Session-based recommendation with an importance extraction module
Abstract The goal of session-based recommendation is to make item predictions at the next timestamp based on the anonymous ongoing session. Previous work mainly models user’s preference by exploring the transition pattern between the interacted items in the session. However, they generally fail to p...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Pan, Zhiqiang [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Neural computing & applications - London : Springer, 1993, 34(2022), 12 vom: 05. Feb., Seite 9813-9829 |
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| Übergeordnetes Werk: |
volume:34 ; year:2022 ; number:12 ; day:05 ; month:02 ; pages:9813-9829 |
| Links: |
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| DOI / URN: |
10.1007/s00521-022-06966-3 |
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| Katalog-ID: |
SPR047013524 |
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| 520 | |a Abstract The goal of session-based recommendation is to make item predictions at the next timestamp based on the anonymous ongoing session. Previous work mainly models user’s preference by exploring the transition pattern between the interacted items in the session. However, they generally fail to pay enough attention to the item importance in terms of the relevance of the items to user’s main purpose. This paper proposes a Session-based Recommendation approach with an Importance Extraction Module, i.e., SR-IEM%$_{{{\text{improved}}}}%$, which can simultaneously consider user’s long-term interactions and recent behavior in current session. Specifically, we modify the self-attention mechanism to avoid introducing bias for estimating the importance of each item in the session. Then, the item importances are utilized to produce user’s long-term preference, and the sequential signals are incorporated in the long-term interest modeling. Next, the long-term preference and user’s current interest which is conveyed by the last interacted item in the session are combined to obtain user’s final preference representation. Finally, item predictions are generated using the user preference, where a normalization layer is adopted to solve the long-tail problem. Extensive experiments are conducted on three public benchmark datasets, i.e., Yoochoose 1/64, Yoochoose 1/4 and Diginetica. The experimental results show that SR-IEM%$_{{{\text{improved}}}}%$ can outperform the start-of-the-art baselines in terms of Recall and MRR for session-based recommendation. In addition, compared to the state-of-the-art neural methods, SR-IEM%$_{{{\text{improved}}}}%$ can obviously reduce the computational complexity. | ||
| 650 | 4 | |a Recommender systems |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Session-based recommendation |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Importance extraction module |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Self-attention mechanism |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Cai, Fei |0 (orcid)0000-0002-5709-1682 |4 aut | |
| 700 | 1 | |a Chen, Wanyu |4 aut | |
| 700 | 1 | |a Chen, Honghui |4 aut | |
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10.1007/s00521-022-06966-3 doi (DE-627)SPR047013524 (SPR)s00521-022-06966-3-e DE-627 ger DE-627 rakwb eng Pan, Zhiqiang verfasserin (orcid)0000-0001-5484-1743 aut Session-based recommendation with an importance extraction module 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 Abstract The goal of session-based recommendation is to make item predictions at the next timestamp based on the anonymous ongoing session. Previous work mainly models user’s preference by exploring the transition pattern between the interacted items in the session. However, they generally fail to pay enough attention to the item importance in terms of the relevance of the items to user’s main purpose. This paper proposes a Session-based Recommendation approach with an Importance Extraction Module, i.e., SR-IEM%$_{{{\text{improved}}}}%$, which can simultaneously consider user’s long-term interactions and recent behavior in current session. Specifically, we modify the self-attention mechanism to avoid introducing bias for estimating the importance of each item in the session. Then, the item importances are utilized to produce user’s long-term preference, and the sequential signals are incorporated in the long-term interest modeling. Next, the long-term preference and user’s current interest which is conveyed by the last interacted item in the session are combined to obtain user’s final preference representation. Finally, item predictions are generated using the user preference, where a normalization layer is adopted to solve the long-tail problem. Extensive experiments are conducted on three public benchmark datasets, i.e., Yoochoose 1/64, Yoochoose 1/4 and Diginetica. The experimental results show that SR-IEM%$_{{{\text{improved}}}}%$ can outperform the start-of-the-art