A content-based recommender system using stacked LSTM and an attention-based autoencoder
Recommender systems (RS) are popular in many areas, such as movies, music, news, books, research articles, search queries, and social tagging. Proprietary recommender systems are used by e-commerce websites like eBay, Amazon, and Alibaba to better match customers with products they are likely to pur...
| Main Authors: | , |
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| Format: | Article |
| Jezik: | English |
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Elsevier
2024-02-01
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| Serija: | Measurement: Sensors |
| Teme: | |
| Online dostop: | http://www.sciencedirect.com/science/article/pii/S2665917423003112 |
| _version_ | 1827373951671599104 |
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| author | Kapil Saini Ajmer Singh |
| author_facet | Kapil Saini Ajmer Singh |
| author_sort | Kapil Saini |
| collection | DOAJ |
| description | Recommender systems (RS) are popular in many areas, such as movies, music, news, books, research articles, search queries, and social tagging. Proprietary recommender systems are used by e-commerce websites like eBay, Amazon, and Alibaba to better match customers with products they are likely to purchase. This study suggests a recommender system that combines stacked long short-term memory (LSTM) and an attention-based autoencoder. This system would be used in a self-supervised learning paradigm, and the Amazon product datasets were used to run simulations. The results showed that the proposed method is more accurate, uses less computing power, and can be used on a large scale. No matter how big the data is, it can capture low-dimensional fixed latent representations and use the bare minimum of information already in the items, whether they are new or old. Several evaluation metrics show that the method works and that the cold start problem has been solved. |
| first_indexed | 2024-03-08T11:25:16Z |
| format | Article |
| id | doaj.art-f86f95b1538446ecb84edc6066acf7df |
| institution | Directory Open Access Journal |
| issn | 2665-9174 |
| language | English |
| last_indexed | 2024-03-08T11:25:16Z |
| publishDate | 2024-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Measurement: Sensors |
| spelling | doaj.art-f86f95b1538446ecb84edc6066acf7df2024-01-26T05:34:53ZengElsevierMeasurement: Sensors2665-91742024-02-0131100975A content-based recommender system using stacked LSTM and an attention-based autoencoderKapil Saini0Ajmer Singh1Department of Computer Science & Engineering, Deenbandhu Chhotu Ram University of Science & Technology, Murthal, Haryana, India; Corresponding author.Department of Computer Science & Engineering, Deenbandhu Chhotu Ram University of Science & Technology, Murthal, Haryana, IndiaRecommender systems (RS) are popular in many areas, such as movies, music, news, books, research articles, search queries, and social tagging. Proprietary recommender systems are used by e-commerce websites like eBay, Amazon, and Alibaba to better match customers with products they are likely to purchase. This study suggests a recommender system that combines stacked long short-term memory (LSTM) and an attention-based autoencoder. This system would be used in a self-supervised learning paradigm, and the Amazon product datasets were used to run simulations. The results showed that the proposed method is more accurate, uses less computing power, and can be used on a large scale. No matter how big the data is, it can capture low-dimensional fixed latent representations and use the bare minimum of information already in the items, whether they are new or old. Several evaluation metrics show that the method works and that the cold start problem has been solved.http://www.sciencedirect.com/science/article/pii/S2665917423003112Recommender system (RS)e-commerceAutoencoderLSTMContent-based filtering |
| spellingShingle | Kapil Saini Ajmer Singh A content-based recommender system using stacked LSTM and an attention-based autoencoder Measurement: Sensors Recommender system (RS) e-commerce Autoencoder LSTM Content-based filtering |
| title | A content-based recommender system using stacked LSTM and an attention-based autoencoder |
| title_full | A content-based recommender system using stacked LSTM and an attention-based autoencoder |
| title_fullStr | A content-based recommender system using stacked LSTM and an attention-based autoencoder |
| title_full_unstemmed | A content-based recommender system using stacked LSTM and an attention-based autoencoder |
| title_short | A content-based recommender system using stacked LSTM and an attention-based autoencoder |
| title_sort | content based recommender system using stacked lstm and an attention based autoencoder |
| topic | Recommender system (RS) e-commerce Autoencoder LSTM Content-based filtering |
| url | http://www.sciencedirect.com/science/article/pii/S2665917423003112 |
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