A tag-based recommender system

Abstract

Recommender systems are being used more and more on the web thanks to their ability to predict user preferences and drive user attention toward new items, increasing sales, and engagement. However, the use of such systems is still very limited to e-commerce and music or movies websites and, most of the times, the user is presented with recommendations limited to products. Our idea is to provide suggestions that are content-agnostic and that can be used to recommend mixed types of contents at the same time (for example, images, posts, and products). In such a way, the power of recommender systems can be exploited in very diverse contexts using a unique model with few adjustments. To achieve this, we provide a tag-based recommender system with a highly scalable implementation that is proposed with the aim of providing performance and reusability in a Software as a Service (SaaS) package.