Rs4rs: Semantically Find Recent Publications from Top Recommendation System-Related Venues
Tri Kurniawan Wijaya, Edoardo D'Amico, Gabor Fodor, Manuel V. Loureiro
TL;DR
Rs4rs addresses the challenge of locating highly relevant, high-quality Recommender Systems literature by offering a targeted semantic search platform restricted to top RS venues and recent papers. It employs an offline batch pipeline to compute embeddings using two SBERT models, all-mpnet-base-v2 and multi-qa-mpnet-base-dot-v1, and online ranking via averaged cosine similarity to ensure fast, context-aware results. The pipeline sources venue lists from recsys-info, with titles from DBLP and abstracts from the Semantic Scholar API, and provides a user-friendly interface with filtering, abstract snippets, and direct access to full texts. By focusing on quality sources and semantic retrieval, Rs4rs aims to streamline literature reviews, enable trend detection, and accelerate research progress in the Recommender Systems domain.
Abstract
Rs4rs is a web application designed to perform semantic search on recent papers from top conferences and journals related to Recommender Systems. Current scholarly search engine tools like Google Scholar, Semantic Scholar, and ResearchGate often yield broad results that fail to target the most relevant high-quality publications. Moreover, manually visiting individual conference and journal websites is a time-consuming process that primarily supports only syntactic searches. Rs4rs addresses these issues by providing a user-friendly platform where researchers can input their topic of interest and receive a list of recent, relevant papers from top Recommender Systems venues. Utilizing semantic search techniques, Rs4rs ensures that the search results are not only precise and relevant but also comprehensive, capturing papers regardless of variations in wording. This tool significantly enhances research efficiency and accuracy, thereby benefitting the research community and public by facilitating access to high-quality, pertinent academic resources in the field of Recommender Systems. Rs4rs is available at https://rs4rs.com.