baselines in terms of Recall and MRR for session-based recommendation. In addition, compared to the state-of-the-art neural methods, SR-IEM%$_{{{\text{improved}}}}%$ can obviously reduce the computational complexity. Recommender systems (dpeaa)DE-He213 Session-based recommendation (dpeaa)DE-He213 Importance extraction module (dpeaa)DE-He213 Self-attention mechanism (dpeaa)DE-He213 Cai, Fei (orcid)0000-0002-5709-1682 aut Chen, Wanyu aut Chen, Honghui aut Enthalten in Neural computing & applications London : Springer, 1993 34(2022), 12 vom: 05. Feb., Seite 9813-9829 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:34 year:2022 number:12 day:05 month:02 pages:9813-9829 https://dx.doi.org/10.1007/s00521-022-06966-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 34 2022 12 05 02 9813-9829 |
| spelling |
10.1007/s00521-022-06966-3 doi (DE-627)SPR047013524 (SPR)s00521-022-06966-3-e DE-627 ger DE-627 rakwb eng Pan, Zhiqiang verfasserin (orcid)0000-0001-5484-1743 aut Session-based recommendation with an importance extraction module 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 Abstract The goal of session-based recommendation is to make item predictions at the next timestamp based on the anonymous ongoing session. Previous work mainly models user’s preference by exploring the transition pattern between the interacted items in the session. However, they generally fail to pay enough attention to the item importance in terms of the relevance of the items to user’s main purpose. This paper proposes a Session-based Recommendation approach with an Importance Extraction Module, i.e., SR-IEM%$_{{{\text{improved}}}}%$, which can simultaneously consider user’s long-term interactions and recent behavior in current session. Specifically, we modify the self-attention mechanism to avoid introducing bias for estimating the importance of each item in the session. Then, the item importances are utilized to produce user’s long-term preference, and the sequential signals are incorporated in the long-term interest modeling. Next, the long-term preference and user’s current interest which is conveyed by the last interacted item in the session are combined to obtain user’s final preference representation. Finally, item predictions are generated using the user preference, where a normalization layer is adopted to solve the long-tail problem. Extensive experiments are conducted on three public benchmark datasets, i.e., Yoochoose 1/64, Yoochoose 1/4 and Diginetica. The experimental results show that SR-IEM%$_{{{\text{improved}}}}%$ can outperform the start-of-the-art baselines in terms of Recall and MRR for session-based recommendation. In addition, compared to the state-of-the-art neural methods, SR-IEM%$_{{{\text{improved}}}}%$ can obviously reduce the computational complexity. Recommender systems (dpeaa)DE-He213 Session-based recommendation (dpeaa)DE-He213 Importance extraction module (dpeaa)DE-He213 Self-attention mechanism (dpeaa)DE-He213 Cai, Fei (orcid)0000-0002-5709-1682 aut Chen, Wanyu aut Chen, Honghui aut Enthalten in Neural computing & applications London : Springer, 1993 34(2022), 12 vom: 05. Feb., Seite 9813-9829 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:34 year:2022 number:12 day:05 month:02 pages:9813-9829 https://dx.doi.org/10.1007/s00521-022-06966-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 34 2022 12 05 02 9813-9829 |
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10.1007/s00521-022-06966-3 doi (DE-627)SPR047013524 (SPR)s00521-022-06966-3-e DE-627 ger DE-627 rakwb eng Pan, Zhiqiang verfasserin (orcid)0000-0001-5484-1743 aut Session-based recommendation with an importance extraction module 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 Abstract The goal of session-based recommendation is to make item predictions at the next timestamp based on the anonymous ongoing session. Previous work mainly models user’s preference by exploring the transition pattern between the interacted items in the session. However, they generally fail to pay enough attention to the item importance in terms of the relevance of the items to user’s main purpose. This paper proposes a Session-based Recommendation approach with an Importance Extraction Module, i.e., SR-IEM%$_{{{\text{improved}}}}%$, which can simultaneously consider user’s long-term interactions and recent behavior in current session. Specifically, we modify the self-attention mechanism to avoid introducing bias for estimating the importance of each item in the session. Then, the item importances are utilized to produce user’s long-term preference, and the sequential signals are incorporated in the long-term interest modeling. Next, the long-term preference and user’s current interest which is conveyed by the last interacted item in the session are combined to obtain user’s final preference representation. Finally, item predictions are generated using the user preference, where a normalization layer is adopted to solve the long-tail problem. Extensive