Unsupervised Learning of Paragraph Embeddings for Context-Aware Recommendation
The sparsity of data is one of the main reasons restricting the performance of recommender systems. In order to solve the sparsity problem, some recommender systems use auxiliary information, especially text information, as a supplement to increase the prediction accuracy of the ratings. However, th...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8676110/ |
| _version_ | 1818415937302298624 |
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| author | Jin Xie Fuxi Zhu Minxue Huang Naixue Xiong Sheng Huang Wei Xiong |
| author_facet | Jin Xie Fuxi Zhu Minxue Huang Naixue Xiong Sheng Huang Wei Xiong |
| author_sort | Jin Xie |
| collection | DOAJ |
| description | The sparsity of data is one of the main reasons restricting the performance of recommender systems. In order to solve the sparsity problem, some recommender systems use auxiliary information, especially text information, as a supplement to increase the prediction accuracy of the ratings. However, the two mainstream approaches based on text analysis have some limitations. The bag-of-words-based model is one of them, being difficult to use the contextual information of the paragraph effectively so that only the shallow understanding of paragraph can be parsed. Another model based on deep learning can extract the contextual information of the paragraph, but it also increases the complexity of the model. This paper proposes a novel context-aware recommendation model named paragraph vector matrix factorization (P2VMF) which integrates the unsupervised learning of paragraph embeddings into probabilistic matrix factorization (PMF). Therefore, P2VMF can capture the semantic information of the paragraph and can improve the prediction accuracy of the ratings. Our extensive experiments on real-world datasets show that the performance of the P2VMF model is preferable as compared with those multiple recommendation models in the situation, where the ratings are quite sparse. And we also verified that the P2V part of the model can well express the semantics in the form of vectors. |
| first_indexed | 2024-12-14T11:42:55Z |
| format | Article |
| id | doaj.art-8b27a7777f9a409e96a180cac525059c |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T11:42:55Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-8b27a7777f9a409e96a180cac525059c2022-12-21T23:02:45ZengIEEEIEEE Access2169-35362019-01-017431004310910.1109/ACCESS.2019.29066598676110Unsupervised Learning of Paragraph Embeddings for Context-Aware RecommendationJin Xie0https://orcid.org/0000-0001-5565-7673Fuxi Zhu1Minxue Huang2Naixue Xiong3https://orcid.org/0000-0002-0394-4635Sheng Huang4Wei Xiong5School of Computer Science, Wuhan University, Wuhan, ChinaSchool of Computer Science, Wuhan University, Wuhan, ChinaDepartment of Marketing and Tourism Management, Economics and Management School, Wuhan University, Wuhan, ChinaCollege of Intelligence and Computing, Tianjin University, Tianjin, ChinaSchool of Computer Science, Wuhan University, Wuhan, ChinaSchool of Computer Science, Wuhan University, Wuhan, ChinaThe sparsity of data is one of the main reasons restricting the performance of recommender systems. In order to solve the sparsity problem, some recommender systems use auxiliary information, especially text information, as a supplement to increase the prediction accuracy of the ratings. However, the two mainstream approaches based on text analysis have some limitations. The bag-of-words-based model is one of them, being difficult to use the contextual information of the paragraph effectively so that only the shallow understanding of paragraph can be parsed. Another model based on deep learning can extract the contextual information of the paragraph, but it also increases the complexity of the model. This paper proposes a novel context-aware recommendation model named paragraph vector matrix factorization (P2VMF) which integrates the unsupervised learning of paragraph embeddings into probabilistic matrix factorization (PMF). Therefore, P2VMF can capture the semantic information of the paragraph and can improve the prediction accuracy of the ratings. Our extensive experiments on real-world datasets show that the performance of the P2VMF model is preferable as compared with those multiple recommendation models in the situation, where the ratings are quite sparse. And we also verified that the P2V part of the model can well express the semantics in the form of vectors.https://ieeexplore.ieee.org/document/8676110/Context awarenessrecommender systemssemanticstext analysisunsupervised learning |
| spellingShingle | Jin Xie Fuxi Zhu Minxue Huang Naixue Xiong Sheng Huang Wei Xiong Unsupervised Learning of Paragraph Embeddings for Context-Aware Recommendation IEEE Access Context awareness recommender systems semantics text analysis unsupervised learning |
| title | Unsupervised Learning of Paragraph Embeddings for Context-Aware Recommendation |
| title_full | Unsupervised Learning of Paragraph Embeddings for Context-Aware Recommendation |
| title_fullStr | Unsupervised Learning of Paragraph Embeddings for Context-Aware Recommendation |
| title_full_unstemmed | Unsupervised Learning of Paragraph Embeddings for Context-Aware Recommendation |
| title_short | Unsupervised Learning of Paragraph Embeddings for Context-Aware Recommendation |
| title_sort | unsupervised learning of paragraph embeddings for context aware recommendation |
| topic | Context awareness recommender systems semantics text analysis unsupervised learning |
| url | https://ieeexplore.ieee.org/document/8676110/ |
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