Exploiting Preferences in Loss Functions for Sequential Recommendation via Weak Transitivity
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866914895578005504 |
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| author | Chung, Hyunsoo Kim, Jungtaek Jo, Hyungeun Choi, Hyungwon |
| author_facet | Chung, Hyunsoo Kim, Jungtaek Jo, Hyungeun Choi, Hyungwon |
| contents | A choice of optimization objective is immensely pivotal in the design of a recommender system as it affects the general modeling process of a user's intent from previous interactions. Existing approaches mainly adhere to three categories of loss functions: pairwise, pointwise, and setwise loss functions. Despite their effectiveness, a critical and common drawback of such objectives is viewing the next observed item as a unique positive while considering all remaining items equally negative. Such a binary label assignment is generally limited to assuring a higher recommendation score of the positive item, neglecting potential structures induced by varying preferences between other unobserved items. To alleviate this issue, we propose a novel method that extends original objectives to explicitly leverage the different levels of preferences as relative orders between their scores. Finally, we demonstrate the superior performance of our method compared to baseline objectives. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_00326 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Exploiting Preferences in Loss Functions for Sequential Recommendation via Weak Transitivity Chung, Hyunsoo Kim, Jungtaek Jo, Hyungeun Choi, Hyungwon Machine Learning Information Retrieval A choice of optimization objective is immensely pivotal in the design of a recommender system as it affects the general modeling process of a user's intent from previous interactions. Existing approaches mainly adhere to three categories of loss functions: pairwise, pointwise, and setwise loss functions. Despite their effectiveness, a critical and common drawback of such objectives is viewing the next observed item as a unique positive while considering all remaining items equally negative. Such a binary label assignment is generally limited to assuring a higher recommendation score of the positive item, neglecting potential structures induced by varying preferences between other unobserved items. To alleviate this issue, we propose a novel method that extends original objectives to explicitly leverage the different levels of preferences as relative orders between their scores. Finally, we demonstrate the superior performance of our method compared to baseline objectives. |
| title | Exploiting Preferences in Loss Functions for Sequential Recommendation via Weak Transitivity |
| topic | Machine Learning Information Retrieval |
| url | https://arxiv.org/abs/2408.00326 |