Personalized Product Search Ranking: A Multi-Task Learning Approach with Tabular and Non-Tabular Data
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915445803581440 |
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| author | Morishetti, Lalitesh Kumar, Abhay Scott, Jonathan Nag, Kaushiki Sharma, Gunjan Vashishtha, Shanu Sridhar, Rahul Chatter, Rohit Achan, Kannan |
| author_facet | Morishetti, Lalitesh Kumar, Abhay Scott, Jonathan Nag, Kaushiki Sharma, Gunjan Vashishtha, Shanu Sridhar, Rahul Chatter, Rohit Achan, Kannan |
| contents | In this paper, we present a novel model architecture for optimizing personalized product search ranking using a multi-task learning (MTL) framework. Our approach uniquely integrates tabular and non-tabular data, leveraging a pre-trained TinyBERT model for semantic embeddings and a novel sampling technique to capture diverse customer behaviors. We evaluate our model against several baselines, including XGBoost, TabNet, FT-Transformer, DCN-V2, and MMoE, focusing on their ability to handle mixed data types and optimize personalized ranking. Additionally, we propose a scalable relevance labeling mechanism based on click-through rates, click positions, and semantic similarity, offering an alternative to traditional human-annotated labels. Experimental results show that combining non-tabular data with advanced embedding techniques in multi-task learning paradigm significantly enhances model performance. Ablation studies further underscore the benefits of incorporating relevance labels, fine-tuning TinyBERT layers, and TinyBERT query-product embedding interactions. These results demonstrate the effectiveness of our approach in achieving improved personalized product search ranking. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_09636 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Personalized Product Search Ranking: A Multi-Task Learning Approach with Tabular and Non-Tabular Data Morishetti, Lalitesh Kumar, Abhay Scott, Jonathan Nag, Kaushiki Sharma, Gunjan Vashishtha, Shanu Sridhar, Rahul Chatter, Rohit Achan, Kannan Information Retrieval Artificial Intelligence Computation and Language Machine Learning In this paper, we present a novel model architecture for optimizing personalized product search ranking using a multi-task learning (MTL) framework. Our approach uniquely integrates tabular and non-tabular data, leveraging a pre-trained TinyBERT model for semantic embeddings and a novel sampling technique to capture diverse customer behaviors. We evaluate our model against several baselines, including XGBoost, TabNet, FT-Transformer, DCN-V2, and MMoE, focusing on their ability to handle mixed data types and optimize personalized ranking. Additionally, we propose a scalable relevance labeling mechanism based on click-through rates, click positions, and semantic similarity, offering an alternative to traditional human-annotated labels. Experimental results show that combining non-tabular data with advanced embedding techniques in multi-task learning paradigm significantly enhances model performance. Ablation studies further underscore the benefits of incorporating relevance labels, fine-tuning TinyBERT layers, and TinyBERT query-product embedding interactions. These results demonstrate the effectiveness of our approach in achieving improved personalized product search ranking. |
| title | Personalized Product Search Ranking: A Multi-Task Learning Approach with Tabular and Non-Tabular Data |
| topic | Information Retrieval Artificial Intelligence Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2508.09636 |