{"ID":2882705,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.09636","arxiv_id":"2508.09636","title":"Personalized Product Search Ranking: A Multi-Task Learning Approach with Tabular and Non-Tabular Data","abstract":"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.","short_abstract":"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 di...","url_abs":"https://arxiv.org/abs/2508.09636","url_pdf":"https://arxiv.org/pdf/2508.09636v1","authors":"[\"Lalitesh Morishetti\",\"Abhay Kumar\",\"Jonathan Scott\",\"Kaushiki Nag\",\"Gunjan Sharma\",\"Shanu Vashishtha\",\"Rahul Sridhar\",\"Rohit Chatter\",\"Kannan Achan\"]","published":"2025-08-13T09:15:08Z","proceeding":"cs.IR","tasks":"[\"cs.IR\",\"cs.AI\",\"cs.CL\",\"cs.LG\"]","methods":"[\"Transformer\"]","has_code":false}