experiments are conducted on three public benchmark datasets, i.e., Yoochoose 1/64, Yoochoose 1/4 and Diginetica. The experimental results show that SR-IEM%$_{{{\text{improved}}}}%$ can outperform the start-of-the-art baselines in terms of Recall and MRR for session-based recommendation. In addition, compared to the state-of-the-art neural methods, SR-IEM%$_{{{\text{improved}}}}%$ can obviously reduce the computational complexity. Recommender systems (dpeaa)DE-He213 Session-based recommendation (dpeaa)DE-He213 Importance extraction module (dpeaa)DE-He213 Self-attention mechanism (dpeaa)DE-He213 Cai, Fei (orcid)0000-0002-5709-1682 aut Chen, Wanyu aut Chen, Honghui aut Enthalten in Neural computing & applications London : Springer, 1993 34(2022), 12 vom: 05. Feb., Seite 9813-9829 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:34 year:2022 number:12 day:05 month:02 pages:9813-9829 https://dx.doi.org/10.1007/s00521-022-06966-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 34 2022 12 05 02 9813-9829 |
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10.1007/s00521-022-06966-3 doi (DE-627)SPR047013524 (SPR)s00521-022-06966-3-e DE-627 ger DE-627 rakwb eng Pan, Zhiqiang verfasserin (orcid)0000-0001-5484-1743 aut Session-based recommendation with an importance extraction module 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 Abstract The goal of session-based recommendation is to make item predictions at the next timestamp based on the anonymous ongoing session. Previous work mainly models user’s preference by exploring the transition pattern between the interacted items in the session. However, they generally fail to pay enough attention to the item importance in terms of the relevance of the items to user’s main purpose. This paper proposes a Session-based Recommendation approach with an Importance Extraction Module, i.e., SR-IEM%$_{{{\text{improved}}}}%$, which can simultaneously consider user’s long-term interactions and recent behavior in current session. Specifically, we modify the self-attention mechanism to avoid introducing bias for estimating the importance of each item in the session. Then, the item importances are utilized to produce user’s long-term preference, and the sequential signals are incorporated in the long-term interest modeling. Next, the long-term preference and user’s current interest which is conveyed by the last interacted item in the session are combined to obtain user’s final preference representation. Finally, item predictions are generated using the user preference, where a normalization layer is adopted to solve the long-tail problem. Extensive experiments are conducted on three public benchmark datasets, i.e., Yoochoose 1/64, Yoochoose 1/4 and Diginetica. The experimental results show that SR-IEM%$_{{{\text{improved}}}}%$ can outperform the start-of-the-art baselines in terms of Recall and MRR for session-based recommendation. In addition, compared to the state-of-the-art neural methods, SR-IEM%$_{{{\text{improved}}}}%$ can obviously reduce the computational complexity. Recommender systems (dpeaa)DE-He213 Session-based recommendation (dpeaa)DE-He213 Importance extraction module (dpeaa)DE-He213 Self-attention mechanism (dpeaa)DE-He213 Cai, Fei (orcid)0000-0002-5709-1682 aut Chen, Wanyu aut Chen, Honghui aut Enthalten in Neural computing & applications London : Springer, 1993 34(2022), 12 vom: 05. Feb., Seite 9813-9829 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:34 year:2022 number:12 day:05 month:02 pages:9813-9829 https://dx.doi.org/10.1007/s00521-022-06966-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 34 2022 12 05 02 9813-9829 |
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10.1007/s00521-022-06966-3 doi (DE-627)SPR047013524 (SPR)s00521-022-06966-3-e DE-627 ger DE-627 rakwb eng Pan, Zhiqiang verfasserin (orcid)0000-0001-5484-1743 aut Session-based recommendation with an importance extraction module 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 Abstract The goal of session-based recommendation is to make item predictions at the next timestamp based on the anonymous ongoing session. Previous work mainly models user’s preference by exploring the transition pattern between the interacted items in the session. However, they generally fail to pay enough attention to the item importance in terms of the relevance of the items to user’s main purpose. This paper proposes a Session-based Recommendation approach with an Importance Extraction Module, i.e., SR-IEM%$_{{{\text{improved}}}}%$, which can simultaneously consider user’s long-term interactions and recent behavior in current session. Specifically, we modify the self-attention mechanism to avoid introducing bias for estimating the importance of each item in the session. Then, the item importances are utilized to produce user’s long-term preference, and the sequential signals are incorporated in the long-term interest modeling. Next, the long-term preference and user’s current interest which is conveyed