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Recommendation Systems
55 posts about Recommendation Systems. Every summary links to the original.
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- 22 companies
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eBay’s Christine Su Named to Fast Company’s 2024 Queer 50 List
eBay Head of Product Christine Su has been named to Fast Company’s 2024 Queer 50 list for the second consecutive year. The list recognizes LGBTQ leaders across industries, while Su’s role covers product work for AI Recommendations, Homepage, and My eBay. She describes using AI to personalize the eBay experience for buyers and sellers, aiming to connect people with products and communities aligned with their passions. Since her first inclusion in 2023, eBay has added AI-powered features including Shop the look, an immersive carousel that curates complementary fashion looks from a buyer’s past shopping history for fashion discovery within the marketplace. Su says the intended experience should feel personal, support discovery, and connect users with people who share their passions.
Efficient Visual Representation Learning And Evaluation
Etsy uses image vector representations for visual search and similar-item recommendations, making low-latency, memory-aware models important for online query inference and downstream retrieval. The work compares EfficientNetB0, a compact CNN, with EfficientFormer-l3, which downsamples multiple blocks and applies attention only in its final stage; its representation uses the last block’s eight-head attention and average-pools the sequence. Fine-tuning adds an embedding and multiple classification heads, while multitask training supervises taxonomy, color, and review-photo labels; both models train on two 16GB P100 GPUs. Evaluation rebuilds a brute-force candidate index after each epoch and measures Recall@5 and Recall@10 on interaction and intra-item retrieval datasets, with early stopping available. EfficientFormer-l3 improves reported offline recall by about 5%, 17%, and 1.8% across three tasks, while experiments report lifts in CTR, purchase rate, click-recall@100, and clicks.
Introducing Shop the look: eBay curating personalized outfits with AI
eBay introduces Shop the look, a generative AI-powered fashion feature that curates personalized outfit carousels from customers’ shopping histories. Interactive hotspots reveal similar items and outfit inspirations, combining new and pre-owned apparel and luxury goods. The experience uses images, items, decades of data, customer insights, and billions of images to adapt recommendations as tastes change, while supporting wardrobe visualization and exploration. It is available on iOS for US and UK shoppers who viewed at least 10 fashion items in the past 180 days; Android is planned later this year. eBay says the feature was developed with its Responsible AI team and RAI Principles, and plans further personalization plus possible expansion beyond fashion.
Graph modelling guidelines
Graph modelling uses graph theory to represent real-world entities, relationships, and properties through nodes and edges, unlocking insights across interconnected datasets. The process begins with defining domain concepts, mapping entities and relationships, and assigning relevant descriptive properties to both nodes and edges. Practitioners choose between graph structures such as property graphs or Resource Description Framework models before optionally developing a structural schema. Data is then imported or generated to populate storage systems such as Neo4j, Amazon Neptune, Azure Cosmos DB, or in-memory data structures. Finally, the graph is visualised and analysed using algorithmic methods like shortest path calculation, centrality measurement, and community detection.
Scaling marketing for merchants with targeted and intelligent promos
Grab previously relied on globally assigned, heuristic promotional campaigns where all users could redeem offers until limits were reached. This lack of targeting and customisation failed to optimize promo spending or meet specific merchant business objectives. To solve this, the engineering team built Bullseye, an automated AI-driven promotional assignment system that customises and assigns offers to consumers. The architecture integrates an operations user interface, backend services, Amazon S3 storage, and Spark jobs executing a suite of predictive modules including eater segmentation, campaign impact simulation, and customer response modeling. Since deploying Bullseye in 2021, the system has increased food campaign sales while decreasing promo spend across food and Mart campaigns.
Stepping up marketing for advertisers: Scalable lookalike audience
Grab's legacy lookalike audience generation platform suffered from long creation SLAs of two working days, high costs, and low weekly update frequencies. To resolve these bottlenecks, the engineering team designed an embedding-based platform powered by an in-memory retrieval service and automated update pipelines. The system creates audience representations by averaging constituent passenger embeddings, determining user membership through real-time cosine score thresholds. To eliminate feature store latency while fitting all embeddings into memory, a hash-based compression method cuts passenger embedding storage needs by roughly 90%. Consequently, audience availability dropped to within 15 minutes of campaign creation, audience generation costs fell by 98%, and ad impressions and clicks doubled.
From Pre-trained to Fine-tuned: Nextdoor’s Path to Effective Embedding Applications
Nextdoor transitioned its ranking and recommendation pipelines from traditional continuous and discrete interaction features to transformer-based representation learning. The engineering team deployed pre-trained Sentence-BERT models to generate multilingual post and comment representations, which were aggregated daily by interaction type to form user embeddings. To improve search recall, the team fine-tuned sentence transformers on unlabeled query session logs using contrastive learning and integrated HNSWlib for approximate nearest neighbor retrieval. Subsequent iterations incorporated labeled feedback, BERTopic for coarse personalization, and experiments with CLIP image embeddings. Infrastructure scaling challenges were addressed by performing embedding transformations directly within FeatureStore and optimizing feature payload formats to minimize microservice network bandwidth.
