# recommender-systems Published articles for recommender-systems. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Building a Transformer-Based Category Recommender at Thumbtack DevFeed: [Building a Transformer-Based Category Recommender at Thumbtack]() Original publisher: [Read original article]() Author: Andrew Morss Published: 2026-07-20T20:23:27Z Content type: article Language: en Sources: [Thumbtack Engineering - Medium]() Topics: [recommendation systems](), [Transformer](), [Machine learning](), [email](), [push-notification]() Tags: [catalog](), [data-science](), [email](), [engineering](), [machine-learning](), [model](), [push-notification](), [recommendations](), [recommender-systems](), [torch]() ### AI overview This engineering article describes Thumbtack's category recommender, which selects home-service categories for push and email notifications. It explains how the system addresses popularity bias through negative sampling and how Thumbtack's small category catalog and sparse user activity shape the recommendation problem. ### Source excerpt A look at compensating for popularity bias in recommender systems using negative sampling strategies By: Andrew Morss, Senior Applied Scientist Introduction A recommender system is a machine learning model that, given a user and a catalog of items, predicts which items that user is most likely to want. Recommenders set your YouTube playlist, determine what items Amazon suggests for you, push you songs on Spotify and customize your Steam store. If you're a homeowner, Thumbtack's recommender systems can suggest home projects for you such as house cleaning or lawn mowing. Thumbtack connects users with professionals spread across almost 500 different job categories. Job categories are a kind of occupational bucket that Thumbtack uses as a taxonomy to categorize local service professionals (pros). Pros have the opportunity to choose categories of jobs that they want to receive leads on; examples include things like "Electrical and Wiring Repair", "Local Moving" or "Snow Plowing". Thumbtack's Lifecycle marketing team uses a category recommender to choose which categories to surface to users in our push and email notifications. Choosing the right ones is critical. A well-chosen suggestion is how a customer starts a search for a new project, finds the right service professional, and ultimately gets work done. An example of a push notification and an in-email category recommendation. The Problem Space: Category Recommendation There are several features of category recommendations at Thumbtack that are fairly unique in the item recommender space. The number of categories that we have is relatively small (~500) compared to the number of items that are usually seen in recommender systems, which simplifies the problem considerably. Usually recommenders require a two stage approach where an initial, low compute, filtering stage reduces the number of items to some "candidate" list of items before a more complicated model ranks that smaller set of items. With a relatively small cat ## Semantic IDs: Product Understanding at Scale DevFeed: [Semantic IDs: Product Understanding at Scale]() Original publisher: [Read original article]() Author: Shrikar Archak Published: 2026-06-02T16:58:00Z Content type: article Language: en Sources: [Instacart]() Topics: [recommendation systems](), [data](), [systems]() Tags: [embedding](), [ids](), [machine-learning](), [model](), [product](), [recommendation-system](), [recommender-systems](), [retrieval](), [scale](), [vector]() ### AI overview Instacart describes how it built semantic IDs to capture relationships among grocery products that a hierarchical taxonomy misses. The approach uses product embeddings, residual vector quantization, catalog structure, contrastive training, and separate precision and discovery strategies to address cold-start products, sparse categories, and catalog-quality issues. ### Source excerpt Key Contributors: Shrikar Archak, Karuna Ahuja, Soroush Sobhkhiz, Marko Avdalovic, Xiyu Wang, JiChao Zhang, Hao Yan, Chris Hartley Introduction Operating a grocery catalog at Instacart's scale means managing millions of products across thousands of categories. Every product is assigned to a category in our hierarchical taxonomy like "Dairy > Cheese > Parmesan". These categories provide broad classification, but they miss the connections that drive how customers actually shop. For example, a customer is building a cheese board. They've added Parmigiano Reggiano, and now they need accompaniments. Our taxonomy puts it in "Dairy > Cheese > Parmesan," so a category-based system can suggest other