by the last interacted item in the session are combined to obtain user’s final preference representation. Finally, item predictions are generated using the user preference, where a normalization layer is adopted to solve the long-tail problem. Extensive experiments are conducted on three public benchmark datasets, i.e., Yoochoose 1/64, Yoochoose 1/4 and Diginetica. The experimental results show that SR-IEM%$_{{{\text{improved}}}}%$ can outperform the start-of-the-art baselines in terms of Recall and MRR for session-based recommendation. In addition, compared to the state-of-the-art neural methods, SR-IEM%$_{{{\text{improved}}}}%$ can obviously reduce the computational complexity. Recommender systems (dpeaa)DE-He213 Session-based recommendation (dpeaa)DE-He213 Importance extraction module (dpeaa)DE-He213 Self-attention mechanism (dpeaa)DE-He213 Cai, Fei (orcid)0000-0002-5709-1682 aut Chen, Wanyu aut Chen, Honghui aut Enthalten in Neural computing & applications London : Springer, 1993 34(2022), 12 vom: 05. Feb., Seite 9813-9829 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:34 year:2022 number:12 day:05 month:02 pages:9813-9829 https://dx.doi.org/10.1007/s00521-022-06966-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 34 2022 12 05 02 9813-9829 |
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Pan, Zhiqiang @@aut@@ Cai, Fei @@aut@@ Chen, Wanyu @@aut@@ Chen, Honghui @@aut@@ |
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Previous work mainly models user’s preference by exploring the transition pattern between the interacted items in the session. However, they generally fail to pay enough attention to the item importance in terms of the relevance of the items to user’s main purpose. This paper proposes a Session-based Recommendation approach with an Importance Extraction Module, i.e., SR-IEM%$_{{{\text{improved}}}}%$, which can simultaneously consider user’s long-term interactions and recent behavior in current session. Specifically, we modify the self-attention mechanism to avoid introducing bias for estimating the importance of each item in the session. Then, the item importances are utilized to produce user’s long-term preference, and the sequential signals are incorporated in the long-term interest modeling. Next, the long-term preference and user’s current interest which is conveyed by the last interacted item in the session are combined to obtain user’s final preference representation. Finally, item predictions are generated using the user preference, where a normalization layer is adopted to solve the long-tail problem. Extensive experiments are conducted on three public benchmark datasets, i.e., Yoochoose 1/64, Yoochoose 1/4 and Diginetica. The experimental results show that SR-IEM%$_{{{\text{improved}}}}%$ can outperform the start-of-the-art baselines in terms of Recall and MRR for session-based recommendation. In addition, compared to the state-of-the-art neural methods, SR-IEM%$_{{{\text{improved}}}}%$ can obviously reduce the computational complexity.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Recommender systems</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Session-based recommendation</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Importance extraction module</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Self-attention mechanism</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Cai, Fei</subfield><subfield code="0">(orcid)0000-0002-5709-1682</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Chen, Wanyu</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Chen, Honghui</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Neural computing & applications</subfield><subfield code="d">London : Springer, 1993</subfield><subfield code="g">34(2022), 12 vom: 05. 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session-based recommendation with an importance extraction module |
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Session-based recommendation with an importance extraction module |
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Abstract The goal of session-based recommendation is to make item predictions at the next timestamp based on the anonymous ongoing session. Previous work mainly models user’s preference by exploring the transition pattern between the interacted items in the session. However, they generally fail to pay enough attention to the item importance in terms of the relevance of the items to user’s main purpose. This paper proposes a Session-based Recommendation approach with an Importance Extraction Module, i.e., SR-IEM%$_{{{\text{improved}}}}%$, which can simultaneously consider user’s long-term interactions and recent behavior in current session. Specifically, we modify the self-attention mechanism to avoid introducing bias for estimating the importance of each item in the session. Then, the item importances are utilized to produce user’s long-term preference, and the sequential signals are incorporated in the long-term