Criteria-Aware Graph Filtering: Extremely Fast Yet Accurate Multi-Criteria Recommendation

• URL: http://arxiv.org/abs/2502.09046v1
• Date: Thu, 13 Feb 2025 08:01:38 GMT
• Title: Criteria-Aware Graph Filtering: Extremely Fast Yet Accurate Multi-Criteria Recommendation
• Authors: Jin-Duk Park, Jaemin Yoo, Won-Yong Shin,
• Abstract summary: Multi-criteria (MC) recommender systems are increasingly widespread in various e-commerce domains. However, the MC recommendation using training-based collaborative filtering, requires consideration of multiple ratings compared to single-criterion counterparts. We propose CA-GF, a training-free MC recommendation method built upon criteria-aware graph for efficient yet accurate MC recommendations.
• Score: 20.740346109417143
• License:
• Abstract: Multi-criteria (MC) recommender systems, which utilize MC rating information for recommendation, are increasingly widespread in various e-commerce domains. However, the MC recommendation using training-based collaborative filtering, requiring consideration of multiple ratings compared to single-criterion counterparts, often poses practical challenges in achieving state-of-the-art performance along with scalable model training. To solve this problem, we propose CA-GF, a training-free MC recommendation method, which is built upon criteria-aware graph filtering for efficient yet accurate MC recommendations. Specifically, first, we construct an item-item similarity graph using an MC user-expansion graph. Next, we design CA-GF composed of the following key components, including 1) criterion-specific graph filtering where the optimal filter for each criterion is found using various types of polynomial low-pass filters and 2) criteria preference-infused aggregation where the smoothed signals from each criterion are aggregated. We demonstrate that CA-GF is (a) efficient: providing the computational efficiency, offering the extremely fast runtime of less than 0.2 seconds even on the largest benchmark dataset, (b) accurate: outperforming benchmark MC recommendation methods, achieving substantial accuracy gains up to 24% compared to the best competitor, and (c) interpretable: providing interpretations for the contribution of each criterion to the model prediction based on visualizations.

Related papers

• An incremental preference elicitation-based approach to learning potentially non-monotonic preferences in multi-criteria sorting [53.36437745983783]
  We first construct a max-margin optimization-based model to model potentially non-monotonic preferences. We devise information amount measurement methods and question selection strategies to pinpoint the most informative alternative in each iteration. Two incremental preference elicitation-based algorithms are developed to learn potentially non-monotonic preferences.
  arXiv  Detail & Related papers  (2024-09-04T14:36:20Z)
• Contextual Distillation Model for Diversified Recommendation [19.136439564988834]
  Contextual Distillation Model (CDM) is an efficient recommendation model that addresses diversification. We propose a contrastive context encoder that employs attention mechanisms to model both positive and negative contexts. During inference, ranking is performed through a linear combination of the recommendation and student model scores.
  arXiv  Detail & Related papers  (2024-06-13T11:55:40Z)
• Aligning GPTRec with Beyond-Accuracy Goals with Reinforcement Learning [67.71952251641545]
  GPTRec is an alternative to the Top-K model for item-by-item recommendations. We show that GPTRec offers a better tradeoff between accuracy and secondary metrics than classic greedy re-ranking techniques. Our experiments on two datasets show that GPTRec's Next-K generation approach offers a better tradeoff between accuracy and secondary metrics than classic greedy re-ranking techniques.
  arXiv  Detail & Related papers  (2024-03-07T19:47:48Z)
• Towards Open-ended Visual Quality Comparison [87.45004129101089]
  We extend the edge of emerging large multi-modality models (LMMs) to advance visual quality comparison into open-ended settings. Co-Instruct is a first-of-its-kind open-source open-ended visual quality comparer. We demonstrate that Co-Instruct achieves in average 30% higher accuracy than state-of-the-art open-source LMMs.
  arXiv  Detail & Related papers  (2024-02-26T15:10:56Z)
• Frequency-aware Graph Signal Processing for Collaborative Filtering [26.317108637430664]
  We propose a frequency-aware graph signal processing method (FaGSP) for collaborative filtering. Firstly, we design a Cascaded Filter Module, consisting of an ideal high-pass filter and an ideal low-pass filter. Then, we devise a Parallel Filter Module, consisting of two low-pass filters that can easily capture the hierarchy of neighborhood.
  arXiv  Detail & Related papers  (2024-02-13T12:53:18Z)
• How to Prune Your Language Model: Recovering Accuracy on the "Sparsity May Cry'' Benchmark [60.72725673114168]
  We revisit the question of accurate BERT-pruning during fine-tuning on downstream datasets. We propose a set of general guidelines for successful pruning, even on the challenging SMC benchmark.
  arXiv  Detail & Related papers  (2023-12-21T03:11:30Z)
• Criteria Tell You More than Ratings: Criteria Preference-Aware Light Graph Convolution for Effective Multi-Criteria Recommendation [5.536402965666082]
  We make the first attempt towards designing a GNN-aided MC recommender system. Specifically, we devise a novel criteria preference-aware light graph convolution CPA-LGC method. To this end, we first construct an MC expansion graph that transforms user--item MC ratings into an expanded bipartite graph. Next, to strengthen the capability of criteria preference awareness, CPA-LGC incorporates newly characterized embeddings.
  arXiv  Detail & Related papers  (2023-05-30T09:27:36Z)
• Recommendation Systems with Distribution-Free Reliability Guarantees [83.80644194980042]
  We show how to return a set of items rigorously guaranteed to contain mostly good items. Our procedure endows any ranking model with rigorous finite-sample control of the false discovery rate. We evaluate our methods on the Yahoo! Learning to Rank and MSMarco datasets.
  arXiv  Detail & Related papers  (2022-07-04T17:49:25Z)
• Conditional Sequential Slate Optimization [15.10459152219771]
  A search ranking system typically orders the results by independent query-document scores to produce a slate of search results. We introduce conditional sequential slate optimization (CSSO), which jointly learns to optimize for traditional ranking metrics as well as prescribed distribution criteria of documents within the slate. The proposed method can be applied to practical real world problems such as enforcing diversity in e-commerce search results, mitigating bias in top results and personalization of results.
  arXiv  Detail & Related papers  (2021-08-12T09:14:46Z)
• Blending Pruning Criteria for Convolutional Neural Networks [13.259106518678474]
  Recent popular network pruning is an effective method to reduce the redundancy of the models. One filter could be important according to a certain criterion, while it is unnecessary according to another one, which indicates that each criterion is only a partial view of the comprehensive "importance" We propose a novel framework to integrate the existing filter pruning criteria by exploring the criteria diversity.
  arXiv  Detail & Related papers  (2021-07-11T12:34:19Z)
• CRACT: Cascaded Regression-Align-Classification for Robust Visual Tracking [97.84109669027225]
  We introduce an improved proposal refinement module, Cascaded Regression-Align- Classification (CRAC) CRAC yields new state-of-the-art performances on many benchmarks. In experiments on seven benchmarks including OTB-2015, UAV123, NfS, VOT-2018, TrackingNet, GOT-10k and LaSOT, our CRACT exhibits very promising results in comparison with state-of-the-art competitors.
  arXiv  Detail & Related papers  (2020-11-25T02:18:33Z)

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.