2014/10/19 by Georgios Pitsilis, Wei Wang, Pitsilis, Georgios +1
Computer Science · #FOS: Computer and information sciences #Image and Video Quality Assessment #Information Retrieval (cs.IR) #Recommender Systems and Techniques #Social and Information Networks (cs.SI) #Video Analysis and Summarization
paper · pdf · doi:10.48550/arxiv.1410.5072
openalex publication_date 2014/10/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Social bookmarking and tagging has emerged a new era in user collaboration. Collaborative Tagging allows users to annotate content of their liking, which via the appropriate algorithms can render useful for the provision of product recommendations. It is the case today for tag-based algorithms to work complementary to rating-based recommendation mechanisms to predict the user liking to various products. In this paper we propose an alternative algorithm for computing personalized recommendations of products, that uses exclusively the tags provided by the users. Our approach is based on the idea of using the semantic similarity of the user-provided tags for clustering them into groups of similar meaning. Afterwards, some measurable characteristics of users' Annotation Competency are combined with other metrics, such as user similarity, for computing predictions. The evaluation on data used from a real-world collaborative tagging system, citeUlike, confirmed that our approach outperforms the baseline Vector Space model, as well as other state of the art algorithms, predicting the user liking more accurately.