Attention distribution guided information transfer networks for recommendation in practice
Recently, an increasing number of deep learning-based methods have been applied in recommendation. Most such methods outperform traditional methods, especially when using the natural language processing (NLP) technique with review texts. Many deep learning-based recommender systems are used to learn...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Sun, Gang [verfasserIn] Li, Yu [verfasserIn] Yu, Hongfang [verfasserIn] Chang, Victor [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Applied soft computing - Amsterdam [u.a.] : Elsevier Science, 2001, 97 |
|---|---|
| Übergeordnetes Werk: |
volume:97 |
| DOI / URN: |
10.1016/j.asoc.2020.106772 |
|---|
| Katalog-ID: |
ELV005148650 |
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| 520 | |a Recently, an increasing number of deep learning-based methods have been applied in recommendation. Most such methods outperform traditional methods, especially when using the natural language processing (NLP) technique with review texts. Many deep learning-based recommender systems are used to learn latent representations of reviews written by target users and reviews written for target items. They are then combined to predict the rating of the target user for the target item. However, most previously proposed review-based deep learning methods do not conform to real-world application scenarios, in which we cannot obtain the reviews of the target user for the target item (called U2I review). In real-world recommendation settings, items are always recommended to users before they have experienced them. Therefore, the review of a target user for a target item would not be available during the testing and validation process. Many methods, such as DeepCoNN and D-ATT, do not exclude the U2I review in the process of validation and testing. Therefore, the process of testing is different from real-world application scenarios, and these methods obtain substantial valuable information from the U2I review that target users write for target items. We propose a model called ADGITN and a training strategy to solve this problem. When training, the auxiliary model learns two attention distributions that the U2I reviews over user reviews and item reviews by auxiliary tasks. These two distributions are used to guide the learning of attention distributions between user reviews and item reviews of the main model. Thus, the main model could learn how to extract attention distributions between user reviews and item reviews according to the valuable information extracted from U2I reviews. During validation, only the main model works, and it could extract better attention distributions even without the help of a U2I review. Extensive experiments show the effectiveness of our model. We validate our model on the Amazon and Yelp19 datasets, and the results show that our model outperforms existing excellent models, with up to 13.8% relative improvement compared to the performance of MPCN, which is one of the best review-based deep learning models for recommendation. | ||
| 650 | 4 | |a Review-based recommender system | |
| 650 | 4 | |a Natural language processing | |
| 650 | 4 | |a Auxiliary tasks | |
| 650 | 4 | |a Teacher–student architecture | |
| 700 | 1 | |a Li, Yu |e verfasserin |4 aut | |
| 700 | 1 | |a Yu, Hongfang |e verfasserin |4 aut | |
| 700 | 1 | |a Chang, Victor |e verfasserin |4 aut | |
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10.1016/j.asoc.2020.106772 doi (DE-627)ELV005148650 (ELSEVIER)S1568-4946(20)30710-9 DE-627 ger DE-627 rda eng 004 DE-600 54.00 bkl Sun, Gang verfasserin aut Attention distribution guided information transfer networks for recommendation in practice 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Recently, an increasing number of deep learning-based methods have been applied in recommendation. Most such methods outperform traditional methods, especially when using the natural language processing (NLP) technique with review texts. Many deep learning-based recommender systems are used to learn latent representations of reviews written by target users and reviews written for target items. They are then combined to predict the rating of the target user for the target item. However, most previously proposed review-based deep learning methods do not conform to real-world application scenarios, in which we cannot obtain the reviews of the target user for the target item (called U2I review). In real-world recommendation settings, items are always recommended to users before they have experienced them. Therefore, the review of a target user for a target item would not be available during the testing and validation process. Many methods, such as DeepCoNN and D-ATT, do not exclude the U2I review in the process of validation and testing. Therefore, the process of testing is different from real-world application scenarios, and these methods obtain substantial valuable information from the U2I review that target users write for target items. We propose a model called ADGITN and a training strategy to solve this problem. When training, the auxiliary model learns two attention distributions that the U2I