parmesan cheeses. But it can't connect them to the Castelvetrano olives in Pantry > Condiments > Olives, the olive tapenade in Deli > Olives Dips and Spreads, or the crudité and pre-assembled cheese tray in Deli > Prepared Meals > Party Trays. These products live in completely different branches of the catalog, with no shared ancestor below "Food." But any customer would tell you they belong together. This cross-category blindness shows up in three ways. Cold start: new products arrive with zero purchase history. We can assign them to the right category, but a category alone can't connect them to the products customers would actually consider alongside them, so they stay invisible. Tail category coverage: recommendation models learn from volume, so they skew toward popular grocery staples. Products in sparse categories lack the interaction data to surface, and the taxonomy gives the model no bridge to related items in other branches. Catalog quality at scale: with millions of products, mislabeling is inevitable -- a protein bar filed under "Candy," a sparkling water under "Soda." A rigid tree has no way to flag these because the only signal is the label itself. In this post, we walk through how we built semantic IDs at Instacart to address these problems: the embedding choices, th ## Improving Embedding-Based Candidate Generation for Recommender Systems with a Two-Tower Model DevFeed: [Improving Embedding-Based Candidate Generation for Recommender Systems with a Two-Tower Model]() Original publisher: [Read original article]() Author: Ahmad Khan Published: 2026-03-18T21:04:58Z Content type: article Language: en Sources: [Glassdoor Engineering]() Topics: [recommendation systems](), [Machine learning](), [Embeddings](), [Neural Network](), [data](), [ordering]() Tags: [ai](), [data](), [embedding](), [machine-learning](), [model-training](), [neural-network](), [ordering](), [ranking](), [recommender-systems](), [self-supervised-learning](), [two-tower-model]() ### AI overview Glassdoor describes a two-tower neural network for candidate generation in its Community feed recommendation system. The model learns user and post embeddings from interaction data to retrieve potentially relevant posts before filtering, ranking, and ordering. ### Source excerpt Overview At Glassdoor (part of Indeed), millions of professionals come to learn about workplaces, ask career questions, and share experiences through our Community platform. When users open the Community Home Feed, they expect to see posts that are relevant to their interests, industry, and career stage. However, generating that feed is not a simple task. At any moment, thousands of new posts across hundreds of community feeds compete for attention. From discussions about interview experiences to workplace culture questions and industry advice, the platform contains a diverse and constantly evolving stream of content. The challenge is to quickly identify which of these posts are most relevant for each user. To address this problem, we built a multi-stage machine learning recommendation system that efficiently sources, filters, and ranks posts before they appear in the user's feed. In a previous post, Inside Glassdoor's Multi-Stage Recommendation System, we described the architecture of this system and how the different stages work together to deliver personalized content. Snapshot of the Glassdoor Community Home Feed At a high level, the system consists of four stages: Candidate Generation -- retrieves a large set of potentially relevant posts Filtering -- removes posts that are undesirable, duplicated, or already seen Ranking -- predicts which posts a user is most likely to engage with Ordering -- applies heuristics to maintain diversity and freshness in the feed Each stage progressively narrows the set of posts until only the most relevant posts remain. 4 Stage ML RecSys Design In this blog post, we focus on the Candidate Generation stage, which is responsible for retrieving an initial pool of potentially relevant posts from a very large pool of posts across many different feeds. For Candidate Generation, we developed a Two-Tower Neural Network Model that learns embeddings for users and posts based on real interaction data. In the rest of this blog post, we will walk ## Our Early Journey to Transform Instacart's Discovery Recommendations with LLMs DevFeed: [Our Early Journey to Transform Instacart's Discovery Recommendations with LLMs]() Original publisher: [Read original article]() Author: Moein Hasani Published: 2026-02-26T18:55:35Z Content type: article Language: en Sources: [Instacart]() Topics: [recommendation systems](), [LLMs](), [Generative