Leveraging user libraries to bootstrap collaborative filtering
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Summary
A novel graphical model, the collaborative score topic model (CSTM), for personal recommendations of textual documents, which performs well in a wide variety of data regimes, smoothly combining the side information with observed ratings as the number of ratings available for a given user ranges from none to many.
- Type
- article
- Published
- 2014-08-24
- Cited by
- 12
- References
- 21
- OpenAlex
- https://openalex.org/W1971599738
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:14093940
Keywords
Computer science, Novelty, Variety (cybernetics), Collaborative filtering, Probabilistic logic
References
- An Introduction to Variational Methods for Graphical Models
- Latent Dirichlet Allocation
- Variational inference in nonconjugate models
- Active set algorithms for isotonic regression; A unifying framework
- Recommending scientific articles using bi-relational graph-based iterative RWR
- Regression-based latent factor models
- Lessons from the Netflix prize challenge
- Bayesian probabilistic matrix factorization using Markov chain Monte Carlo
- Replicated Softmax: an Undirected Topic Model
- Relational learning via collective matrix factorization
- A correlated topic model of Science
- fLDA: matrix factorization through latent dirichlet allocation
- Collaborative topic modeling for recommending scientific articles
- Probabilistic Matrix Factorization
- Generalized Probabilistic Matrix Factorizations for Collaborative Filtering
- Unsupervised Organization of Image Collections: Taxonomies and Beyond
- Expertise modeling for matching papers with reviewers
- Stochastic variational inference
- Visualizing Data using t-SNE
- IR evaluation methods for retrieving highly relevant documents
Cited by
- Content Driven User Profiling for Comment-Worthy Recommendations of News and Blog Articles
- Mutual benefit aware task assignment in a bipartite labor market
- A Meta-Learning Perspective on Cold-Start Recommendations for Items
- Health Forum Thread Recommendation Using an Interest Aware Topic Model
- Mining information interaction behavior : Academic papers and enterprise emails
- Personalised Reranking of Paper Recommendations Using Paper Content and User Behavior
- Improving Collaborative Filtering with Social Influence over Heterogeneous Information Networks
- Hotspot Information Network and Domain Knowledge Graph Aggregation in Heterogeneous Network for Literature Recommendation
- Leveraging Machine Learning for Personalized Recommendations in Mobile Tourism: A Study on Collaborative and Content-Based Filtering
- Heterogeneous graph neural network with hierarchical attention for group-aware paper recommendation in scientific social networks
- MIARec: Mutual-influence-aware Heterogeneous Network Embedding for Scientific Paper Recommendation
- I ’ ll know it when I see it : Toward cognitively plausible recommendations
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