AI 中文总结
本研究提出MIJSR框架，从结构和语义视角对搜索-推荐混合序列进行多兴趣挖掘与自适应整合，经实验可提升搜索和推荐的准确率。
AI 中文摘要
搜索和推荐是理解用户偏好的关键，越来越多研究尝试通过整合用户主动搜索与被动推荐行为数据来联合建模搜索和推荐行为，以更好挖掘用户偏好。然而，现有跨域统一建模框架虽能有效弥补域间行为差异，却忽略了混合序列中不同场景下的兴趣表达。本研究提出一种基于多兴趣的混合序列建模框架MIJSR，从结构和语义视角对搜索-推荐混合序列进行多兴趣挖掘与自适应整合。具体而言，该模型大致分为三个模块：跨域行为融合、多兴趣挖掘和多任务预测。首先，通过对比学习训练对齐查询和物品的表示；接着从结构和语义视角提取混合行为序列的多兴趣：结构层面，通过子序列划分和掩码设置提取搜索兴趣、推荐兴趣和交叉兴趣；语义层面，利用查询的语义信息进行聚类，对混合序列进行语义分割以构建语义多兴趣；最后，将多兴趣的自适应融合与其他侧信息结合，采用渐进分层提取模型进行多任务预测。在两个开源数据集上的大量实验表明，本模型通过细粒度提取用户多兴趣，可进一步提升搜索和推荐的准确率。代码可在该https地址获取。
英文摘要
Search and recommendation are crucial for understanding user preferences. More and more studies are attempting to jointly model search behavior and recommendation behavior, by integrating user active search and passive recommendation behavior data to better mine user preferences. However, although existing cross-domain unified modeling frameworks can effectively compensate for the differences in behavior between domains, they overlook the expression of interests in different scenarios under mixed sequences. In this study, we propose a multi-interest-based mixed sequential modeling framework MIJSR, which performs multi-interest mining and adaptive integration on search recommendation mixed sequences from both structural and semantic perspectives. Specifically, our model can be roughly divided into three modules: cross-domain behavior fusion, multi-interest mining, and multi-task prediction. Firstly, we align the representations of query and item through contrastive learning training. Then, we extract the multi interests of the mixed behavior sequence from both structural and semantic perspectives. Structurally, we extract search interests, recommendation interests, and cross interests through subsequence partitioning and mask settings; In terms of semantics, we use the semantic information of queries for clustering and perform semantic segmentation on mixed sequences to construct semantic multi interests. Finally, the adaptive fusion of multiple interests is combined with other side information to use a progressive layered extraction model for multi-task prediction. Extensive experiments on two open-source datasets have shown that our model can further enhance its accuracy in search and recommendation by extracting users' multi interests at a fine-grained level. Codes are available at https://github.com/pxcstart/MIJSR.