reviews over user reviews and item reviews by auxiliary tasks. These two distributions are used to guide the learning of attention distributions between user reviews and item reviews of the main model. Thus, the main model could learn how to extract attention distributions between user reviews and item reviews according to the valuable information extracted from U2I reviews. During validation, only the main model works, and it could extract better attention distributions even without the help of a U2I review. Extensive experiments show the effectiveness of our model. We validate our model on the Amazon and Yelp19 datasets, and the results show that our model outperforms existing excellent models, with up to 13.8% relative improvement compared to the performance of MPCN, which is one of the best review-based deep learning models for recommendation. Review-based recommender system Natural language processing Auxiliary tasks Teacher–student architecture Li, Yu verfasserin aut Yu, Hongfang verfasserin aut Chang, Victor verfasserin aut Enthalten in Applied soft computing Amsterdam [u.a.] : Elsevier Science, 2001 97 Online-Ressource (DE-627)334375754 (DE-600)2057709-6 (DE-576)256145733 1568-4946 nnns volume:97 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines AR 97 |
| spelling |
10.1016/j.asoc.2020.106772 doi (DE-627)ELV005148650 (ELSEVIER)S1568-4946(20)30710-9 DE-627 ger DE-627 rda eng 004 DE-600 54.00 bkl Sun, Gang verfasserin aut Attention distribution guided information transfer networks for recommendation in practice 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Recently, an increasing number of deep learning-based methods have been applied in recommendation. Most such methods outperform traditional methods, especially when using the natural language processing (NLP) technique with review texts. Many deep learning-based recommender systems are used to learn latent representations of reviews written by target users and reviews written for target items. They are then combined to predict the rating of the target user for the target item. However, most previously proposed review-based deep learning methods do not conform to real-world application scenarios, in which we cannot obtain the reviews of the target user for the target item (called U2I review). In real-world recommendation settings, items are always recommended to users before they have experienced them. Therefore, the review of a target user for a target item would not be available during the testing and validation process. Many methods, such as DeepCoNN and D-ATT, do not exclude the U2I review in the process of validation and testing. Therefore, the process of testing is different from real-world application scenarios, and these methods obtain substantial valuable information from the U2I review that target users write for target items. We propose a model called ADGITN and a training strategy to solve this problem. When training, the auxiliary model learns two attention distributions that the U2I reviews over user reviews and item reviews by auxiliary tasks. These two distributions are used to guide the learning of attention distributions between user reviews and item reviews of the main model. Thus, the main model could learn how to extract attention distributions between user reviews and item reviews according to the valuable information extracted from U2I reviews. During validation, only the main model works, and it could extract better attention distributions even without the help of a U2I review. Extensive experiments show the effectiveness of our model. We validate our model on the Amazon and Yelp19 datasets, and the results show that our model outperforms existing excellent models, with up to 13.8% relative improvement compared to the performance of MPCN, which is one of the best review-based deep learning models for recommendation. Review-based recommender system Natural language processing Auxiliary tasks Teacher–student architecture Li, Yu verfasserin aut Yu, Hongfang verfasserin aut Chang, Victor verfasserin aut Enthalten in Applied soft computing Amsterdam [u.a.] : Elsevier Science, 2001 97 Online-Ressource (DE-627)334375754 (DE-600)2057709-6 (DE-576)256145733 1568-4946 nnns volume:97 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines AR 97 |
| allfields_unstemmed |
10.1016/j.asoc.2020.106772 doi (DE-627)ELV005148650 (ELSEVIER)S1568-4946(20)30710-9 DE-627 ger DE-627 rda eng 004 DE-600 54.00 bkl Sun, Gang verfasserin aut Attention distribution guided information transfer networks for recommendation in practice 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Recently, an increasing number of deep learning-based methods have been applied in recommendation. Most such methods outperform traditional methods, especially when using the natural language processing (NLP) technique with review texts. Many deep learning-based recommender systems are used to learn latent representations of reviews written by target users and reviews written for target items. They are then combined to predict the rating of the target user for the target item. However, most previously proposed review-based deep learning methods do not conform to real-world application scenarios, in which we