Learning k-Determinantal Point Processes for Personalized Ranking

• URL: http://arxiv.org/abs/2406.15983v1
• Date: Sun, 23 Jun 2024 02:24:50 GMT
• Title: Learning k-Determinantal Point Processes for Personalized Ranking
• Authors: Yuli Liu, Christian Walder, Lexing Xie,
• Abstract summary: We present a new optimization criterion LkP based on set probability comparison for personalized ranking. LkP is broadly applicable, and when applied to existing recommendation models it also yields strong performance improvements.
• Score: 13.677246792673564
• License:
• Abstract: The key to personalized recommendation is to predict a personalized ranking on a catalog of items by modeling the user's preferences. There are many personalized ranking approaches for item recommendation from implicit feedback like Bayesian Personalized Ranking (BPR) and listwise ranking. Despite these methods have shown performance benefits, there are still limitations affecting recommendation performance. First, none of them directly optimize ranking of sets, causing inadequate exploitation of correlations among multiple items. Second, the diversity aspect of recommendations is insufficiently addressed compared to relevance. In this work, we present a new optimization criterion LkP based on set probability comparison for personalized ranking that moves beyond traditional ranking-based methods. It formalizes set-level relevance and diversity ranking comparisons through a Determinantal Point Process (DPP) kernel decomposition. To confer ranking interpretability to the DPP set probabilities and prioritize the practicality of LkP, we condition the standard DPP on the cardinality k of the DPP-distributed set, known as k-DPP, a less-explored extension of DPP. The generic stochastic gradient descent based technique can be directly applied to optimizing models that employ LkP. We implement LkP in the context of both Matrix Factorization (MF) and neural networks approaches, on three real-world datasets, obtaining improved relevance and diversity performances. LkP is broadly applicable, and when applied to existing recommendation models it also yields strong performance improvements, suggesting that LkP holds significant value to the field of recommender systems.

Related papers

• Federated Fine-Tuning of Large Language Models: Kahneman-Tversky vs. Direct Preference Optimization [49.88778604259453]
  We evaluate Kahneman-Tversky Optimization (KTO) as a fine-tuning method for large language models (LLMs) in federated learning (FL) settings. In both its original (KTOO) and redistributed (KTOR) configurations, KTO consistently outperforms DPO across all benchmarks. These findings establish KTO as a robust and scalable fine-tuning method for FL, motivating its adoption for privacy-preserving, decentralized, and heterogeneous environments.
  arXiv  Detail & Related papers  (2025-02-20T01:44:21Z)
• Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment [19.02679077706812]
  We study the problem of aligning large language models with human preference data. We propose direct preference optimization (Cal-DPO), a simple yet effective algorithm. The results of our experiments on a variety of standard benchmarks show that Cal-DPO remarkably improves off-the-shelf methods.
  arXiv  Detail & Related papers  (2024-12-19T04:31:56Z)
• MPPO: Multi Pair-wise Preference Optimization for LLMs with Arbitrary Negative Samples [22.521746860874305]
  This study introduces the MPPO algorithm, which leverages the average likelihood of model responses to fit the reward function. Through a comparison of Point-wise, Pair-wise, and List-wise implementations, we found that the Pair-wise approach achieves the best performance. Experimental results demonstrate MPPO's outstanding performance across various benchmarks.
  arXiv  Detail & Related papers  (2024-12-13T14:18:58Z)
• 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)
• Ordinal Preference Optimization: Aligning Human Preferences via NDCG [28.745322441961438]
  We develop an end-to-end preference optimization algorithm by approxing NDCG with a differentiable surrogate loss. OPO outperforms existing pairwise and listwise approaches on evaluation sets and general benchmarks like AlpacaEval.
  arXiv  Detail & Related papers  (2024-10-06T03:49:28Z)
• 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)
• On Softmax Direct Preference Optimization for Recommendation [50.896117978746]
  We propose Softmax-DPO (S-DPO) to instill ranking information into the LM to help LM-based recommenders distinguish preferred items from negatives. Specifically, we incorporate multiple negatives in user preference data and devise an alternative version of DPO loss tailored for LM-based recommenders.
  arXiv  Detail & Related papers  (2024-06-13T15:16:11Z)
• Comparing Bad Apples to Good Oranges: Aligning Large Language Models via Joint Preference Optimization [105.3612692153615]
  We propose a new axis based on eliciting preferences jointly over instruction-response pairs. Joint preferences over instruction and response pairs can significantly enhance the alignment of large language models.
  arXiv  Detail & Related papers  (2024-03-31T02:05:40Z)
• Probabilistic Permutation Graph Search: Black-Box Optimization for Fairness in Ranking [53.94413894017409]
  We present a novel way of representing permutation distributions, based on the notion of permutation graphs. Similar to PL, our distribution representation, called PPG, can be used for black-box optimization of fairness.
  arXiv  Detail & Related papers  (2022-04-28T20:38:34Z)
• Determinantal Point Process Likelihoods for Sequential Recommendation [12.206748373325972]
  We propose two new loss functions based on the Determinantal Point Process (DPP) likelihood, that can be adaptively applied to estimate the subsequent item or items. Experimental results using the proposed loss functions on three real-world datasets show marked improvements over state-of-the-art sequential recommendation methods in both quality and diversity metrics.
  arXiv  Detail & Related papers  (2022-04-25T11:20:10Z)

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.