提升图书推荐多样性，让图书馆平台推荐更丰富有趣。
Effective Diversification of Multi-Carousel Book Recommendation
- 在协同过滤基础上设计多样化推荐策略
- 实测在保持准确率的同时显著提升多样性
- 适合关注推荐系统多样性的图书馆与平台
当前多数影视流媒体平台采用多轮播图（multi-carousel）形式展示内容，轮播图可突出用户兴趣的多个维度，如类型和作者。然而，仅靠轮播图无法提升推荐多样性，而多样性对维持用户参与至关重要。本文针对公共图书馆图书推荐场景，提出多种在协同过滤基础上增强项目多样性的方法。同时引入评估指标，验证所提系统能在保持推荐准确率的同时有效提升多样性，实现精度与多样性的良好平衡。
原文摘要 · Abstract (English)
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.
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