cannot obtain the reviews of the target user for the target item (called U2I review). In real-world recommendation settings, items are always recommended to users before they have experienced them. Therefore, the review of a target user for a target item would not be available during the testing and validation process. Many methods, such as DeepCoNN and D-ATT, do not exclude the U2I review in the process of validation and testing. Therefore, the process of testing is different from real-world application scenarios, and these methods obtain substantial valuable information from the U2I review that target users write for target items. We propose a model called ADGITN and a training strategy to solve this problem. When training, the auxiliary model learns two attention distributions that the U2I reviews over user reviews and item reviews by auxiliary tasks. These two distributions are used to guide the learning of attention distributions between user reviews and item reviews of the main model. Thus, the main model could learn how to extract attention distributions between user reviews and item reviews according to the valuable information extracted from U2I reviews. During validation, only the main model works, and it could extract better attention distributions even without the help of a U2I review. Extensive experiments show the effectiveness of our model. We validate our model on the Amazon and Yelp19 datasets, and the results show that our model outperforms existing excellent models, with up to 13.8% relative improvement compared to the performance of MPCN, which is one of the best review-based deep learning models for recommendation. Review-based recommender system Natural language processing Auxiliary tasks Teacher–student architecture Li, Yu verfasserin aut Yu, Hongfang verfasserin aut Chang, Victor verfasserin aut Enthalten in Applied soft computing Amsterdam [u.a.] : Elsevier Science, 2001 97 Online-Ressource (DE-627)334375754 (DE-600)2057709-6 (DE-576)256145733 1568-4946 nnns volume:97 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines AR 97 |
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10.1016/j.asoc.2020.106772 doi (DE-627)ELV005148650 (ELSEVIER)S1568-4946(20)30710-9 DE-627 ger DE-627 rda eng 004 DE-600 54.00 bkl Sun, Gang verfasserin aut Attention distribution guided information transfer networks for recommendation in practice 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Recently, an increasing number of deep learning-based methods have been applied in recommendation. Most such methods outperform traditional methods, especially when using the natural language processing (NLP) technique with review texts. Many deep learning-based recommender systems are used to learn latent representations of reviews written by target users and reviews written for target items. They are then combined to predict the rating of the target user for the target item. However, most previously proposed review-based deep learning methods do not conform to real-world application scenarios, in which we cannot obtain the reviews of the target user for the target item (called U2I review). In real-world recommendation settings, items are always recommended to users before they have experienced them. Therefore, the review of a target user for a target item would not be available during the testing and validation process. Many methods, such as DeepCoNN and D-ATT, do not exclude the U2I review in the process of validation and testing. Therefore, the process of testing is different from real-world application scenarios, and these methods obtain substantial valuable information from the U2I review that target users write for target items. We propose a model called ADGITN and a training strategy to solve this problem. When training, the auxiliary model learns two attention distributions that the U2I reviews over user reviews and item reviews by auxiliary tasks. These two distributions are used to guide the learning of attention distributions between user reviews and item reviews of the main model. Thus, the main model could learn how to extract attention distributions between user reviews and item reviews according to the valuable information extracted from U2I reviews. During validation, only the main model works, and it could extract better attention distributions even without the help of a U2I review. Extensive experiments show the effectiveness of our model. We validate our model on the Amazon and Yelp19 datasets, and the results show that our model outperforms existing excellent models, with up to 13.8% relative improvement compared to the performance of MPCN, which is one of the best review-based deep learning models for recommendation. Review-based recommender system Natural language processing Auxiliary tasks Teacher–student architecture Li, Yu verfasserin aut Yu, Hongfang verfasserin aut Chang, Victor verfasserin aut Enthalten in Applied soft computing Amsterdam [u.a.] : Elsevier Science, 2001 97 Online-Ressource (DE-627)334375754 (DE-600)2057709-6 (DE-576)256145733 1568-4946 nnns volume:97 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines AR 97 |
