Jianghong Zhou

Jianghong Zhou, Joyce C. Ho, Chen Lin et al.

Interactive search can provide a better experience by incorporating interaction feedback from the users. This can significantly improve search accuracy as it helps avoid irrelevant information and captures the users' search intents. Existing state-of-the-art (SOTA) systems use reinforcement learning (RL) models to incorporate the interactions but focus on item-level feedback, ignoring the fine-grained information found in sentence-level feedback. Yet such feedback requires extensive RL action space exploration and large amounts of annotated data. This work addresses these challenges by proposing a new deep Q-learning (DQ) approach, DQrank. DQrank adapts BERT-based models, the SOTA in natural language processing, to select crucial sentences based on users' engagement and rank the items to obtain more satisfactory responses. We also propose two mechanisms to better explore optimal actions. DQrank further utilizes the experience replay mechanism in DQ to store the feedback sentences to obtain a better initial ranking performance. We validate the effectiveness of DQrank on three search datasets. The results show that DQRank performs at least 12% better than the previous SOTA RL approaches. We also conduct detailed ablation studies. The ablation results demonstrate that each model component can efficiently extract and accumulate long-term engagement effects from the users' sentence-level feedback. This structure offers new technologies with promised performance to construct a search system with sentence-level interaction.

Jianghong Zhou, Weizhi Du, Md Omar Faruk Rokon et al.

The rapid proliferation of e-commerce platforms accentuates the need for advanced search and retrieval systems to foster a superior user experience. Central to this endeavor is the precise extraction of product attributes from customer queries, enabling refined search, comparison, and other crucial e-commerce functionalities. Unlike traditional Named Entity Recognition (NER) tasks, e-commerce queries present a unique challenge owing to the intrinsic decorative relationship between product types and attributes. In this study, we propose a pioneering framework that integrates BERT for classification, a Conditional Random Fields (CRFs) layer for attribute value extraction, and Large Language Models (LLMs) for data annotation, significantly advancing attribute recognition from customer inquiries. Our approach capitalizes on the robust representation learning of BERT, synergized with the sequence decoding prowess of CRFs, to adeptly identify and extract attribute values. We introduce a novel decorative relation correction mechanism to further refine the extraction process based on the nuanced relationships between product types and attributes inherent in e-commerce data. Employing LLMs, we annotate additional data to expand the model's grasp and coverage of diverse attributes. Our methodology is rigorously validated on various datasets, including Walmart, BestBuy's e-commerce NER dataset, and the CoNLL dataset, demonstrating substantial improvements in attribute recognition performance. Particularly, the model showcased promising results during a two-month deployment in Walmart's Sponsor Product Search, underscoring its practical utility and effectiveness.