Fast Slate Policy Optimization: Going Beyond Plackett-Luce
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866909056430505984 |
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| author | Sakhi, Otmane Rohde, David Chopin, Nicolas |
| author_facet | Sakhi, Otmane Rohde, David Chopin, Nicolas |
| contents | An increasingly important building block of large scale machine learning systems is based on returning slates; an ordered lists of items given a query. Applications of this technology include: search, information retrieval and recommender systems. When the action space is large, decision systems are restricted to a particular structure to complete online queries quickly. This paper addresses the optimization of these large scale decision systems given an arbitrary reward function. We cast this learning problem in a policy optimization framework and propose a new class of policies, born from a novel relaxation of decision functions. This results in a simple, yet efficient learning algorithm that scales to massive action spaces. We compare our method to the commonly adopted Plackett-Luce policy class and demonstrate the effectiveness of our approach on problems with action space sizes in the order of millions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_01566 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Fast Slate Policy Optimization: Going Beyond Plackett-Luce Sakhi, Otmane Rohde, David Chopin, Nicolas Machine Learning Information Retrieval An increasingly important building block of large scale machine learning systems is based on returning slates; an ordered lists of items given a query. Applications of this technology include: search, information retrieval and recommender systems. When the action space is large, decision systems are restricted to a particular structure to complete online queries quickly. This paper addresses the optimization of these large scale decision systems given an arbitrary reward function. We cast this learning problem in a policy optimization framework and propose a new class of policies, born from a novel relaxation of decision functions. This results in a simple, yet efficient learning algorithm that scales to massive action spaces. We compare our method to the commonly adopted Plackett-Luce policy class and demonstrate the effectiveness of our approach on problems with action space sizes in the order of millions. |
| title | Fast Slate Policy Optimization: Going Beyond Plackett-Luce |
| topic | Machine Learning Information Retrieval |
| url | https://arxiv.org/abs/2308.01566 |