AI](), [personalization](), [User Experience](), [Artificial Intelligence]() Tags: [ai](), [discovery](), [generative-ai](), [instacart](), [large-language-models](), [llm](), [llms](), [machine-learning](), [personalization](), [recommendation-systems](), [recommendations](), [recommender-systems](), [systems](), [user-experience]() ### AI overview Instacart describes its early effort to use large language models in the Shopping Hub, an app surface for personalized product discovery. The article covers an AI-native platform for content generation, evaluation, and retrieval, and reports that generative models show promise for improving recommendations at scale. ### Source excerpt Key Contributors: Moein Hasani, Hamidreza Shahidi, Trace Levinson, Guanghua Shu Introduction At Instacart, we are laser-focused on improving the user experience by making shopping feel easy, engaging, and personalized. Our discovery surfaces play a central role in bringing this to life. Alongside explicit Search intents, discovery is our opportunity to meet customers' implicit needs, presenting them with the most relevant and inspiring content we have to offer. The main discovery surface within the Instacart app, referred to here as the "Shopping Hub", is one of the most critical in this regard. This is the surface a customer lands on within the Instacart app after selecting their desired retailer, guiding them along their entire journey. What users see here shapes not just what they buy, but how intuitive and enjoyable their experience feels. Given its importance, our team runs dozens of Shopping Hub experiments per year, constantly evaluating new ways to enrich the discovery experience. Historically, these experiments have been constrained by static content libraries feeding our recommendation systems. With the rapid advancement of generative AI, a critical opportunity began to emerge: rather than incrementally improving a swath of legacy systems, could we leverage LLMs to rethink how content shows up for a user from the ground up? Which new primitives could we build to uplevel quality, personalization, and cohesion across the page? This blog post walks through our early journey to answer these questions. By investing in a new AI-native platform for content generation, evaluation, and retrieval, we have found generative models to show real promise in improving recommendations at scale. Below, we highlight the approach we took in developing this platform, a few key learnings so far, and where we're most bullish moving forward. Limitations of Traditional Recommendation Engines Our Shopping Hub page is constructed from multiple subcomponents called placements. Each p ## Inside Glassdoor's Multi-Stage Recommendation System DevFeed: [Inside Glassdoor's Multi-Stage Recommendation System]() Original publisher: [Read original article]() Author: Prateek Shekhar Published: 2025-08-12T19:37:39Z Content type: article Language: en Sources: [Glassdoor Engineering]() Topics: [recommendation systems](), [recommendations](), [personalization](), [Algorithms](), [ordering](), [ann]() Tags: [algorithms](), [ann](), [glassdoor](), [ordering](), [personalization](), [recommendation-system](), [recommendation-systems](), [recommender-systems]() ### AI overview This article gives a high-level overview of Glassdoor's hybrid, multi-stage recommendation system. It describes how the system uses user behavior, similar-user behavior, and item features to produce personalized recommendations for the Community Feed, Email, and Push notifications, with stages including candidate generation, filtering, scoring, and ordering. ### Source excerpt Introduction: Recommendation systems are central to modern digital platforms, delivering personalized content by intelligently analyzing user behavior and preferences through advanced algorithms. Widely used in e-commerce, streaming services, social media, and online learning, they drive user satisfaction, engagement, and retention while supporting business growth. Typically, these systems operate using one or more of the following approaches: Collaborative Filtering: This approach examines user behavior by comparing how users interact with items. It identifies patterns by analyzing similar user actions. For example, if two users have watched or purchased similar items, the system will recommend those same items to other users with similar behavior. Content-Based Filtering: In this approach, the system analyzes the characteristics of the items themselves, such as text, images, or other features. It recommends items that have features similar to those a user has previously liked. For