Published May 24, 2026
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Multi-Model Content Recommendation: An Ensemble Architecture
Authors/Creators
Description
This paper describes a multi-model recommendation ensemble developed for CNET that combines four fundamentally different recommendation paradigms — topic modeling (LDA), user-user collaborative filtering (ALS), item-item collaborative filtering, and locality-sensitive hashing (LSH-MinHash) — under a meta-orchestration layer. Each model contributes a distinct recommendation signal: semantic content similarity, user behavior similarity, item co-consumption patterns, and efficient set-based similarity. The paper documents the architecture, the individual model designs, the orchestration strategy, and discusses the ensemble approach in the context of modern recommendation systems.
Files
cnet_recommender_ensemble.pdf
Additional details
Related works
- Is supplement to
- Preprint: 10.5281/zenodo.20367208 (DOI)
Dates
- Created
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2014Original ideation & implementation
- Issued
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2026-05-24
References
- Blei, D., Ng, A. and Jordan, M. (2003). Latent Dirichlet allocation. JMLR.
- Hu, Y., Koren, Y. and Volinsky, C. (2008). Collaborative filtering for implicit feedback datasets. ICDM.
- Sarwar, B. et al. (2001). Item-based collaborative filtering recommendation algorithms. WWW.
- Indyk, P. and Motwani, R. (1998). Approximate nearest neighbors: Towards removing the curse of dimensionality. STOC
- Broder, A. (1997). On the resemblance and containment of documents. SEQUENCES.
- Subsequent work: Covington, P., Adams, J. and Sargin, E. (2016). Deep neural networks for YouTube recommendations. RecSys.
- Subsequent work: He, X. et al. (2017). Neural collaborative filtering. WWW.
- Subsequent work: Devlin, J. et al. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. NAACL.
- Subsequent work: Reimers, N. and Gurevych, I. (2019). Sentence-BERT: Sentence embeddings using Siamese BERT-networks. EMNLP.