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10.1016/j.asoc.2020.106772 doi (DE-627)ELV005148650 (ELSEVIER)S1568-4946(20)30710-9 DE-627 ger DE-627 rda eng 004 DE-600 54.00 bkl Sun, Gang verfasserin aut Attention distribution guided information transfer networks for recommendation in practice 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Recently, an increasing number of deep learning-based methods have been applied in recommendation. Most such methods outperform traditional methods, especially when using the natural language processing (NLP) technique with review texts. Many deep learning-based recommender systems are used to learn latent representations of reviews written by target users and reviews written for target items. They are then combined to predict the rating of the target user for the target item. However, most previously proposed review-based deep learning methods do not conform to real-world application scenarios, in which we cannot obtain the reviews of the target user for the target item (called U2I review). In real-world recommendation settings, items are always recommended to users before they have experienced them. Therefore, the review of a target user for a target item would not be available during the testing and validation process. Many methods, such as DeepCoNN and D-ATT, do not exclude the U2I review in the process of validation and testing. Therefore, the process of testing is different from real-world application scenarios, and these methods obtain substantial valuable information from the U2I review that target users write for target items. We propose a model called ADGITN and a training strategy to solve this problem. When training, the auxiliary model learns two attention distributions that the U2I reviews over user reviews and item reviews by auxiliary tasks. These two distributions are used to guide the learning of attention distributions between user reviews and item reviews of the main model. Thus, the main model could learn how to extract attention distributions between user reviews and item reviews according to the valuable information extracted from U2I reviews. During validation, only the main model works, and it could extract better attention distributions even without the help of a U2I review. Extensive experiments show the effectiveness of our model. We validate our model on the Amazon and Yelp19 datasets, and the results show that our model outperforms existing excellent models, with up to 13.8% relative improvement compared to the performance of MPCN, which is one of the best review-based deep learning models for recommendation. Review-based recommender system Natural language processing Auxiliary tasks Teacher–student architecture Li, Yu verfasserin aut Yu, Hongfang verfasserin aut Chang, Victor verfasserin aut Enthalten in Applied soft computing Amsterdam [u.a.] : Elsevier Science, 2001 97 Online-Ressource (DE-627)334375754 (DE-600)2057709-6 (DE-576)256145733 1568-4946 nnns volume:97 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines AR 97 |
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004 DE-600 54.00 bkl Attention distribution guided information transfer networks for recommendation in practice Review-based recommender system Natural language processing Auxiliary tasks Teacher–student architecture |
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attention distribution guided information transfer networks for recommendation in practice |
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Attention distribution guided information transfer networks for recommendation in practice |
| abstract |
Recently, an increasing number of deep learning-based methods have been applied in recommendation. Most such methods outperform traditional methods, especially when using the natural language processing (NLP) technique with review texts. Many deep learning-based recommender systems are used to learn latent representations of reviews written by target users and reviews written for target items. They are then combined to predict the rating of the target user for the target item. However, most previously proposed review-based deep learning methods do not conform to real-world application scenarios, in which we cannot obtain the reviews of the target user for the target item (called U2I review). In real-world recommendation settings, items are always recommended to users before they have experienced them. Therefore, the review of a target user for a target item would not be available during the testing and validation process. Many methods, such as DeepCoNN and D-ATT, do not exclude the U2I review in the process of validation and testing. Therefore, the process of testing is different from real-world application scenarios, and these methods obtain substantial valuable information from the U2I review that target users write for target items. We propose a model called ADGITN and a training strategy to solve this problem. When training, the auxiliary model learns two attention distributions that the U2I reviews over user reviews and item reviews by auxiliary tasks. These two distributions are used to guide the learning of attention distributions between user reviews and item reviews of the main model. Thus, the main model could learn how to extract attention distributions between user reviews and item reviews according to the valuable information extracted from U2I reviews. During validation, only the main model works, and it could extract better attention distributions even without the help of a U2I review. Extensive experiments show the effectiveness of our model. We validate our model on the Amazon and Yelp19 datasets, and the results show that our model outperforms existing excellent models, with up to 13.8% relative improvement compared to the performance of MPCN, which is one of the best review-based deep learning models for recommendation. |