example, if a user enjoyed books with specific keywords, the system will suggest books with those exact keywords. Hybrid Approach: This approach combines collaborative and content-based filtering to enhance personalization and accuracy. It utilizes user behavior patterns in conjunction with item features, offering more personalized and accurate recommendations. At Glassdoor, our Community Feed, Email, and Push notifications are powered by a recommendation system that follows a hybrid approach. It analyzes individual users' behavior, such as posts, comments, and reactions, as well as the behavior of similar users, to deliver tailored, personalized recommendations. This post presents a high-level overview of the multi-stage architecture behind our system, highlighting candidate generation, filtering, scoring, ordering, and supporting infrastructure. Glassdoor's Recommendation System ArchitectureFigure 1: Glassdoor's Recommendation System Architecture Glassdoor's Recommendation System follo ## 8 Data Conferences Shopify Data Thinks You Should Attend DevFeed: [8 Data Conferences Shopify Data Thinks You Should Attend]() Original publisher: [Read original article]() Author: Rebekah Morgan Published: 2022-06-10T13:58:09Z Content type: article Language: en Sources: [Shopify Engineering](), [Shopify Engineering - Shopify Engineering]() Topics: [Data Science](), [data analytics](), [recommendation systems](), [Apache Spark]() Tags: [analytics](), [conferences](), [data](), [data-science](), [learning](), [open-source](), [recommender-systems](), [spark]() ### AI overview Shopify data scientists and engineers curate a list of upcoming 2022 data conferences, including events covering data science, analytics, machine learning, open source technologies, and recommender systems. ### Source excerpt We asked our data scientists and engineers to curate a list of the top upcoming data conferences for 2022. Whether you're looking for a virtual conference or in-person learning, we've got something that works for everyone. ## Personalized Fishbowl Recommendations with Learned Embeddings: Part 2 DevFeed: [Personalized Fishbowl Recommendations with Learned Embeddings: Part 2]() Original publisher: [Read original article]() Author: Ahmad Khan Published: 2022-04-05T00:24:03Z Content type: article Language: en Sources: [Glassdoor Engineering]() Topics: [recommendation systems](), [Embeddings](), [Natural language processing](), [datasets]() Tags: [deep-learning](), [doc2vec](), [embeddings](), [model](), [nlp](), [personalization](), [quality](), [ranking](), [recommendations](), [recommender-systems](), [research](), [word-embeddings]() ### AI overview This engineering article explains how Fishbowl recommendations can use learned embeddings. It describes Doc2Vec-based post and user embeddings, collaborative signals from users with similar likes, cosine-similarity ranking, and additional personalization features such as employer, job title, work city, and feed information. It also introduces transfer learning for text content embeddings. ### Source excerpt Introduction In the previous blog post, we saw how we can utilize text based embeddings to help recommend posts to users on Fishbowl, a professional networking community that was recently acquired by Glassdoor, in which working professionals can have workplace related conversations with other peers in industry. On Fishbowl, users can anonymously write what's on their mind in posts and also comment on posts from other anonymous users in what we call "bowls" or "feeds"; a collection of posts related to a certain industry or topic. Previously we discussed how in the absence of clear negative signals from click stream data we cannot as easily use a supervised learning setup to rank items to users. Given the inputs we used were text based, we can instead use more unsupervised methods like Doc2Vec [1] to generate post text embeddings. We can treat the text of the posts as individual documents and use those to train a Doc2Vec model to generate a post text embedding. For users, we can take the average post text embedding of the posts the user liked and consider that as the user embedding. We can also add the user embeddings of other users who liked a post into our post embedding calculation so we incorporate some "collaborative" notion of what other similar users liked as opposed to a pure content similarity ranking. We can next compute the cosine similarity between the user and posts embeddings and use the similarity score to rank posts to recommend to users. Ranking via Embeddings Overview However, such an approach can have some shortcomings. First the text