Personalised Reranking of Paper Recommendations Using Paper Content and User Behavior
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Summary
This article examines an academic paper recommender that sends out paper recommendations in email newsletters, based on the users’ browsing history on the academic search engine, and proposes an approach to reranking candidate recommendations that utilizes both paper content and user behavior.
- Type
- article
- Published
- 2019-03-16
- Cited by
- 62
- References
- 74
- OpenAlex
- https://openalex.org/W2923988880
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:83458713
Keywords
Computer science, Recommender system, Information retrieval, Metadata, Focus (optics)
References
- SVDFeature: a toolkit for feature-based collaborative filtering
- A Neural Probabilistic Model for Context Based Citation Recommendation
- Industry Report: Amazon.com Recommendations: Item-to-Item Collaborative Filtering
- Understanding “influence:” an exploratory study of academics' processes of knowledge construction through iterative and interactive information seeking
- Refining Recency Search Results with User Click Feedback
- Context-Based Collaborative Filtering for Citation Recommendation
- Exploring behavior of E-journal users in science and technology: Transaction log analysis of Elsevier's ScienceDirect OnSite in Taiwan
- Leveraging user libraries to bootstrap collaborative filtering
- SLIM: Sparse Linear Methods for Top-N Recommender Systems
- A study of factors that affect the information-seeking behavior of academic scientists
- Recommending citations for academic papers
- Analysis of recommendation algorithms for e-commerce
- Citation recommendation without author supervision
- Recommending academic papers via users' reading purposes
- Training linear SVMs in linear time
- The YouTube video recommendation system
- Item-based collaborative filtering recommendation algorithms
- LorSLIM: Low Rank Sparse Linear Methods for Top-N Recommendations
- Scholarly paper recommendation via user's recent research interests
- User-Specific Feature-Based Similarity Models for Top-n Recommendation of New Items
Cited by
- BoRe
- SoulMate: Short-Text Author Linking Through Multi-Aspect Temporal-Textual Embedding
- A name disambiguation module for intelligent robotic consultant in industrial internet of things
- RecCite: A Hybrid Approach to Recommend Potential Papers
- CRSAL
- Deep learning in citation recommendation models survey
- Improving Session-Based Recommendation Adopting Linear Regression-Based Re-ranking
- A Hybrid Paper Recommendation Method by Using Heterogeneous Graph and Metadata
- AI Marker-based Large-scale AI Literature Mining
- Developing a Prediction Model for Author Collaboration in Bioinformatics Research Using Graph Mining Techniques and Big Data Applications
- An overview and evaluation of citation recommendation models
- A personalized paper recommendation method considering diverse user preferences
- A decision-analytic framework for interpretable recommendation systems with multiple input data sources: a case study for a European e-tailer
- Developing a mathematical model of the co-author recommender system using graph mining techniques and big data applications
- A Comprehensive Survey of Knowledge Graph-Based Recommender Systems: Technologies, Development, and Contributions
- Scaling High-Quality Pairwise Link-Based Similarity Retrieval on Billion-Edge Graphs
- Neural Re-ranking in Multi-stage Recommender Systems: A Review
- A deep learning approach for context-aware citation recommendation using rhetorical zone classification and similarity to overcome cold-start problem
- A Systematic Literature Review on the Hybrid Approaches for Recommender Systems
- Scholarly knowledge graphs through structuring scholarly communication: a review
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