Personalized search aims to tailor the retrieval results to the particular interests of individual users. The user’s cognitive level and level of expertise in specific domains are some of the key factors to be considered during the development of personalized models. This work proposes the integration of a Mixture-of-Experts architecture with Dense Retrieval Models, leveraging the cognitive complexity levels defined in Bloom’s Taxonomy for personalized Information Retrieval based on the user’s level of expertise in educational search.
Sokli, E., Raganato, A., Pasi, G. (2024). Incorporating Cognitive Complexity of Text in Dense Retrieval for Personalized Search. In Proceedings of the 14th Italian Information Retrieval Workshop (pp.82-85). CEUR-WS.
Incorporating Cognitive Complexity of Text in Dense Retrieval for Personalized Search
Sokli E.;Raganato A.;Pasi G.
2024
Abstract
Personalized search aims to tailor the retrieval results to the particular interests of individual users. The user’s cognitive level and level of expertise in specific domains are some of the key factors to be considered during the development of personalized models. This work proposes the integration of a Mixture-of-Experts architecture with Dense Retrieval Models, leveraging the cognitive complexity levels defined in Bloom’s Taxonomy for personalized Information Retrieval based on the user’s level of expertise in educational search.
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