PPNW: personalized pairwise novelty loss weighting for novel recommendation
PPNW: personalized pairwise novelty loss weighting for novel recommendation
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DOI：
10.1007/s10115-021-01546-8
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发表时间：
2021-02
影响因子：
2.7
通讯作者：
Kachun Lo;Tsukasa Ishigaki
中科院分区：
文献类型：
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作者：
Kachun Lo;Tsukasa Ishigaki
推荐系统的大部分工作都集中在为用户提供高度准确的项目预测上，这是基于准确的建议可以最好地满足用户的假设。然而，以准确性为中心的模型也会对流行商品产生很大的系统偏见，因此，不受欢迎的商品很少得到推荐，并将永远保持“冷商品”的状态。在这种情况下，用户和物品提供者都会受到影响。为了提高项目的新颖性，对系统的鲁棒性和多样性起着至关重要的作用，以往的研究主要集中在对基于精度的基础模型生成的top-N列表进行重新排序。因此，重新排序算法完全独立于基本模型。最终，这些框架在本质上受到基础模型的限制，并且分离的两个阶段在提供新颖建议时导致更大的复杂性和效率低下。本文针对BPR损失函数提出了一种个性化的两两新颖性加权框架，该框架覆盖了BPR的局限性，在精度可以忽略不计的情况下有效地提高了新颖性。基础模型将在新颖性感知损失权重的指导下，学习用户偏好，并在1个阶段生成新的top-N列表。在3个公开数据集上的综合实验表明，我们的方法有效地提高了新颖性，并且几乎没有降低准确率。
Most works of recommender systems focus on providing users with highly accurate item predictions based on the assumption that accurate suggestions can best satisfy users. However, accuracy-focused models also create great system bias towards popular items and, as a result, unpopular items rarely get recommended and will stay as “cold items” forever. Both users and item providers will suffer in such scenario. To promote item novelty, which plays a crucial role in system robustness and diversity, previous studies focus mainly on re-ranking a top-N list generated by an accuracy-focused base model. The re-ranking algorithm is thus completely independent of the base model. Eventually, these frameworks are essentially limited by the base model and the separated 2 stages cause greater complication and inefficiency in providing novel suggestions. In this work, we propose a personalized pairwise novelty weighting framework for BPR loss function, which covers the limitations of BPR and effectively improves novelty with negligible decrease in accuracy. Base model will be guided by the novelty-aware loss weights to learn user preference and to generate novel top-N list in only 1 stage. Comprehensive experiments on 3 public datasets show that our approach effectively promotes novelty with almost no decrease in accuracy.