Quickstep: A Hybrid Recommender System for Scientific Paper Recommendations

Title: Quickstep: A Hybrid Recommender System for Scientific Paper Recommendations

Abstract: Quickstep is a hybrid recommender system designed to help researchers discover relevant scientific papers. It combines content-based and collaborative filtering techniques to provide personalized recommendations. By analyzing a user's browsing history and ratings, Quickstep adapts to the user's preferences over time, making it an effective tool for information retrieval in the rapidly expanding world of online research.

Authors: Stuart E. Middleton, David C. De Roure, and Nigel R. Shadbolt

Introduction: Quickstep aims to address the challenge of filtering vast amounts of online research papers. Traditional search engines struggle to meet this need, as users often find it difficult to articulate their search queries. Quickstep, on the other hand, uses machine learning algorithms to learn from a user's browsing history and ratings, providing personalized recommendations based on the user's interests and preferences.

Methodology: Quickstep employs a multi-class approach to paper classification, allowing it to utilize a hierarchical topic ontology during profile construction. This ontology, derived from the paper's topic taxonomy, helps in organizing the recommendations in a structured manner. The system also uses heuristics to infer negative examples, further improving the accuracy of its recommendations.

Results: Two empirical studies were conducted to evaluate Quickstep's effectiveness. The studies involved real-world users and measured the system's performance against a flat list of recommendations. The results showed that the hierarchical topic ontology significantly improved the quality of recommendations, making it easier for users to find relevant papers.

Implications: Quickstep's hybrid approach to recommender systems has several implications. First, it demonstrates that machine learning techniques can be effectively applied to unobtrusive monitoring of user preferences, leading to more accurate and personalized recommendations. Second, it highlights the importance of using a hierarchical topic ontology in recommender systems, as it helps in organizing recommendations and enhances user experience.

Conclusion: Quickstep is a promising tool for scientific paper recommendation, combining content-based and collaborative filtering techniques with a hierarchical topic ontology. Its ability to adapt to user preferences over time makes it an effective solution for managing the ever-growing volume of online research papers.

Link to Article: https://arxiv.org/abs/0203011v1 Authors: arXiv ID: 0203011v1