Sánchez Pérez, Pablo Bellogín, Alejandro 2025-09-26T17:57:38Z 2025-09-26T17:57:38Z 2025-04-03 http://hdl.handle.net/11531/105298 Capítulos en libros Neighborhood-based approaches remain widely used techniques in collaborative filtering recommender systems due to their versatility, simplicity, and efficiency. Traditionally, these algorithms consider similarity functions to measure how close user or item interactions are. However, their focus on capturing similar tastes often overlooks divergent preferences that could enhance recommendations. In this paper, we explore alternative methods to incorporate such information to improve beyond-accuracy performance in this type of recommenders.We define three mechanisms based on various modeling assumptions to integrate differing preferences into traditional nearest neighbors algorithms.Our comparison on four well-known and different datasets shows that our proposed approach can enhance the novelty and diversity of the recommendations while maintaining ranking accuracy. Our implementation is available at https:github.compablosanchezpkNNDissimilarities . Neighborhood-based approaches remain widely used techniques in collaborative filtering recommender systems due to their versatility, simplicity, and efficiency. Traditionally, these algorithms consider similarity functions to measure how close user or item interactions are. However, their focus on capturing similar tastes often overlooks divergent preferences that could enhance recommendations. In this paper, we explore alternative methods to incorporate such information to improve beyond-accuracy performance in this type of recommenders.We define three mechanisms based on various modeling assumptions to integrate differing preferences into traditional nearest neighbors algorithms.Our comparison on four well-known and different datasets shows that our proposed approach can enhance the novelty and diversity of the recommendations while maintaining ranking accuracy. Our implementation is available at https:github.compablosanchezpkNNDissimilarities . application/pdf en-GB Consiglio Nazionale delle Ricerche; Universitá di Pisa; Sapienza Università di Roma; Scuola IMT Alti (Lucca, Italia) Libro: 47th European Conference on Information Retrieval - ECIR 2025, Página inicial: 187-196, Página final: Instituto de Investigación Tecnológica (IIT) Improving Novelty and Diversity of Nearest-Neighbors Recommendation by Exploiting Dissimilarities info:eu-repo/semantics/bookPart info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/restrictedAccess Nearest neighbors · Beyond-accuracy evaluation · Dissimilarity Nearest neighbors · Beyond-accuracy evaluation · Dissimilarity