Effective Two-Stage Knowledge Transfer for Multi-Entity Cross-Domain
  Recommendation
Jianyu Guan Zongming Yin Tianyi Zhang Leihui Chen Yin Zhang Fei Huang Jufeng Chen Shuguang Han
In recent years, the recommendation content on e-commerce platforms has
become increasingly rich -- a single user feed may contain multiple entities,
such as selling products, short videos, and content posts. To deal with the
multi-entity recommendation problem, an intuitive solution is to adopt the
shared-network-based architecture for joint training. The idea is to transfer
the extracted knowledge from one type of entity (source entity) to another
(target entity). However, different from the conventional same-entity
cross-domain recommendation, multi-entity knowledge transfer encounters several
important issues: (1) data distributions of the source entity and target entity
are naturally different, making the shared-network-based joint training
susceptible to the negative transfer issue, (2) more importantly, the
corresponding feature schema of each entity is not exactly aligned (e.g., price
is an essential feature for selling product while missing for content posts),
making the existing methods no longer appropriate. Recent researchers have also
experimented with the pre-training and fine-tuning paradigm. Again, they only
consider the scenarios with the same entity type and feature systems, which is
inappropriate in our case. To this end, we design a pre-training & fine-tuning
based Multi-entity Knowledge Transfer framework called MKT. MKT utilizes a
multi-entity pre-training module to extract transferable knowledge across
different entities. In particular, a feature alignment module is first applied
to scale and align different feature schemas. Afterward, a couple of knowledge
extractors are employed to extract the common and entity-specific knowledge. In
the end, the extracted common knowledge is adopted for target entity model
training. Through extensive offline and online experiments, we demonstrated the
superiority of MKT over multiple State-Of-The-Art methods.