MISS: Multi-Modal Tree Indexing and Searching with Lifelong Sequential Behavior for Retrieval Recommendation

Chengcheng Guo, Junda She, Kuo Cai, shiyao wang, QigenHu, Qiang Luo, Kun Gai, Guorui Zhou

Abstract:

Large-scale industrial recommendation systems typically employ
a two-stage paradigm of retrieval and ranking to handle huge
amounts of information. Recent research focuses on improving the
performance of retrieval model. A promising way is to introduce
extensive information about users and items. On one hand, lifelong
sequential behavior is valuable. Existing lifelong behavior modeling
methods in ranking stage focus on the interaction of lifelong behavior and candidate items from retrieval stage. In retrieval stage, it is
difficult to utilize lifelong behavior because of a large corpus of candidate items. On the other hand, existing retrieval methods mostly
relay on interaction information, potentially disregarding valuable
multi-modal information. To solve these problems, we represent
the pioneering exploration of leveraging multi-modal information
and lifelong sequence model within the advanced tree-based retrieval model. We propose Multi-modal Indexing and Searching
with lifelong Sequence (MISS), which contains a multi-modal index
tree and a multi-modal lifelong sequence modeling module. Specifically, for better index structure, we propose multi-modal index
tree, which is built using the multi-modal embedding to precisely
represent item similarity. To precisely capture diverse user interests
in user lifelong sequence, we propose collaborative general search
unit (Co-GSU) and multi-modal general search unit (MM-GSU) for
multi-perspective interests searching. Online experiments have
demonstrated the effectiveness of the proposed method