论文标题
通过伪标签上的知识图来减轻推荐中的冷启动问题
Alleviating Cold-Start Problems in Recommendation through Pseudo-Labelling over Knowledge Graph
论文作者
论文摘要
解决冷启动问题是必不可少的，即可为新用户和项目提供有意义的建议结果。在稀疏观察到的数据下，未观察到的用户项目对也是提取潜在用户信息需求的重要来源。当前的大多数作品都利用未观察到的样本来提取负信号。但是，这样的优化策略可以通过经常将新项目作为负面实例来导致对已经流行项目的偏见结果。在这项研究中，我们通过适当利用未观察到的样本来解决新用户/项目的冷启动问题。我们根据图神经网络提出了一个知识图（KG） - 注意推荐人，该网络通过伪标签增强了标记样品的标记。我们的方法积极地采用了未观察到的样本作为积极的实例，并将新项目带入了人们的焦点。为了避免对所有可能的用户和项目对详尽的标签分配，我们利用了一个kg来为每个用户选择可能的积极项目。我们还利用了改进的负面抽样策略，从而抑制了流行偏见的加剧。通过实验，我们证明了我们的方法在各种情况下都对最先进的KG意见推荐人进行了改进。特别是，我们的方法论成功地改善了冷启动用户/项目的建议性能。
Solving cold-start problems is indispensable to provide meaningful recommendation results for new users and items. Under sparsely observed data, unobserved user-item pairs are also a vital source for distilling latent users' information needs. Most present works leverage unobserved samples for extracting negative signals. However, such an optimisation strategy can lead to biased results toward already popular items by frequently handling new items as negative instances. In this study, we tackle the cold-start problems for new users/items by appropriately leveraging unobserved samples. We propose a knowledge graph (KG)-aware recommender based on graph neural networks, which augments labelled samples through pseudo-labelling. Our approach aggressively employs unobserved samples as positive instances and brings new items into the spotlight. To avoid exhaustive label assignments to all possible pairs of users and items, we exploit a KG for selecting probably positive items for each user. We also utilise an improved negative sampling strategy and thereby suppress the exacerbation of popularity biases. Through experiments, we demonstrate that our approach achieves improvements over the state-of-the-art KG-aware recommenders in a variety of scenarios; in particular, our methodology successfully improves recommendation performance for cold-start users/items.
