  Leveraging generative retrieval (GR) techniques to enhance search systems is
an emerging methodology that has shown promising results in recent years. In
GR, a text-to-text model maps string queries directly to relevant document
identifiers (docIDs), so it dramatically simplifies the whole retrieval
process. However, when applying most GR models in large-scale E-commerce for
personalized item search, we have to face two key problems in encoding and
decoding. (1) Existing docID generation methods ignore the encoding of
efficiency information, which is critical in E-commerce. (2) The positional
information is important in decoding docIDs, while prior studies have not
adequately discriminated the significance of positional information or well
exploited the inherent interrelation among these positions. To overcome these
problems, we introduce an efficient Hierarchical encoding-decoding Generative
retrieval method (Hi-Gen) for large-scale personalized E-commerce search
systems. Specifically, we first design a representation learning model along
with metric learning to learn discriminative feature representations of items
to capture both semantic relevance and efficiency information. Then, we propose
a category-guided hierarchical clustering scheme that makes full use of the
semantic and efficiency information of items to facilitate docID generation.
Finally, we design a position-aware loss to discriminate the importance of
positions and mine the inherent interrelation between different tokens at the
same position. This loss boosts the performance of the language model used in
the decoding stage. Besides, we propose two variants of Hi-Gen (i.e.,Hi-Gen-I2I
and Hi-Gen-Cluster) to support online real-time large-scale recall in the
online serving process. Extensive experiments on both public and industry
datasets demonstrate the effectiveness and efficiency of Hi-Gen.
