Handling data sparsity via item metadata embedding into deep collaborative recommender system
The tremendous growth in information over the last decade leads to information overwhelming problems for accessing personalized products. The recommender framework that retrieves user preferences on past interactions is known as collaborative filtering (CF). Although, CF is a prevalent technique amo...
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| Format: | Article |
| Language: | English |
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Springer
2022-11-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157821003670 |
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| author | Gopal Behera Neeta Nain |
| author_facet | Gopal Behera Neeta Nain |
| author_sort | Gopal Behera |
| collection | DOAJ |
| description | The tremendous growth in information over the last decade leads to information overwhelming problems for accessing personalized products. The recommender framework that retrieves user preferences on past interactions is known as collaborative filtering (CF). Although, CF is a prevalent technique amongst the techniques applied in the recommender environment. However, it suffers from many problems like information sparsity, scalability, cold-start, etc. Many investigations have been made to tackle some of these issues with the help of matrix factorization (MF) approaches. However, MF cannot handle the nonlinearity among the data. Deep learning is an advanced learning technique that has shown success in many applications such as image classification, computer vision, natural language processing, etc. Little work has been reported on deep learning techniques in the recommender domain. We propose an efficient deep collaborative recommender system that embeds item metadata to handle the nonlinearity in data and sparsity. The model consists of two stages, wherein the first stage, a neural network, is used to retrieve the data’s nonlinear features through embedding vectors. These vectors are concatenated together and fed as input to the second stage of the model. The output of the model yields a partially observed rating. The input and the parameters are simultaneously optimized and updated to minimize errors. The proposed strategy is evaluated against the benchmark techniques on two well-known datasets. The exploratory outcomes signify our approach’s exactitude and efficiency. Moreover, the missing values can also be recovered by propagating the embedding vectors from the input to the output layers. |
| format | Article |
| id | doaj-art-8976196a5efa469288ff2a4bd9f45be5 |
| institution | Kabale University |
| issn | 1319-1578 |
| language | English |
| publishDate | 2022-11-01 |
| publisher | Springer |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj-art-8976196a5efa469288ff2a4bd9f45be52025-08-20T03:48:31ZengSpringerJournal of King Saud University: Computer and Information Sciences1319-15782022-11-0134109953996310.1016/j.jksuci.2021.12.021Handling data sparsity via item metadata embedding into deep collaborative recommender systemGopal Behera0Neeta Nain1Corresponding author.; Department of Computer Science and Engineering, Malaviya National Institute of Technology Jaipur, IndiaDepartment of Computer Science and Engineering, Malaviya National Institute of Technology Jaipur, IndiaThe tremendous growth in information over the last decade leads to information overwhelming problems for accessing personalized products. The recommender framework that retrieves user preferences on past interactions is known as collaborative filtering (CF). Although, CF is a prevalent technique amongst the techniques applied in the recommender environment. However, it suffers from many problems like information sparsity, scalability, cold-start, etc. Many investigations have been made to tackle some of these issues with the help of matrix factorization (MF) approaches. However, MF cannot handle the nonlinearity among the data. Deep learning is an advanced learning technique that has shown success in many applications such as image classification, computer vision, natural language processing, etc. Little work has been reported on deep learning techniques in the recommender domain. We propose an efficient deep collaborative recommender system that embeds item metadata to handle the nonlinearity in data and sparsity. The model consists of two stages, wherein the first stage, a neural network, is used to retrieve the data’s nonlinear features through embedding vectors. These vectors are concatenated together and fed as input to the second stage of the model. The output of the model yields a partially observed rating. The input and the parameters are simultaneously optimized and updated to minimize errors. The proposed strategy is evaluated against the benchmark techniques on two well-known datasets. The exploratory outcomes signify our approach’s exactitude and efficiency. Moreover, the missing values can also be recovered by propagating the embedding vectors from the input to the output layers.http://www.sciencedirect.com/science/article/pii/S1319157821003670Recommendation systemCollaborative filteringMatrix factorizationDeep learningDeep neural networkEmbedding |
| spellingShingle | Gopal Behera Neeta Nain Handling data sparsity via item metadata embedding into deep collaborative recommender system Journal of King Saud University: Computer and Information Sciences Recommendation system Collaborative filtering Matrix factorization Deep learning Deep neural network Embedding |
| title | Handling data sparsity via item metadata embedding into deep collaborative recommender system |
| title_full | Handling data sparsity via item metadata embedding into deep collaborative recommender system |
| title_fullStr | Handling data sparsity via item metadata embedding into deep collaborative recommender system |
| title_full_unstemmed | Handling data sparsity via item metadata embedding into deep collaborative recommender system |
| title_short | Handling data sparsity via item metadata embedding into deep collaborative recommender system |
| title_sort | handling data sparsity via item metadata embedding into deep collaborative recommender system |
| topic | Recommendation system Collaborative filtering Matrix factorization Deep learning Deep neural network Embedding |
| url | http://www.sciencedirect.com/science/article/pii/S1319157821003670 |
| work_keys_str_mv | AT gopalbehera handlingdatasparsityviaitemmetadataembeddingintodeepcollaborativerecommendersystem AT neetanain handlingdatasparsityviaitemmetadataembeddingintodeepcollaborativerecommendersystem |