A unified Neural Network Approach to E-CommerceRelevance Learning
This addresses the challenge of improving user experience in e-commerce search by enhancing semantic relevance understanding, though it appears incremental as it builds on existing neural network techniques with specific optimizations.
The paper tackled the problem of result relevance scoring in e-commerce search by developing a scalable feed-forward neural model using only query and item title features, achieving significant improvement over GBDT and deep-learning baselines on an independent ratings dataset, with the GBDT model requiring 10 times more features.
Result relevance scoring is critical to e-commerce search user experience. Traditional information retrieval methods focus on keyword matching and hand-crafted or counting-based numeric features, with limited understanding of item semantic relevance. We describe a highly-scalable feed-forward neural model to provide relevance score for (query, item) pairs, using only user query and item title as features, and both user click feedback as well as limited human ratings as labels. Several general enhancements were applied to further optimize eval/test metrics, including Siamese pairwise architecture, random batch negative co-training, and point-wise fine-tuning. We found significant improvement over GBDT baseline as well as several off-the-shelf deep-learning baselines on an independently constructed ratings dataset. The GBDT model relies on 10 times more features. We also present metrics for select subset combinations of techniques mentioned above.