A Survey on the Use of Personalized Model-Based Search Engine

Mahdi, Mohammad Najah; Ahmad, Abdul Rahim; Qassim, Qais Saif; Subhi, Mohammed Ahmed; Bakare, Taofiq Adeola

doi:10.1007/978-3-030-82616-1_9

Cited by 1 publication

(1 citation statement)

References 31 publications

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“…Initial work in this field [21,25] relied on the semantic features of queries and products, but subsequent research has highlighted the importance of incorporating behavioral signals [5] such as click-through data and purchase data in learning representations. However, these methods combine semantic and graph information in a latent space, which results in a lack of interpretability.…”

Section: Introductionmentioning

confidence: 99%

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

Choudhary,

Huang,

Subbian

et al. 2024

Companion Proceedings of the ACM Web Conference 2024

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The problem of search relevance in the E-commerce domain is a challenging one since it involves understanding the intent of a user's short nuanced query and matching it with the appropriate products in the catalog. This problem has traditionally been addressed using language models (LMs) and graph neural networks (GNNs) to capture semantic and inter-product behavior signals, respectively. However, the rapid development of new architectures has created a gap between research and the practical adoption of these techniques. Evaluating the generalizability of these models for deployment requires extensive experimentation on complex, real-world datasets, which can be non-trivial and expensive. Furthermore, such models often operate on latent space representations that are incomprehensible to humans, making it difficult to evaluate and compare the effectiveness of different models. This lack of interpretability hinders the development and adoption of new techniques in the field. To bridge this gap, we propose Plug and Play Graph LAnguage Model (PP-GLAM), an explainable ensemble of plug and play models. Our approach uses a modular framework with uniform data processing pipelines. It employs additive explanation metrics to independently decide whether to include (i) language model candidates, (ii) GNN model candidates, and (iii) inter-product behavioral signals. For the task of search relevance, we show that PP-GLAM outperforms several state-of-the-art baselines as well as a proprietary model on real-world multilingual, multi-regional e-commerce datasets. To promote better model comprehensibility and adoption, we also provide an analysis of the explainability and computational complexity of our model. We also provide the public codebase and provide a deployment strategy for practical implementation.

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Section: Introductionmentioning

confidence: 99%