Understanding the Effects of Adversarial Personalized Ranking Optimization Method on Recommendation Quality

• URL: http://arxiv.org/abs/2107.13876v1
• Date: Thu, 29 Jul 2021 10:22:20 GMT
• Title: Understanding the Effects of Adversarial Personalized Ranking Optimization Method on Recommendation Quality
• Authors: Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, Felice Antonio Merra
• Abstract summary: We model the learning characteristics of the Bayesian Personalized Ranking (BPR) and APR optimization frameworks. We show that APR amplifies the popularity bias more than BPR due to an unbalanced number of received positive updates from short-head items.
• Score: 6.197934754799158
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recommender systems (RSs) employ user-item feedback, e.g., ratings, to match customers to personalized lists of products. Approaches to top-k recommendation mainly rely on Learning-To-Rank algorithms and, among them, the most widely adopted is Bayesian Personalized Ranking (BPR), which bases on a pair-wise optimization approach. Recently, BPR has been found vulnerable against adversarial perturbations of its model parameters. Adversarial Personalized Ranking (APR) mitigates this issue by robustifying BPR via an adversarial training procedure. The empirical improvements of APR's accuracy performance on BPR have led to its wide use in several recommender models. However, a key overlooked aspect has been the beyond-accuracy performance of APR, i.e., novelty, coverage, and amplification of popularity bias, considering that recent results suggest that BPR, the building block of APR, is sensitive to the intensification of biases and reduction of recommendation novelty. In this work, we model the learning characteristics of the BPR and APR optimization frameworks to give mathematical evidence that, when the feedback data have a tailed distribution, APR amplifies the popularity bias more than BPR due to an unbalanced number of received positive updates from short-head items. Using matrix factorization (MF), we empirically validate the theoretical results by performing preliminary experiments on two public datasets to compare BPR-MF and APR-MF performance on accuracy and beyond-accuracy metrics. The experimental results consistently show the degradation of novelty and coverage measures and a worrying amplification of bias.

Related papers

• Uncertainty-Penalized Direct Preference Optimization [52.387088396044206]
  We develop a pessimistic framework for DPO by introducing preference uncertainty penalization schemes. The penalization serves as a correction to the loss which attenuates the loss gradient for uncertain samples. We show improved overall performance compared to vanilla DPO, as well as better completions on prompts from high-uncertainty chosen/rejected responses.
  arXiv  Detail & Related papers  (2024-10-26T14:24:37Z)
• Preference Diffusion for Recommendation [50.8692409346126]
  We propose PreferDiff, a tailored optimization objective for DM-based recommenders. PreferDiff transforms BPR into a log-likelihood ranking objective to better capture user preferences. It is the first personalized ranking loss designed specifically for DM-based recommenders.
  arXiv  Detail & Related papers  (2024-10-17T01:02:04Z)
• Reward-Augmented Data Enhances Direct Preference Alignment of LLMs [63.32585910975191]
  We introduce reward-conditioned Large Language Models (LLMs) that learn from the entire spectrum of response quality within the dataset. We propose an effective yet simple data relabeling method that conditions the preference pairs on quality scores to construct a reward-augmented dataset.
  arXiv  Detail & Related papers  (2024-10-10T16:01:51Z)
• Bridging and Modeling Correlations in Pairwise Data for Direct Preference Optimization [75.1240295759264]
  We propose an effective framework for Bridging and Modeling Correlations in pairwise data, named BMC. We increase the consistency and informativeness of the pairwise preference signals through targeted modifications. We identify that DPO alone is insufficient to model these correlations and capture nuanced variations.
  arXiv  Detail & Related papers  (2024-08-14T11:29:47Z)
• Gradient-Boosted Decision Tree for Listwise Context Model in Multimodal Review Helpfulness Prediction [40.09991896766369]
  Multimodal Review Helpfulness Prediction aims to rank product reviews based on predicted helpfulness scores. We propose a listwise attention network that clearly captures the MRHP ranking context. We also propose gradient-boosted decision tree as the score predictor to efficaciously partition product reviews' representations.
  arXiv  Detail & Related papers  (2023-05-22T03:31:00Z)
• Improving Recommendation Fairness via Data Augmentation [66.4071365614835]
  Collaborative filtering based recommendation learns users' preferences from all users' historical behavior data, and has been popular to facilitate decision making. A recommender system is considered unfair when it does not perform equally well for different user groups according to users' sensitive attributes. In this paper, we study how to improve recommendation fairness from the data augmentation perspective.
  arXiv  Detail & Related papers  (2023-02-13T13:11:46Z)
• Item-based Variational Auto-encoder for Fair Music Recommendation [1.8782288713227568]
  The EvalRS DataChallenge aims to build a more realistic recommender system considering accuracy, fairness, and diversity in evaluation. Our proposed system is based on an ensemble between an item-based variational auto-encoder (VAE) and a Bayesian personalized ranking matrix factorization (BPRMF)
  arXiv  Detail & Related papers  (2022-10-24T06:42:16Z)
• Debiasing Neural Retrieval via In-batch Balancing Regularization [25.941718123899356]
  We develop a differentiable textitnormed Pairwise Ranking Fairness (nPRF) and leverage the T-statistics on top of nPRF to improve fairness. Our method with nPRF achieves significantly less bias with minimal degradation in ranking performance compared with the baseline.
  arXiv  Detail & Related papers  (2022-05-18T22:57:15Z)
• Cross Pairwise Ranking for Unbiased Item Recommendation [57.71258289870123]
  We develop a new learning paradigm named Cross Pairwise Ranking (CPR) CPR achieves unbiased recommendation without knowing the exposure mechanism. We prove in theory that this way offsets the influence of user/item propensity on the learning.
  arXiv  Detail & Related papers  (2022-04-26T09:20:27Z)
• Debiased Explainable Pairwise Ranking from Implicit Feedback [0.3867363075280543]
  We focus on the state of the art pairwise ranking model, Bayesian Personalized Ranking (BPR) BPR is a black box model that does not explain its outputs, thus limiting the user's trust in the recommendations. We propose a novel explainable loss function and a corresponding Matrix Factorization-based model that generates recommendations along with item-based explanations.
  arXiv  Detail & Related papers  (2021-07-30T17:19:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.