Modified collaborative filtering for hybrid recommender systems and personalized search: The case of digital library

Koliarakis, Antonios; Krouska, Akrivi; Troussas, Christos; Sgouropoulou, Cleo

doi:10.1109/smap56125.2022.9942020

Cited by 4 publications

(1 citation statement)

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“…There are a great variety of algorithms and techniques available in the field of machine learning, of which the domain of designing a recommender system has access to a diverse set. Collaborative Filtering (CF) [15,16] is one such technique that involves collecting data on numerous peers' preferences in order to make predictions about users' preferences automatically. The CF principle argues that people who have the same view about one subject are more likely to have the same attitude about another subject than any other random user.…”

Section: Introductionmentioning

confidence: 99%

Harnessing the Power of User-Centric Artificial Intelligence: Customized Recommendations and Personalization in Hybrid Recommender Systems

et al. 2023

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Recommender systems are widely used in various fields, such as e-commerce, entertainment, and education, to provide personalized recommendations to users based on their preferences and/or behavior. Τhis paper presents a novel approach to providing customized recommendations with the use of user-centric artificial intelligence. In greater detail, we introduce an enhanced collaborative filtering (CF) approach in order to develop hybrid recommender systems that personalize search results for users. The proposed CF enhancement incorporates user actions beyond explicit ratings to collect data and alleviate the issue of sparse data, resulting in high-quality recommendations. As a testbed for our research, a web-based digital library, incorporating the proposed algorithm, has been developed. Examples of operation of the use of the system are presented using cognitive walkthrough inspection, which demonstrates the effectiveness of the approach in producing personalized recommendations and improving user experience. Thus, the hybrid recommender system, which is incorporated in the digital library, has been evaluated, yielding promising results.

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Section: Introductionmentioning

confidence: 99%