Logit Averaging: Capturing Global Relation for Session-Based Recommendation
Session-based recommendation predicts an anonymous user’s next action, whether she or he is likely to purchase based on the user’s behavior in the current session as sequences. Most recent research on session-based recommendations makes predictions based on a single-session without incorporating glo...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/9/4256 |
| _version_ | 1797505756432433152 |
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| author | Heeyoon Yang Gahyung Kim Jee-Hyoung Lee |
| author_facet | Heeyoon Yang Gahyung Kim Jee-Hyoung Lee |
| author_sort | Heeyoon Yang |
| collection | DOAJ |
| description | Session-based recommendation predicts an anonymous user’s next action, whether she or he is likely to purchase based on the user’s behavior in the current session as sequences. Most recent research on session-based recommendations makes predictions based on a single-session without incorporating global relationships between sessions. It does not guarantee a better performance because item embeddings learned by solely utilizing a single session (inter-session) have less item transition information than utilizing both intra- and inter-session ones. Some existing methods tried to enhance recommendation performance by adopting memory modules and global transition graphs; however, those need more computation cost and time. We propose a novel algorithm called Logit Averaging (LA), utilizing both (i) local-level logits, which come from intra-sessions item transitions and (ii) global-level logits, which come from gathered logits of related sessions. The proposed method shows an improvement in recommendation performance in respect of accuracy and diversity through extensive experiments. |
| first_indexed | 2024-03-10T04:22:45Z |
| format | Article |
| id | doaj.art-4b051119307848b8891eb6813a8a1597 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T04:22:45Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-4b051119307848b8891eb6813a8a15972023-11-23T07:46:26ZengMDPI AGApplied Sciences2076-34172022-04-01129425610.3390/app12094256Logit Averaging: Capturing Global Relation for Session-Based RecommendationHeeyoon Yang0Gahyung Kim1Jee-Hyoung Lee2Department of Artificial Intelligent, SungKyunKwan University, Suwon 16419, KoreaDepartment of Artificial Intelligent, SungKyunKwan University, Suwon 16419, KoreaDepartment of Artificial Intelligent, SungKyunKwan University, Suwon 16419, KoreaSession-based recommendation predicts an anonymous user’s next action, whether she or he is likely to purchase based on the user’s behavior in the current session as sequences. Most recent research on session-based recommendations makes predictions based on a single-session without incorporating global relationships between sessions. It does not guarantee a better performance because item embeddings learned by solely utilizing a single session (inter-session) have less item transition information than utilizing both intra- and inter-session ones. Some existing methods tried to enhance recommendation performance by adopting memory modules and global transition graphs; however, those need more computation cost and time. We propose a novel algorithm called Logit Averaging (LA), utilizing both (i) local-level logits, which come from intra-sessions item transitions and (ii) global-level logits, which come from gathered logits of related sessions. The proposed method shows an improvement in recommendation performance in respect of accuracy and diversity through extensive experiments.https://www.mdpi.com/2076-3417/12/9/4256session-based recommendationglobal relationlong-tail distributionLogit Averaging |
| spellingShingle | Heeyoon Yang Gahyung Kim Jee-Hyoung Lee Logit Averaging: Capturing Global Relation for Session-Based Recommendation Applied Sciences session-based recommendation global relation long-tail distribution Logit Averaging |
| title | Logit Averaging: Capturing Global Relation for Session-Based Recommendation |
| title_full | Logit Averaging: Capturing Global Relation for Session-Based Recommendation |
| title_fullStr | Logit Averaging: Capturing Global Relation for Session-Based Recommendation |
| title_full_unstemmed | Logit Averaging: Capturing Global Relation for Session-Based Recommendation |
| title_short | Logit Averaging: Capturing Global Relation for Session-Based Recommendation |
| title_sort | logit averaging capturing global relation for session based recommendation |
| topic | session-based recommendation global relation long-tail distribution Logit Averaging |
| url | https://www.mdpi.com/2076-3417/12/9/4256 |
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