Adversarial Personalized Ranking for Recommendation (1808.03908v1)

Abstract: Item recommendation is a personalized ranking task. To this end, many recommender systems optimize models with pairwise ranking objectives, such as the Bayesian Personalized Ranking (BPR). Using matrix Factorization (MF) --- the most widely used model in recommendation --- as a demonstration, we show that optimizing it with BPR leads to a recommender model that is not robust. In particular, we find that the resultant model is highly vulnerable to adversarial perturbations on its model parameters, which implies the possibly large error in generalization. To enhance the robustness of a recommender model and thus improve its generalization performance, we propose a new optimization framework, namely Adversarial Personalized Ranking (APR). In short, our APR enhances the pairwise ranking method BPR by performing adversarial training. It can be interpreted as playing a minimax game, where the minimization of the BPR objective function meanwhile defends an adversary, which adds adversarial perturbations on model parameters to maximize the BPR objective function. To illustrate how it works, we implement APR on MF by adding adversarial perturbations on the embedding vectors of users and items. Extensive experiments on three public real-world datasets demonstrate the effectiveness of APR --- by optimizing MF with APR, it outperforms BPR with a relative improvement of 11.2% on average and achieves state-of-the-art performance for item recommendation. Our implementation is available at: https://github.com/hexiangnan/adversarial_personalized_ranking.

Insights into Adversarial Personalized Ranking for Recommendation

In exploring the robustness of recommender systems, the paper "Adversarial Personalized Ranking for Recommendation" presents a novel approach to enhance personalization models using adversarial learning. The fundamental concern addressed in this research is the vulnerability of traditional pairwise ranking algorithms, such as Bayesian Personalized Ranking (BPR), to adversarial perturbations. This aligns with broader findings in adversarial machine learning that have historically focused on domains like image classification.

Core Contributions

The main contribution of this paper is the introduction of Adversarial Personalized Ranking (APR), a framework that bolsters the robust learning of recommendation models. The authors argue that APR takes matrix factorization (MF) — a cornerstone method in recommendation system modeling — and optimizes it further using adversarial training. The integration involves a minimax game structure where a model is trained to perform well not only on available data but also against an adversarial model that attempts to degrade performance through crafted perturbations. This dual process aligns with a strategic adversarial training paradigm, intended to yield models that generalize better over unseen data.

Key Experimental Findings

The experimental validation involves extensive testing on three well-known real-world datasets: Yelp, Pinterest, and Gowalla. These datasets underpin testing across varying recommendation contexts, from business ratings to multimedia content and social graph interactions, respectively.

Remarkably, when APR is applied to MF (resulting in Adversarial Matrix Factorization, or AMF), the benchmarks show a relative improvement of 11.2% on average over the baseline BPR model for NDCG and hit ratio. This improvement is noteworthy as it outstrips state-of-the-art models like CDAE, NeuMF, and IRGAN in these recommendation tasks. Such enhancement indicates an advancement in handling adversarial vulnerabilities, thus supporting better generalization and robustness in practical deployment.

Technical Implications

The theoretical basis of APR rests on adversarial regularization — an elegantly integrated loss metric that stabilizes BPR-functions against adversarial attacks. This regulation increases the robustness of the recommendation model's predictive capabilities by reducing sensitivity to perturbations in its parameters. Practically, this translates to improvements in the quality of ranking results, addressing common pitfalls in overfitting that traditional non-adversarial models encounter.

Future Directions

The methodology and insights proposed in this paper catalyze several future research avenues:

1. Broader Model Applicability: Extending APR beyond matrix factorization is a natural progression. Deep learning models that integrate user interactions (e.g., NeuMF or additional NLP-based frameworks) can potentially benefit from similar adversarial training methods.
2. Adaptation to Different Tasks: While APR has been validated in the domain of item recommendation, its application potential may extend to other ranking-focused domains such as information retrieval, search engine optimization, and even complex tasks like question answering or chatbot response ranking.
3. Dynamic Adversarial Perturbation Schemes: Adjusting the adversarial strength (controlled by parameters such as ε and λ in APR) dynamically based on model state and validation performance could fine-tune the robustness further, offering nuanced control over model training efficacy.

Conclusion

This paper importantly broadens the scope of adversarial training in recommendation systems, providing refined strategies for enhancing model robustness. The significant enhancements in ranking metrics confirm that APR is an efficacious approach in mitigating adversarial vulnerabilities. As the recommendation landscape becomes more intricate with increased data complexities, such methodologies offer promising paths to more resilient and high-performing systems.