interest modeling. Next, the long-term preference and user’s current interest which is conveyed by the last interacted item in the session are combined to obtain user’s final preference representation. Finally, item predictions are generated using the user preference, where a normalization layer is adopted to solve the long-tail problem. Extensive experiments are conducted on three public benchmark datasets, i.e., Yoochoose 1/64, Yoochoose 1/4 and Diginetica. The experimental results show that SR-IEM%$_{{{\text{improved}}}}%$ can outperform the start-of-the-art baselines in terms of Recall and MRR for session-based recommendation. In addition, compared to the state-of-the-art neural methods, SR-IEM%$_{{{\text{improved}}}}%$ can obviously reduce the computational complexity. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 |
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Abstract The goal of session-based recommendation is to make item predictions at the next timestamp based on the anonymous ongoing session. Previous work mainly models user’s preference by exploring the transition pattern between the interacted items in the session. However, they generally fail to pay enough attention to the item importance in terms of the relevance of the items to user’s main purpose. This paper proposes a Session-based Recommendation approach with an Importance Extraction Module, i.e., SR-IEM%$_{{{\text{improved}}}}%$, which can simultaneously consider user’s long-term interactions and recent behavior in current session. Specifically, we modify the self-attention mechanism to avoid introducing bias for estimating the importance of each item in the session. Then, the item importances are utilized to produce user’s long-term preference, and the sequential signals are incorporated in the long-term interest modeling. Next, the long-term preference and user’s current interest which is conveyed by the last interacted item in the session are combined to obtain user’s final preference representation. Finally, item predictions are generated using the user preference, where a normalization layer is adopted to solve the long-tail problem. Extensive experiments are conducted on three public benchmark datasets, i.e., Yoochoose 1/64, Yoochoose 1/4 and Diginetica. The experimental results show that SR-IEM%$_{{{\text{improved}}}}%$ can outperform the start-of-the-art baselines in terms of Recall and MRR for session-based recommendation. In addition, compared to the state-of-the-art neural methods, SR-IEM%$_{{{\text{improved}}}}%$ can obviously reduce the computational complexity. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 |
| abstract_unstemmed |
Abstract The goal of session-based recommendation is to make item predictions at the next timestamp based on the anonymous ongoing session. Previous work mainly models user’s preference by exploring the transition pattern between the interacted items in the session. However, they generally fail to pay enough attention to the item importance in terms of the relevance of the items to user’s main purpose. This paper proposes a Session-based Recommendation approach with an Importance Extraction Module, i.e., SR-IEM%$_{{{\text{improved}}}}%$, which can simultaneously consider user’s long-term interactions and recent behavior in current session. Specifically, we modify the self-attention mechanism to avoid introducing bias for estimating the importance of each item in the session. Then, the item importances are utilized to produce user’s long-term preference, and the sequential signals are incorporated in the long-term interest modeling. Next, the long-term preference and user’s current interest which is conveyed by the last interacted item in the session are combined to obtain user’s final preference representation. Finally, item predictions are generated using the user preference, where a normalization layer is adopted to solve the long-tail problem. Extensive experiments are conducted on three public benchmark datasets, i.e., Yoochoose 1/64, Yoochoose 1/4 and Diginetica. The experimental results show that SR-IEM%$_{{{\text{improved}}}}%$ can outperform the start-of-the-art baselines in terms of Recall and MRR for session-based recommendation. In addition, compared to the state-of-the-art neural methods, SR-IEM%$_{{{\text{improved}}}}%$ can obviously reduce the computational complexity. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 |
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| title_short |
Session-based recommendation with an importance extraction module |
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https://dx.doi.org/10.1007/s00521-022-06966-3 |
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Cai, Fei Chen, Wanyu Chen, Honghui |
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10.1007/s00521-022-06966-3 |
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2024-07-04T01:28:07.735Z |
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| score |
7.4003353 |