Spammer Types in Recommender Systems

Recommender systems play a crucial role in delivering personalized recommendations to users in various online platforms. As recommender systems continue to play a vital role in enhancing user experiences and facilitating decision-making, combatting spammers and ensuring the quality and reliability of recommendations remains a critical endeavor. The collaborative efforts of researchers, system administrators, and industry stakeholders are necessary to create a safer and more trustworthy online environment for users. However, the presence of spammers in recommender systems poses significant challenges to their effectiveness and user experience. Spamming activities can distort the integrity of recommendations and hinder the system's ability to provide accurate and relevant suggestions. This paper aims to investigate different types of spammers in recommender systems and explore spam detection techniques, with a particular focus on identifying spammer words and patterns.