# Empirical Analysis of Predictive Algorithms for Collaborative Filtering

arXiv: https://arxiv.org/abs/1301.7363 · HTML: https://mlsanity.com/paper/1301.7363/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2013.

## Built on by (most cited first, 30 of 47)

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- [Artificial Intelligence and Life in 2030: The One Hundred Year Study on Artificial Intelligence](https://mlsanity.com/paper/2211.06318/) (2022)
- [A Survey of Generative Search and Recommendation in the Era of Large Language Models](https://mlsanity.com/paper/2404.16924/) (2024)
- [The Unfairness of Popularity Bias in Book Recommendation](https://mlsanity.com/paper/2202.13446/) (2022)
- [Distilling the Knowledge of Romanian BERTs Using Multiple Teachers](https://mlsanity.com/paper/2112.12650/) (2021)
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- [Trust your neighbors: A comprehensive survey of neighborhood-based methods for recommender systems](https://mlsanity.com/paper/2109.04584/) (2021)
- [Recommender systems based on graph embedding techniques: A comprehensive review](https://mlsanity.com/paper/2109.09587/) (2021)
- [Online Learning in a Creator Economy](https://mlsanity.com/paper/2305.11381/) (2023)
- [TPMCF: Temporal QoS Prediction using Multi-Source Collaborative Features](https://mlsanity.com/paper/2303.18201/) (2023)
- [Recommender Systems for Online and Mobile Social Networks: A survey](https://mlsanity.com/paper/2307.01207/) (2023)
- [Unmasking Gender Bias in Recommendation Systems and Enhancing Category-Aware Fairness](https://mlsanity.com/paper/2502.17921/) (2025)
- [GR-LLMs: Recent Advances in Generative Recommendation Based on Large Language Models](https://mlsanity.com/paper/2507.06507/) (2025)
- [Improving Conversational Recommender System via Contextual and Time-Aware Modeling with Less Domain-Specific Knowledge](https://mlsanity.com/paper/2209.11386/) (2022)
- [Intent Recognition in Conversational Recommender Systems](https://mlsanity.com/paper/2212.03721/) (2022)
- [Informative Scene Graph Generation via Debiasing](https://mlsanity.com/paper/2308.05286/) (2023)
- [Who You Are Matters: Bridging Topics and Social Roles via LLM-Enhanced Logical Recommendation](https://mlsanity.com/paper/2505.10940/) (2025)
- [Context-Specific Likelihood Weighting](https://mlsanity.com/paper/2101.09791/) (2021)
- [Interrogating the Black Box: Transparency through Information-Seeking Dialogues](https://mlsanity.com/paper/2102.04714/) (2021)
- [ARRQP: Anomaly Resilient Real-time QoS Prediction Framework with Graph Convolution](https://mlsanity.com/paper/2310.02269/) (2023)
- [Accelerating Matrix Factorization by Dynamic Pruning for Fast Recommendation](https://mlsanity.com/paper/2404.04265/) (2024)
- [Handling Heterophily in Recommender Systems with Wavelet Hypergraph Diffusion](https://mlsanity.com/paper/2501.14399/) (2025)
- [FES: A Fast Efficient Scalable QoS Prediction Framework](https://mlsanity.com/paper/2103.07494/) (2021)
- [When and Whom to Collaborate with in a Changing Environment: A Collaborative Dynamic Bandit Solution](https://mlsanity.com/paper/2104.07150/) (2021)
- [A Comprehensive Review on Non-Neural Networks Collaborative Filtering Recommendation Systems](https://mlsanity.com/paper/2106.10679/) (2021)

## Abstract (arXiv)

Collaborative filtering or recommender systems use a database about user preferences to predict additional topics or products a new user might like. In this paper we describe several algorithms designed for this task, including techniques based on correlation coefficients, vector-based similarity calculations, and statistical Bayesian methods. We compare the predictive accuracy of the various methods in a set of representative problem domains. We use two basic classes of evaluation metrics. The first characterizes accuracy over a set of individual predictions in terms of average absolute deviation. The second estimates the utility of a ranked list of suggested items. This metric uses an estimate of the probability that a user will see a recommendation in an ordered list. Experiments were run for datasets associated with 3 application areas, 4 experimental protocols, and the 2 evaluation metrics for the various algorithms. Results indicate that for a wide range of conditions, Bayesian networks with decision trees at each node and correlation methods outperform Bayesian-clustering and vector-similarity methods. Between correlation and Bayesian networks, the preferred method depends on the nature of the dataset, nature of the application (ranked versus one-by-one presentation), and the availability of votes with which to make predictions. Other considerations include the size of database, speed of predictions, and learning time.