based models have their own distinct advantages and disadvantages. Consequently, relying solely on either the LFA or DNN-based models cannot ensure optimal recommendation performance across diverse real-world application scenarios. To address this issue, this paper proposes a novel hybrid recommendation model that combines Autoencoder and LFA techniques, termed AutoLFA. The main idea of AutoLFA is two-fold: (1) It leverages an Autoencoder and an LFA model separately to construct two distinct recommendation models, each residing in a unique metric representation space with its own set of strengths; and (2) it integrates the Autoencoder and LFA model using a customized self-adaptive weighting strategy, thereby capitalizing on the merits of both approaches. To evaluate the proposed AutoLFA model, extensive experiments on five real recommendation datasets are conducted. The results demonstrate that AutoLFA achieves significantly better recommendation performance than the seven related state-of-the-art models. |
| first_indexed | 2024-03-11T01:05:22Z |
| format | Article |
| id | doaj.art-11163d0abdf64ed5a155206895700943 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-11T01:05:22Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-11163d0abdf64ed5a1552068957009432023-11-18T19:14:12ZengMDPI AGEntropy1099-43002023-07-01257106210.3390/e25071062A Hybrid Recommender System Based on Autoencoder and Latent Feature AnalysisShangzhi Guo0Xiaofeng Liao1Gang Li2Kaiyi Xian3Yuhang Li4Cheng Liang5College of Computer Science, Chongqing University, Chongqing 400044, ChinaCollege of Computer Science, Chongqing University, Chongqing 400044, ChinaCollege of Computer Science, Chongqing University, Chongqing 400044, ChinaCollege of Computer Science, Chongqing University, Chongqing 400044, ChinaCollege of Computer and Information Science, Southwest University, Chongqing 400715, ChinaInstitute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou 510006, ChinaA recommender system (RS) is highly efficient in extracting valuable information from a deluge of big data. The key issue of implementing an RS lies in uncovering users’ latent preferences on different items. Latent Feature Analysis (LFA) and deep neural networks (DNNs) are two of the most popular and successful approaches to addressing this issue. However, both the LFA-based and the DNNs-based models have their own distinct advantages and disadvantages. Consequently, relying solely on either the LFA or DNN-based models cannot ensure optimal recommendation performance across diverse real-world application scenarios. To address this issue, this paper proposes a novel hybrid recommendation model that combines Autoencoder and LFA techniques, termed AutoLFA. The main idea of AutoLFA is two-fold: (1) It leverages an Autoencoder and an LFA model separately to construct two distinct recommendation models, each residing in a unique metric representation space with its own set of strengths; and (2) it integrates the Autoencoder and LFA model using a customized self-adaptive weighting strategy, thereby capitalizing on the merits of both approaches. To evaluate the proposed AutoLFA model, extensive experiments on five real recommendation datasets are conducted. The results demonstrate that AutoLFA achieves significantly better recommendation performance than the seven related state-of-the-art models.https://www.mdpi.com/1099-4300/25/7/1062data sciencedeep neural networkLatent Feature Analysismulti-metric recommender systemmatrix representation |
| spellingShingle | Shangzhi Guo Xiaofeng Liao Gang Li Kaiyi Xian Yuhang Li Cheng Liang A Hybrid Recommender System Based on Autoencoder and Latent Feature Analysis Entropy data science deep neural network Latent Feature Analysis multi-metric recommender system matrix representation |
| title | A Hybrid Recommender System Based on Autoencoder and Latent Feature Analysis |
| title_full | A Hybrid Recommender System Based on Autoencoder and Latent Feature Analysis |
| title_fullStr | A Hybrid Recommender System Based on Autoencoder and Latent Feature Analysis |
| title_full_unstemmed | A Hybrid Recommender System Based on Autoencoder and Latent Feature Analysis |
| title_short | A Hybrid Recommender System Based on Autoencoder and Latent Feature Analysis |
| title_sort | hybrid recommender system based on autoencoder and latent feature analysis |
| topic | data science deep neural network Latent Feature Analysis multi-metric recommender system matrix representation |
| url | https://www.mdpi.com/1099-4300/25/7/1062 |
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