Effective Diversification of Multi-Carousel Book Recommendation

TL;DR

This paper proposes novel methods to enhance item diversity in multi-carousel book recommendations for public libraries, balancing accuracy with diversity to improve user engagement.

Contribution

It introduces new approaches to increase diversity in book recommendations on top of collaborative filtering, tailored for library web catalogs.

Findings

Proposed strategies improve diversity without sacrificing accuracy.

Metrics effectively evaluate the balance between accuracy and diversity.

System demonstrates enhanced user engagement potential.

Abstract

Using multiple carousels, lists that wrap around and can be scrolled, is the basis for offering content in most contemporary movie streaming platforms. Carousels allow for highlighting different aspects of users' taste, that fall in categories such as genres and authors. However, while carousels offer structure and greater ease of navigation, they alone do not increase diversity in recommendations, while this is essential to keep users engaged. In this work we propose several approaches to effectively increase item diversity within the domain of book recommendations, on top of a collaborative filtering algorithm. These approaches are intended to improve book recommendations in the web catalogs of public libraries. Furthermore, we introduce metrics to evaluate the resulting strategies, and show that the proposed system finds a suitable balance between accuracy and beyond-accuracy aspects.