Maximizing cart value by increasing the number of items in electronic carts is one of the key strategies adopted by e-commerce platforms for optimal conversion of positive user intent during an online shopping session. Recommender systems play a key-role in suggesting personalized candidate items that can be added to cart by the user. However, it is important to serve a diverse set of personalized recommendations that 'complement' user's cart content to practically increase item count in cart and also contribute towards product discovery. Borrowed from Quantum Physics, Determinantal Point Processes (DPP) are used widely in recommender systems to diversify personalized product recommendations for improved user engagement. However, vertically scaling DPP for recommendation sets, personalized with vector similarity metric like cosine similarity, to serve large scale real-time concurrent user requests is non-trivial. We propose a vectorized reformulation of cosine similarity and conditional DPP implementation to best utilize the highly improved vector computation capabilities (SIMD) of modern processors. Experimental evidence on real-world traffic shows that the proposed method can handle upto 15x more concurrent traffic while improving latency. The proposed method also uses portable SIMD constructs from Python libraries which can be easily adopted in most available SIMD supported CPUs with minimal code changes.

Fashion Ecommerce is challenging because users have intents that could be very broad Eg: ''I want a dress to wear for my cousin's wedding''. Traditional single-shot retrieval mechanisms like search don't work very well for such intents. To solve this problem we developed ''Maya'', a conversational shopping assistant based on LLMs. Maya has been deployed in production and is available to select cohort of users at scale who come to our platform. It is arguably, one of the earliest production deployments of GenAI Fashion assistants at this scale. We describe the journey of building Maya and lessons post the production deployment.

We describe a human-in-the loop system - AgentBuddy, that is helping Intuit improve the quality of search it offers to its internal Customer Care Agents (CCAs). AgentBuddy aims to reduce the cognitive effort on part of the CCAs while at the same time boosting the quality of our legacy federated search system. Under the hood, it leverages bandit algorithms to improve federated search and other ML models like LDA, Siamese networks to help CCAs zero in on high quality search results. An intuitive UI designed ground up working with the users (CCAs) is another key feature of the system. AgentBuddy has been deployed internally and initial results from User Acceptance Trials indicate a 4x lift in quality of highlights compared to the incumbent system.