Building Your Own Reading List Anytime via Embedding Relevance, Quality, Timeliness and Diversity

Abstract

During every summer holidays, several editions of reading lists are recommended and emerged on mass media, e.g., New York Times, and BBC. However, these reading lists are built for whole people with general topics for some purposes. What if we expect the books of a specific topic at a specific moment? How to generate the requested reading list for our own automatically? In this paper, we propose a searching framework for building a topical reading list anytime, where the Relevance (between topics and books), Quality (of books), Timeliness (of popularities) and Diversity (of results) are embedded into vector representations respectively based on user-generated contents and statistics on social media. We collected 8,197 real-world topics from 198 diverse groups on Librarything.com. The proposed methods are evaluated on the topic collection and the public benchmarks Social Book Search 2012-2016 (SBS). Experimental results demonstrate the robustness and effectiveness of our framework.