Abstract
Many platforms, such as e-commerce websites, offer both search and recommendation services simultaneously to better meet users' diverse needs. Recommendation services suggest items based on user preferences, while search services allow users to search for items before providing recommendations. Since users and items are often shared between the search and recommendation domains, there is a valuable opportunity to enhance the recommendation domain by leveraging user preferences extracted from the search domain. Existing approaches either overlook the shift in user intention between these domains or fail to capture the significant impact of learning from users' search queries on understanding their interests. In this paper, we propose a framework that learns from user search query embeddings within the context of user preferences in the recommendation domain. Specifically, user search query sequences from the search domain are used to predict the items users will click at the next time point in the recommendation domain. Additionally, the relationship between queries and items is explored through contrastive learning. To address issues of data sparsity, the diffusion model is incorporated to infer positive items the user will select after searching with certain queries in a denoising manner, which is particularly effective in preventing false positives. Effectively extracting this information, the queries are integrated into click-through rate prediction in the recommendation domain. Experimental analysis demonstrates that our model outperforms state-of-the-art models in the recommendation domain.
Abstract (translated)
许多平台，例如电子商务网站，同时提供搜索和推荐服务以更好地满足用户多样的需求。推荐服务根据用户的偏好提出商品建议，而搜索服务允许用户在提供建议之前搜索商品。由于用户和商品经常在搜索域和推荐域之间共享，因此有很好的机会通过利用从搜索域提取的用户偏好来增强推荐领域。现有方法要么忽略了这些领域之间的用户意图变化，要么未能捕捉到学习用户的搜索查询对理解其兴趣的重要影响。在这篇论文中，我们提出了一种框架，该框架在推荐领域的用户偏好的背景下学习用户的搜索查询嵌入。具体来说，从搜索域收集的用户搜索查询序列用于预测用户在未来时间点将在推荐领域点击的商品。此外，通过对比学习探索了查询和商品之间的关系。为了解决数据稀疏性问题，引入扩散模型以去噪的方式推断出用户在使用特定查询进行搜索后将选择的正向商品，这尤其有效地防止了假阳性情况的发生。有效提取这些信息后，查询被整合进推荐领域的点击率预测中。实验分析表明，我们的模型在推荐领域优于最先进的模型。
URL
https://arxiv.org/abs/2410.21487
