Deep Learning Recommendation Algorithm Based on Reviews and Item Descriptions
Reviews contain rich user and item information,which helps to alleviate the problem of data sparsity.However,the existing recommendation model based on reviews is not sufficient and effective enough to mine the review texts,and most of them ignore the migration of user interest over time and the ite...
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| Format: | Article |
| Language: | zho |
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Editorial office of Computer Science
2022-03-01
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| Series: | Jisuanji kexue |
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| Online Access: | https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2022-3-99.pdf |
| _version_ | 1818157936282697728 |
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| author | WANG Mei-ling, LIU Xiao-nan, YIN Mei-juan, QIAO Meng, JING Li-na |
| author_facet | WANG Mei-ling, LIU Xiao-nan, YIN Mei-juan, QIAO Meng, JING Li-na |
| author_sort | WANG Mei-ling, LIU Xiao-nan, YIN Mei-juan, QIAO Meng, JING Li-na |
| collection | DOAJ |
| description | Reviews contain rich user and item information,which helps to alleviate the problem of data sparsity.However,the existing recommendation model based on reviews is not sufficient and effective enough to mine the review texts,and most of them ignore the migration of user interest over time and the item description documents containing the item attribute,which makes the recommendation result not accurate enough.In this paper,a deep semantic mining based recommendation model (DSMR) is proposed.By mining the semantic information of review texts and item description documents in depth,user characteristics and item attributes can be extracted more accurately,so as to realize more accurate recommendation.Firstly,the BERT pre-training model is used to process the comment text and item description document,and the user characteristics and item attributes are excavated deeply,which effectively alleviated the problems of data sparse and item cold start.Then,the forward LSTM is used to pay attention to the change of user preferences over time,and more accurate recommendations are obtained.Finally,in the model training stage,the experimental data are randomly selected from 1 to 5 points at 1:1:1:1:1 to ensure the same amount of data for each score value,so as to make the results more accurate and the model more robust.Experiments on four commonly used Amazon open datasets show that the root mean square error (RMSE) of DSMR is at least 11.95% lower than the two classical recommendation models based only on rating data,and it is better than the three new recommendation models based only on review text,and 5.1% lower than the optimal model. |
| first_indexed | 2024-12-11T15:22:06Z |
| format | Article |
| id | doaj.art-08474a9acfed4a6a9419c238ee0d1412 |
| institution | Directory Open Access Journal |
| issn | 1002-137X |
| language | zho |
| last_indexed | 2024-12-11T15:22:06Z |
| publishDate | 2022-03-01 |
| publisher | Editorial office of Computer Science |
| record_format | Article |
| series | Jisuanji kexue |
| spelling | doaj.art-08474a9acfed4a6a9419c238ee0d14122022-12-22T01:00:20ZzhoEditorial office of Computer ScienceJisuanji kexue1002-137X2022-03-014939910410.11896/jsjkx.210200170Deep Learning Recommendation Algorithm Based on Reviews and Item DescriptionsWANG Mei-ling, LIU Xiao-nan, YIN Mei-juan, QIAO Meng, JING Li-na0State Key Laboratory of Mathematical Engineering and Advanced Computing (Information Engineering University),Zhengzhou 450002,ChinaReviews contain rich user and item information,which helps to alleviate the problem of data sparsity.However,the existing recommendation model based on reviews is not sufficient and effective enough to mine the review texts,and most of them ignore the migration of user interest over time and the item description documents containing the item attribute,which makes the recommendation result not accurate enough.In this paper,a deep semantic mining based recommendation model (DSMR) is proposed.By mining the semantic information of review texts and item description documents in depth,user characteristics and item attributes can be extracted more accurately,so as to realize more accurate recommendation.Firstly,the BERT pre-training model is used to process the comment text and item description document,and the user characteristics and item attributes are excavated deeply,which effectively alleviated the problems of data sparse and item cold start.Then,the forward LSTM is used to pay attention to the change of user preferences over time,and more accurate recommendations are obtained.Finally,in the model training stage,the experimental data are randomly selected from 1 to 5 points at 1:1:1:1:1 to ensure the same amount of data for each score value,so as to make the results more accurate and the model more robust.Experiments on four commonly used Amazon open datasets show that the root mean square error (RMSE) of DSMR is at least 11.95% lower than the two classical recommendation models based only on rating data,and it is better than the three new recommendation models based only on review text,and 5.1% lower than the optimal model.https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2022-3-99.pdfrecommendation algorithm|deep learning|review|item description|data sparsity|cold start |
| spellingShingle | WANG Mei-ling, LIU Xiao-nan, YIN Mei-juan, QIAO Meng, JING Li-na Deep Learning Recommendation Algorithm Based on Reviews and Item Descriptions Jisuanji kexue recommendation algorithm|deep learning|review|item description|data sparsity|cold start |
| title | Deep Learning Recommendation Algorithm Based on Reviews and Item Descriptions |
| title_full | Deep Learning Recommendation Algorithm Based on Reviews and Item Descriptions |
| title_fullStr | Deep Learning Recommendation Algorithm Based on Reviews and Item Descriptions |
| title_full_unstemmed | Deep Learning Recommendation Algorithm Based on Reviews and Item Descriptions |
| title_short | Deep Learning Recommendation Algorithm Based on Reviews and Item Descriptions |
| title_sort | deep learning recommendation algorithm based on reviews and item descriptions |
| topic | recommendation algorithm|deep learning|review|item description|data sparsity|cold start |
| url | https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2022-3-99.pdf |
| work_keys_str_mv | AT wangmeilingliuxiaonanyinmeijuanqiaomengjinglina deeplearningrecommendationalgorithmbasedonreviewsanditemdescriptions |