Recommendation systems with user and item profiles based on symbolic modal data

• Published in: Neural computing & applications 2024-12, Vol.36 (35), p.22315-22333
• Main Authors: Sampaio-Neto, Delmiro D., Silva Filho, Telmo M., Souza, Renata M. C. R.
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Collaboration; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Filtration; Image Processing and Computer Vision; Modal data; Original Article; Probability and Statistics in Computer Science; Qualitative analysis; Recommender systems; User profiles
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-024-10411-y

Most recommendation systems are implemented using numerical or categorical data, that is, traditional data. This type of data can be a limiting factor when used to model complex concepts where there is internal variability or internal structure in the data. To overcome these limitations, symbolic data is used, where data can be represented by different types of values, such as intervals, lists, or histograms. This work introduces a single approach to constructing recommendation systems based on content or based on collaborative filtering using modal variables for users and items. In the content-based system, user profiles and item profiles are created from modal representations of their features, and a list of items is matched against a user profile. For collaborative filtering, user profiles are built, and users are grouped to form a neighborhood, products rated by users of this neighborhood are recommended based on the similarity between the neighbor and the user who will receive the recommendation. Experiments are carried out, using a movie domain dataset, to evaluate the effectiveness of the proposed approach. The outcomes suggest our ability to generate ranked lists of superior quality compared to previous methods utilizing symbolic data. Specifically, the lists created through the proposed method exhibit higher normalized discounted cumulative gain and, in qualitative terms, showcase more diverse content.