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Paper Type
Complete
Description
Recommendation Systems (RS) tackle Information Overload on the web, and Collaborative Filtering (CF) is a widely used technique to estimate content relevance for users based on similar profiles. Traditional CF, however, often overlooks hidden semantic information and item relationships. This work proposes an RS that integrates Linked Open Data with CF to improve recommendation precision by leveraging semantic information about resources and their relationships. The approach calculates semantic similarity among resources using the Linked Open Data graph. By comparing error rates (MAE and RMSE) in varying scenarios, the proposed approach shows at least a 1.5% improvement in precision rates compared to traditional CF. This study contributes insights for researchers seeking to exploit Linked Open Data in recommendation contexts.
Paper Number
1010
Recommended Citation
de Souza, Paulo Roberto; Durao, Frederico Araujo; Dias de Almeida, João Paulo; Mascarenhas, Ramon; and Dos Santos Oliveira, Mayki, "Exploiting Linked Open Data for a Collaborative Filtering Recommendation System" (2023). AMCIS 2023 Proceedings. 1.
https://aisel.aisnet.org/amcis2023/sig_odis/sig_odis/1
Exploiting Linked Open Data for a Collaborative Filtering Recommendation System
Recommendation Systems (RS) tackle Information Overload on the web, and Collaborative Filtering (CF) is a widely used technique to estimate content relevance for users based on similar profiles. Traditional CF, however, often overlooks hidden semantic information and item relationships. This work proposes an RS that integrates Linked Open Data with CF to improve recommendation precision by leveraging semantic information about resources and their relationships. The approach calculates semantic similarity among resources using the Linked Open Data graph. By comparing error rates (MAE and RMSE) in varying scenarios, the proposed approach shows at least a 1.5% improvement in precision rates compared to traditional CF. This study contributes insights for researchers seeking to exploit Linked Open Data in recommendation contexts.
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