Application of Dimensionality Reduction in Recommender System - A Case Study
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Summary
This paper presents two different experiments where one technology called Singular Value Decomposition (SVD) is explored to reduce the dimensionality of recommender system databases and suggests that SVD has the potential to meet many of the challenges ofRecommender systems, under certain conditions.
- Type
- report
- Published
- 2000-07-14
- Cited by
- 1,574
- References
- 33
- Access
- Open access
- OpenAlex
- https://openalex.org/W1832221731
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:17065558
Keywords
Recommender system, Dimensionality reduction, Reduction (mathematics), Computer science, Artificial intelligence
References
- Direct Marketing Response Models Using Genetic Algorithms
- Data Mining for Direct Marketing: Problems and Solutions
- Applying Knowledge from KDD to Recommender Systems
- Combining Collaborative Filtering with Personal Agents for Better Recommendations
- Bayesian Networks for Knowledge Discovery
- An open architecture for collaborative filtering of netnews
- Learning Collaborative Information Filters
- Using collaborative filtering to weave an information tapestry
- A re-examination of text categorization methods
- Using filtering agents to improve prediction quality in the GroupLens research collaborative filtering system
- Recommending and evaluating choices in a virtual community of use
- Using Linear Algebra for Intelligent Information Retrieval
- Mining business databases
- Social information filtering: algorithms for automating “word of mouth”
- Construction and Comparison of Two Receiver Operating Characteristic Curves Derived from the Same Samples
- An algorithmic framework for performing collaborative filtering
- GroupLens
- Indexing by Latent Semantic Analysis
- Knowledge = concepts: a harmful equation
- Direct marketing response models using genetic algorithms
Cited by
- Apprentissage automatique pour l'extraction de caractéristiques : application au partitionnement de documents, au résumé automatique et au filtrage collaboratif
- Scalable optimization-based feature selection with application to recommender systems
- Collaborative filtering recommender systems based on popular tags
- Deriving Private Information from Randomly Perturbed Ratings
- Introduction on health recommender systems.
- Generating Personalized Recommendations in a Model-Based Product Configurator System
- A random-walk based scoring algorithm with application to recommender systems for large-scale e-commerce
- An improved memory-based collaborative filtering method based on the TOPSIS technique
- Graphical Models and Overlay Networks for Reasoning about Large Distributed Systems
- State-of-the-Art-Betrachtung sensorischer B2C-Empfehlungsfunktionalität im stationären Einzelhandel
- Alleviating the Sparsity Problem in Collaborative Filtering by Using an Adapted Distance and a Graph-Based Method
- A Survey of Attack-Resistant Collaborative Filtering Algorithms
- Low dimensional approximations: problems and algorithms
- Combining Algorithms and User Experience: A Hybrid Personalized Movie Recommender Based on Perceived Similarity
- Factorisation en matrices non négatives pour le filtrage collaboratif
- Design of Web Recommendation Service Based on Consumer`s Sensibility
- Supporting People in Finding Information: Hybrid Recommender Systems and Goal-Based Structuring
- Nonparametric Relational Learning for Social Network Analysis
- Data Analysis, Machine Learning, and Applications
- Building Switching Hybrid Recommender System Using Machine Learning Classifiers and Collaborative Filtering
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