  Personalization lies at the core of boosting the product search system
performance. Prior studies mainly resorted to the semantic matching between
textual queries and user/product related documents, leaving the user
collaborative behaviors untapped. In fact, the collaborative filtering signals
between users intuitively offer a complementary information for the semantic
matching. To close the gap between collaborative filtering and product search,
we propose a Hierarchical Heterogeneous Graph Neural Network (HHGNN) approach
in this paper. Specifically, we organize HHGNN with a hierarchical graph
structure according to the three edge types. The sequence edge accounts for the
syntax formulation from word nodes to sentence nodes; the composition edge
aggregates the semantic features to the user and product nodes; and the
interaction edge on the top performs graph convolutional operation between user
and product nodes. At last, we integrate the higher-order neighboring
collaborative features and the semantic features for better representation
learning. We conduct extensive experiments on six Amazon review datasets. The
results show that our proposed method can outperform the state-of-the-art
baselines with a large margin. In addition, we empirically prove that
collaborative filtering and semantic matching are complementary to each other
in product search performance enhancement.

Information Retrieval