Dense Retrieval Models (DRMs) estimate the semantic similarity between queries and documents based on their embeddings. Prior studies highlight the importance of embedding contextualization in enhancing retrieval performance. To this aim, existing approaches primarily leverage token-level information derived from query/document interactions. In this paper, we introduce a novel DRM, namely DenseC3, which leverages query/document interactions based on the full embedding representations generated by a Transformer-based model. To enhance similarity estimation, DenseC3 integrates external linguistic information about the Cognitive Complexity of texts, enriching the contextualization of embeddings. We empirically evaluate our approach across seven benchmarks and three different IR tasks to assess the impact of Cognitive Complexity-aware query and document embeddings for contextualization in dense retrieval. Results show that our approach consistently outperforms standard fine-tuning techniques on lightweight bi-encoders (e.g., BERT-based) and traditional late-interaction models (i.e., ColBERT) across all benchmarks. On larger retrieval-optimized bi-encoders like Contriever, our model achieves comparable or higher performance on four of the considered evaluation benchmarks. Our findings suggest that Cognitive Complexity-aware embeddings enhance query and document representations, improving retrieval effectiveness in DRMs. Our code is available online at: https://github.com/FaySokli/DenseC3.

Personalization of search results has gained increasing attention in the past few years, also thanks to the development of Neural Networks-based approaches for Information Retrieval. Recent works have proposed to build user models at query time by leveraging the Attention mechanism, which allows weighing the contribution of the user-related information w.r.t. the current query.This approach allows giving more importance to the user’s interests related to the current search performed by the user.In this paper, we discuss some shortcomings of the Attention mechanism when employed for personalization and introduce a novel Attention variant, the Denoising Attention, to solve them.Denoising Attention adopts a robust normalization scheme and introduces a filtering mechanism to better discern among the user-related data those helpful for personalization.Experimental evaluation shows improvements in MAP, MRR, and NDCG above 15% w.r.t. other Attention variants at the state-of-the-art.