of the post is just one of many features we can use for personalization. We can also leverage other user provided information when users agree to sign up like the employer, job title, work city of a user as features. In addition, the bowl or feed name and its description can be considered important features for posts as posts under the same feed tend to be about similar topics. Second, the previous Doc2Vec approach tra ## RecSys Challenge 2019 DevFeed: [RecSys Challenge 2019]() Original publisher: [Read original article]() Author: Jens Adamczak Data Scientist Published: 2019-03-11T00:00:00Z Content type: news Language: en Sources: [Trivago]() Topics: [recommendation systems](), [Data Science](), [Machine learning](), [datasets](), [Algorithms, Complexity](), [Benchmark]() Tags: [algorithm](), [benchmark](), [community](), [data](), [data-science](), [launch](), [machine-learning](), [recommender-systems]() ### AI overview trivago and research partners launched the RecSys Challenge 2019, inviting participants to use released website interaction data to detect user intent and build a click-prediction model for accommodation recommendations. ### Source excerpt Our data scientists and engineers love the challenges that their work presents to them on a daily basis and thrive in our agile environment where they can share their knowledge, learn from other... ## Sharing Vespa (Open Source Big Data Serving Engine) at the SF Big Analytics Meetup DevFeed: [Sharing Vespa (Open Source Big Data Serving Engine) at the SF Big Analytics Meetup]() Original publisher: [Read original article]() Author: amberwilsonla-blog Published: 2018-10-17T17:02:27Z Content type: article Language: en Sources: [Yahoo]() Topics: [big-data](), [Open Source](), [recommendation systems]() Tags: [github](), [meetup](), [open-source](), [recommender-systems](), [yahoo-engineering]() ### AI overview A report on a Vespa presentation at the SF Big Analytics Meetup. It describes Vespa as an open-source big data processing and serving engine and gives Zedge as an example of its use for search, recommendation systems, and mobile content discovery. ### Source excerpt yahoodevelopers: By Jon Bratseth, Distinguished Architect, Oath I had the wonderful opportunity to present Vespa at the SF Big Analytics Meetup on September 26th, hosted by Amplitude. Several members of the Vespa team (Kim, Frode and Kristian) also attended. We all enjoyed meeting with members of the Big Analytics community to discuss how Vespa could be helpful for their companies. Thank you to Chester Chen, T.J. Bay, and Jin Hao Wan for planning the meetup, and here's our presentation, in case you missed it (slides are also available here): Largely developed by Yahoo engineers, Vespa is our big data processing and serving engine, available as open source on GitHub. It's in use by many products, such as Yahoo News, Yahoo Sports, Yahoo Finance and Oath Ads Platforms. Vespa use is growing even more rapidly; since it is open source under a permissive Apache license, Vespa can power other external third-party apps as well. A great example is Zedge, which uses Vespa for search and recommender systems to support content discovery for personalization of mobile phones (Android, iOS, and Web). Zedge uses Vespa in production to serve millions of monthly active users. Visit https://vespa.ai/ to learn more and download the code. We encourage code contributions and welcome opportunities to collaborate. ## Recommender Systems: Content-based, Social recommendations and Collaborative filtering DevFeed: [Recommender Systems: Content-based, Social recommendations and Collaborative filtering]() Original publisher: [Read original article]() Author: Federico Published: 2016-06-26T13:50:00Z Content type: article Language: en Sources: [Federico Cargnelutti]() Topics: [recommendation systems](), [Streaming](), [Machine learning]() Tags: [machine-learning](), [programming](), [recommender-systems](), [software-architecture](), [streaming](), [web-development]() ### AI overview This article explains recommender systems for video-on-demand services. It introduces content-based analysis, social recommendations, and collaborative filtering, and notes that platforms often combine these approaches in hybrid systems. ### Source excerpt With the proliferation of video on-demand streaming services, viewers face a big challenge: finding content across multiple screens and apps. There may be quality information available online but it may be difficult to find. Traditionally, viewers resort to "app switching" which can be frustrating when it comes to finding quality content. With the emergence of [...]