针对旅行推荐中的重复景点问题，提出抗重复模型AR-Trip
Analyzing and Mitigating Repetitions in Trip Recommendation
- 设计循环感知预测器，动态避免景点重复
- 在4个数据集上提升精度并显著减少重复
- 适合需要高质量行程规划的推荐系统开发者
旅行推荐在过去十年中成为备受关注的服务。尽管现有研究已深入理解人类意图的一致性，但仍面临难以解决的重复推荐问题。通过统计分析和实验设计，我们发现：(1) 重复现象与模型及解码策略密切相关；(2) 在训练与解码过程中对logits加入扰动可降低重复率。基于此，我们提出AR-Trip（Trip Recommendation反重复机制），包含三个循环感知机制，有效避免重复景点推荐。在四个公开数据集上的实验表明，AR-Trip不仅能显著缓解重复问题，还提升了推荐精度。
原文摘要 · Abstract (English)
Trip recommendation has emerged as a highly sought-after service over the past decade. Although current studies significantly understand human intention consistency, they struggle with undesired repetitive outcomes that need resolution. We make two pivotal discoveries using statistical analyses and experimental designs: (1) The occurrence of repetitions is intricately linked to the models and decoding strategies. (2) During training and decoding, adding perturbations to logits can reduce repetition. Motivated by these observations, we introduce AR-Trip (Anti Repetition for Trip Recommendation), which incorporates a cycle-aware predictor comprising three mechanisms to avoid duplicate Points-of-Interest (POIs) and demonstrates their effectiveness in alleviating repetition. Experiments on four public datasets illustrate that AR-Trip successfully mitigates repetition issues while enhancing precision.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。