Proceedings - Product Search and Recommendation 2025¶

In this work, we present the fully lexical and reproducible system developed by the DUTH team for the TREC 2025 Product Search & Recommendation track, aiming to improve performance on task-oriented e-commerce queries. Such queries (e.g., home office makeover, birthday party essentials) often perform poorly in purely lexical retrieval systems because they express high-level user intents rather than concrete product attributes. Our system indexes approximately 1.08M products using Lucene/Pyserini, retrieves with BM25 (tuned to k1=0.9, b=0.4), and bridges the intent–metadata gap through carefully calibrated RM3 pseudo-relevance feedback. For the interactive setting, we automatically generate four PRF-based query reformulations per topic and aggregate complementary signals using weighted Reciprocal Rank Fusion. The system requires neither neural re-ranking nor external resources, runs efficiently on a single CPU node, and produces standard six-column TREC runs with strict de-duplication. Official evaluation results confirm that RM3 and fusion yield consistent improvements over the BM25 baseline across task completion nDCG, MAP, and Essential Recall@1000. These findings highlight that thoughtful lexical reformulation, classical PRF, and simple fusion strategies remain strong and efficient baselines for task-oriented product search.

This paper presents the JBNU team’s participation in the TREC 2025 Product Search and Recommendations Track. For the Search Task, we develop two complementary query reformulation strategies: an LLM-driven method that generates structured Lucene-style reformulations to reduce query ambiguity, and a multimodal approach that leverages a vision–language model (VLM) to extract additional semantic cues from web-sourced images. For the Recommendation Task, we adopt a two-stage architecture in which neural retrieval models (dense and learned sparse) generate candidate products, and relation classification—performed by either an LLM or a fine-tuned BERT model—reranks them as substitutes or complements, with final lists refined through weighted score aggregation. Experimental results show that both LLM-based query reformulation and classification-driven reranking consistently improve effectiveness across tasks. Overall, the study demonstrates that lightweight LLM components, when strategically integrated into retrieval and recommendation pipelines, provide a scalable and robust approach to product understanding in the TREC setting.