| abstractGer |
Recently, an increasing number of deep learning-based methods have been applied in recommendation. Most such methods outperform traditional methods, especially when using the natural language processing (NLP) technique with review texts. Many deep learning-based recommender systems are used to learn latent representations of reviews written by target users and reviews written for target items. They are then combined to predict the rating of the target user for the target item. However, most previously proposed review-based deep learning methods do not conform to real-world application scenarios, in which we cannot obtain the reviews of the target user for the target item (called U2I review). In real-world recommendation settings, items are always recommended to users before they have experienced them. Therefore, the review of a target user for a target item would not be available during the testing and validation process. Many methods, such as DeepCoNN and D-ATT, do not exclude the U2I review in the process of validation and testing. Therefore, the process of testing is different from real-world application scenarios, and these methods obtain substantial valuable information from the U2I review that target users write for target items. We propose a model called ADGITN and a training strategy to solve this problem. When training, the auxiliary model learns two attention distributions that the U2I reviews over user reviews and item reviews by auxiliary tasks. These two distributions are used to guide the learning of attention distributions between user reviews and item reviews of the main model. Thus, the main model could learn how to extract attention distributions between user reviews and item reviews according to the valuable information extracted from U2I reviews. During validation, only the main model works, and it could extract better attention distributions even without the help of a U2I review. Extensive experiments show the effectiveness of our model. We validate our model on the Amazon and Yelp19 datasets, and the results show that our model outperforms existing excellent models, with up to 13.8% relative improvement compared to the performance of MPCN, which is one of the best review-based deep learning models for recommendation. |
| abstract_unstemmed |
Recently, an increasing number of deep learning-based methods have been applied in recommendation. Most such methods outperform traditional methods, especially when using the natural language processing (NLP) technique with review texts. Many deep learning-based recommender systems are used to learn latent representations of reviews written by target users and reviews written for target items. They are then combined to predict the rating of the target user for the target item. However, most previously proposed review-based deep learning methods do not conform to real-world application scenarios, in which we cannot obtain the reviews of the target user for the target item (called U2I review). In real-world recommendation settings, items are always recommended to users before they have experienced them. Therefore, the review of a target user for a target item would not be available during the testing and validation process. Many methods, such as DeepCoNN and D-ATT, do not exclude the U2I review in the process of validation and testing. Therefore, the process of testing is different from real-world application scenarios, and these methods obtain substantial valuable information from the U2I review that target users write for target items. We propose a model called ADGITN and a training strategy to solve this problem. When training, the auxiliary model learns two attention distributions that the U2I reviews over user reviews and item reviews by auxiliary tasks. These two distributions are used to guide the learning of attention distributions between user reviews and item reviews of the main model. Thus, the main model could learn how to extract attention distributions between user reviews and item reviews according to the valuable information extracted from U2I reviews. During validation, only the main model works, and it could extract better attention distributions even without the help of a U2I review. Extensive experiments show the effectiveness of our model. We validate our model on the Amazon and Yelp19 datasets, and the results show that our model outperforms existing excellent models, with up to 13.8% relative improvement compared to the performance of MPCN, which is one of the best review-based deep learning models for recommendation. |
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|
| score |
7.39966 |