Modeling shopper interest broadness with entropy-driven dialogue policy in the context of arbitrarily large product catalogs
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912575880429568 |
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| author | Jarboui, Firas Memari, Issa |
| author_facet | Jarboui, Firas Memari, Issa |
| contents | Conversational recommender systems promise rich interactions for e-commerce, but balancing exploration (clarifying user needs) and exploitation (making recommendations) remains challenging, especially when deploying large language models (LLMs) with vast product catalogs. We address this challenge by modeling the breadth of user interest via the entropy of retrieval score distributions. Our method uses a neural retriever to fetch relevant items for a user query and computes the entropy of the re-ranked scores to dynamically route the dialogue policy: low-entropy (specific) queries trigger direct recommendations, whereas high-entropy (ambiguous) queries prompt exploratory questions. This simple yet effective strategy allows an LLM-driven agent to remain aware of an arbitrarily large catalog in real-time without bloating its context window. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_06185 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Modeling shopper interest broadness with entropy-driven dialogue policy in the context of arbitrarily large product catalogs Jarboui, Firas Memari, Issa Information Retrieval Machine Learning Conversational recommender systems promise rich interactions for e-commerce, but balancing exploration (clarifying user needs) and exploitation (making recommendations) remains challenging, especially when deploying large language models (LLMs) with vast product catalogs. We address this challenge by modeling the breadth of user interest via the entropy of retrieval score distributions. Our method uses a neural retriever to fetch relevant items for a user query and computes the entropy of the re-ranked scores to dynamically route the dialogue policy: low-entropy (specific) queries trigger direct recommendations, whereas high-entropy (ambiguous) queries prompt exploratory questions. This simple yet effective strategy allows an LLM-driven agent to remain aware of an arbitrarily large catalog in real-time without bloating its context window. |
| title | Modeling shopper interest broadness with entropy-driven dialogue policy in the context of arbitrarily large product catalogs |
| topic | Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2509.06185 |