Using instruction-tuned LMs for scalable use case-based shopping - Where customers meet their needs

Products on e-commerce platforms are usually organized based on seller-provided product attributes. Customers looking for a product typically have certain needs or use cases in mind, such as headphones for gym classes, or a printer for school projects. However, they often struggle to map these use cases to product attributes, thereby failing to find the product they need. To help customers shop online confidently, we propose a Use case-Based Shopping (UBS) system that facilitates customer experiences based on use cases (Fig. 1). UBS consists of three steps: 1) use case phrase extraction from product reviews, 2) clustering the extracted use case phrases to identify the dominant ones, and 3) mapping products in a given category to one or more of these dominant use cases. In this work, we utilize instruction-tuned LMs to primarily focus on the first two steps. However, the way we design them also helps us to seamlessly solve the third step to complete the design of our proposed system. First, we define the novel task of joint Use Case, Sentiment Extraction (UCSE) from product reviews which can be used for both steps 1 and 3. We harness the task adaptation capability of instruction-tuned FLAN-T5 models and gradually improve their zero-shot UCSE performance through instruction tuning, multi-task training, and few-shot iterative re-training for new categories, achieving around 90% reduction in annotation band-width. We then employ Anthropic’s Claude 2 LLM to propose an unsupervised approach for hierarchical use case phrase clustering that demonstrates better clustering and cluster naming capabilities when compared to K-Means and LDA. In an online experiment targeting the top 7 product categories, UBS recommendations on search, browse, and product pages resulted in a revenue lift of 0.77%, 0.94%, and 0.44% respectively, and an average click rate lift of 0.15%.