Neural IR Meets Graph Embedding: A Ranking Model for Product Search
This work addresses the problem of enhancing product search accuracy for e-commerce platforms by integrating graph embeddings into neural IR models, representing an incremental advancement.
The paper tackled the challenge of incorporating graph-based features into neural information retrieval models for product search, achieving significant improvements over strong baselines on a real-world e-commerce dataset.
Recently, neural models for information retrieval are becoming increasingly popular. They provide effective approaches for product search due to their competitive advantages in semantic matching. However, it is challenging to use graph-based features, though proved very useful in IR literature, in these neural approaches. In this paper, we leverage the recent advances in graph embedding techniques to enable neural retrieval models to exploit graph-structured data for automatic feature extraction. The proposed approach can not only help to overcome the long-tail problem of click-through data, but also incorporate external heterogeneous information to improve search results. Extensive experiments on a real-world e-commerce dataset demonstrate significant improvement achieved by our proposed approach over multiple strong baselines both as an individual retrieval model and as a feature used in learning-to-rank frameworks.