论文标题
学习一个基于评论的细粒度变压器模型，用于个性化产品搜索
Learning a Fine-Grained Review-based Transformer Model for Personalized Product Search
论文作者
论文摘要
产品搜索一直是为人们在线购物的关键切入点。大多数现有的个性化产品模型都遵循表示和匹配用户意图和在语义领域中的范式，在语义空间中，较细粒度的匹配被完全丢弃，并且不仅仅是用户/项目级别的相似性，就无法进一步解释项目的排名。此外，尽管现有研究中的某些模型基于搜索上下文创建了动态用户表示，但其项目的表示形式在所有搜索课程中都是静态的。这使得有关该项目的每条信息在与各种用户意图匹配时表示该项目始终同样重要。意识到上述限制，我们为个性化产品搜索提出了一个基于审核的变压器模型（RTM），该模型使用变压器体系结构编码查询，用户评论和项目评论的顺序。 RTM在用户和项目之间进行审核级匹配，在该序列中，每个评论都会根据上下文具有动态效果。这使您可以识别有用的评论来解释评分。实验结果表明，RTM明显胜过最先进的个性化产品搜索基线。
Product search has been a crucial entry point to serve people shopping online. Most existing personalized product models follow the paradigm of representing and matching user intents and items in the semantic space, where finer-grained matching is totally discarded and the ranking of an item cannot be explained further than just user/item level similarity. In addition, while some models in existing studies have created dynamic user representations based on search context, their representations for items are static across all search sessions. This makes every piece of information about the item always equally important in representing the item during matching with various user intents. Aware of the above limitations, we propose a review-based transformer model (RTM) for personalized product search, which encodes the sequence of query, user reviews, and item reviews with a transformer architecture. RTM conducts review-level matching between the user and item, where each review has a dynamic effect according to the context in the sequence. This makes it possible to identify useful reviews to explain the scoring. Experimental results show that RTM significantly outperforms state-of-the-art personalized product search baselines.