8 Data Conferences Shopify Data Thinks You Should Attend
Shopify’s data scientists and engineers curated eight upcoming data conferences for 2022, covering virtual, hybrid, and in-person formats. The selection supports learning and development by helping practitioners hear from peers about applications, techniques, use cases, and opportunities in the wider data community. Hybrid options include Data + AI Summit, Transform, RecSys, and ODSC West, with topics spanning analytics, machine learning, open source technologies, recommender systems, MLOps, natural language processing, and big data. In-person choices—PyData London, KDD, Re-Work Deep Learning Summit, and Crunch—feature tutorials, research, business applications, AI challenges, data-team practices, and machine learning at scale, with locations including London, Washington D.C., Toronto, and Budapest.
Director of Machine Learning Insights [Part 2: SaaS Edition]
Engineering directors across SaaS organizations share operational experiences regarding the integration, maintenance, and impact of machine learning systems. Deploying production ML introduces significant operational overhead beyond initial modeling, requiring teams to manage data drift, resolve siloed data sources, and adapt to vertical-specific domain shifts. Leaders emphasize that integrating ML effectively often requires prioritizing core business requirements and traditional ML techniques over newly released, complex algorithms. Allowing end users to incorporate domain knowledge and business controls directly into model interactions remains critical for trust and usability. Looking forward, these leaders anticipate substantial ML-driven operational improvements across enterprise workflows, cybersecurity threat detection, and connected healthcare infrastructure.
Director of Machine Learning Insights
Directors of machine learning must balance complex mathematical modeling, system design, and sector-specific business operations across diverse industries. In digital media, practitioners leverage deep learning and Bayesian techniques to power assistive content creation tools and short-term interest recommendations while maintaining user privacy. Pharmaceutical teams employ natural language understanding, computer vision, and reinforcement learning across research and manufacturing, requiring strict GxP validation to navigate regulatory compliance and dataset diversity challenges. In the utility and energy sector, machine learning bridges disparate relational billing records, spatial GIS feeds, and physical grid machinery data to generate operational insights. Across these domains, leaders emphasize that successful integration depends on data quality validation, model monitoring safeguards, and assistive human-in-the-loop collaboration.
Designing products and services based on Jobs to be Done
The Jobs to be Done (JTBD) framework helps organizations understand consumer goals by defining a job as the progress a consumer seeks to achieve in a specific context. To prioritize new features, GrabFood applied this framework and force diagrams to investigate consumer motivations and friction points. Interviews revealed that working parents ordering family meals experienced anxiety from navigating excessive individual choices during checkout. To address this obstacle, Grab developed an algorithm within the GrabMerchant application that identified complementary food items and automatically suggested ready-made bundles. Following the release, thousands of merchants adopted one-tap bundle creation, streamlining ordering for families while supporting restaurant growth.
Automating Multi-Armed Bandit testing during feature rollout
Traditional feature rollouts and Multi-Armed Bandit testing operate as separate workflows that often depend on delayed offline analysis. To eliminate manual intervention, the Multi-Armed Bandit Optimiser automates testing concurrently during feature rollouts by responding to minute-level feedback metrics. The architecture connects Kafka Streams data processing, a metrics server with Spark jobs, and an adaptive rollout module updating online experimentation configurations. Candidate models are evaluated via Thompson Sampling on Beta distributions, with Monte Carlo simulations determining traffic allocation across user entities. In production for the GrabFood recommendation widget, the system optimizes the Effective Conversion Rate over a 30-minute window and includes fallback distribution logic.
Making Grab’s Everyday App Super
Grab manages an expanding superapp ecosystem comprising ride-hailing, food delivery, payments, and partner content surfaced through the Grab Feed. As content volume grows, the platform risks overwhelming users with irrelevant information. To address this, Grab built a recommendation engine that ranks cards using signals across user profiles, content metadata, and contextual factors such as time and location. The system employs multiple recommendation strategies—including popularity metrics, user favorites, collaborative filtering, habitual patterns, and cross-platform deep embeddings—which are selected or aggregated. Recommendation quality is evaluated via offline metrics like Recall@K and NDCG alongside online engagement experiments.
Meet Shopify Developer Kirsten Westeinde - Shopify
Kirsten Westeinde’s just-over-five-year Shopify career spans internal tools, partner-facing applications, and backend development on Kit as the company scaled. She began with Ruby on Rails, later helped extract partner software from Shopify Core using Elasticsearch, Kafka, Ruby on Rails, and the Shopify API, and now works with Data Engineering on real-time recommendations. The feature combines Go and Ruby and is designed to handle the current load while scaling to future demand. Her problem-solving practices include whiteboarding system interactions, taking mental breaks, and writing or explaining ideas to align collaborators. She also describes mentorship, pair programming, a women’s developer meetup, and f(empower) as ways to support growth and address exclusion, bias, and microaggressions in technology.
How Shopify Uses Recommender Systems to Empower Entrepreneurs
Shopify uses recommender systems to help merchants find relevant insights, actions, apps, themes, services, and other resources across its commerce platform. Its approach represents implicit signals such as purchases, installations, clicks, and views as user-item interactions, then applies binary classification and linear methods including SLIM and LRec to learn an item-item similarity matrix and rank unseen items. The framework supports task-parallel training, interpretable coefficients, user-feature enrichment, and cold-start handling for new users or items, while evaluation considers precision alongside diversity, novelty, serendipity, and business-specific goals. In initial A/B tests, personalized app recommendations produced a 50% higher install rate, home feeds were up to 12% more likely to be rated useful, expert matching increased response and collaboration, and theme recommendations made merchants over 10% more likely to launch stores.
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