Improving Search Relevance in E-Commerce with Graph and Language Models

Основные понятия

The authors propose PP-GLAM, an ensemble model of language and graph models, to enhance search relevance in e-commerce by addressing interpretability and performance challenges.

Аннотация

An ensemble model, PP-GLAM, is introduced to improve search relevance in e-commerce by combining language and graph models. The model outperforms baselines on real-world datasets, providing better interpretability and adoption strategies.

The content discusses the challenges of search relevance in e-commerce due to evolving architectures. It introduces PP-GLAM as a solution for bridging the gap between research and practical deployment. The model combines language models (LMs) and graph neural networks (GNNs) to capture semantic and behavioral signals for improved search relevance.

PP-GLAM uses additive explanation metrics to independently decide on including language model candidates, GNN model candidates, and inter-product behavioral signals. The approach aims to address the lack of interpretability in existing models by providing a modular framework with uniform data processing pipelines.

Extensive experiments on real-world multilingual e-commerce datasets show that PP-GLAM outperforms state-of-the-art baselines and proprietary models. The model's interpretability is analyzed along with its computational complexity. A deployment strategy is provided for practical implementation.

Overall, PP-GLAM offers a comprehensive solution for improving search relevance in e-commerce through an interpretable ensemble of graph and language models.

Статистика

Rapid advancements in LM research have created challenges for industry frameworks.
E-commerce datasets contain additional information useful for training models.
Initial work focused on semantic features but later highlighted the importance of incorporating behavioral signals.
GBDT-based ensembles can be made explainable using additive SHAP values.
Extensive experiments demonstrate that PP-GLAM outperforms baselines in search relevance tasks.

Цитаты

"The rapid advancements in language modeling and graph neural network research have created challenges for traditional industry frameworks looking to adopt the latest developments."
"PP-GLAM offers a comprehensive solution for improving search relevance in e-commerce through an interpretable ensemble of graph and language models."

Ключевые выводы из

An Interpretable Ensemble of Graph and Language Models for Improving Search Relevance in E-Commerce

by Nurendra Cho... в arxiv.org 03-05-2024

https://arxiv.org/pdf/2403.00923.pdf

Дополнительные вопросы

How can industry frameworks effectively adopt the latest advancements in LM research?

In order to effectively adopt the latest advancements in Language Model (LM) research, industry frameworks can implement a modular ensemble approach like Plug and Play Graph LAnguage Model (PP-GLAM). This approach allows for flexibility in incorporating new LM models without disrupting the existing framework. By using an ensemble of different language-specific and multilingual LM models, companies can leverage the strengths of each model while adapting to diverse datasets across multiple regions. Additionally, employing a Gradient Boosting Decision Trees (GBDT) ensemble for feature aggregation enables interpretability and adaptability to new data distributions without requiring re-training of individual models.
Industry frameworks should also focus on optimizing their computational resources by parallelizing data processing and model training steps. This includes pre-computing certain information like graph neighborhoods and de-noising product descriptions to reduce inference time during deployment. By carefully managing hyperparameters within a constrained search space, companies can ensure that their models are compliant with industry standards while still benefiting from the latest LM advancements.

How can interpretability be enhanced in existing models without compromising performance?

Enhancing interpretability in existing models without compromising performance involves utilizing explainable techniques such as SHAP values for feature attribution. By employing additive explanation metrics like SHAP values, companies can independently assess the importance of individual features or components within a model. This not only provides insights into how decisions are made but also aids in comparing different models' effectiveness.
To enhance interpretability further, companies should consider ensembling different types of interpretable models such as GBDTs instead of traditional MLPs or attention layers which operate in latent spaces. GBDT ensembles offer transparency through decision trees that allow stakeholders to understand how predictions are made at each step. Moreover, these ensembles do not require re-training all previous candidates when adapting to new data distributions, making them more efficient for practical implementation.

What are the implications of combining semantic features with behavioral signals in e-commerce datasets?

Combining semantic features with behavioral signals in e-commerce datasets has significant implications for improving search relevance and recommendation systems. Semantic features extracted from query-product text help capture user intent more accurately by understanding nuances in language usage. On the other hand, behavioral signals such as clicks, purchases, adds-to-cart provide valuable insights into user interactions with products based on past behavior.
By integrating both types of features into a unified model like PP-GLAM mentioned earlier, companies can enhance their understanding of customer preferences and improve product recommendations accordingly. The combination allows for a holistic view of user behavior patterns along with textual context, leading to more personalized and relevant suggestions for users browsing e-commerce platforms.
This integration also addresses challenges related to dataset diversity across regions by leveraging multilingual language models alongside region-specific behavioral signals analysis.