Recommenders in Ecommerce
Introduction
The presence of recommenders has become ubiquitous in daily life over the past decade or more. Experts agree that improved or hyper-personalisation offers substantial value to both consumers and businesses (see McKinsey, 2023), so it is no surprise that brands are doing their best to make use of today’s recommenders in meeting and exceeding those standards. There are also risks to be managed and drawbacks to be avoided, among them privacy, data quality, unintended effects, and discrimination, and AI offers both an opportunity to better manage and risk of amplifying these downsides. This article looks at the story of the recommender’s development to where it is today, and shed a light on its potential in the future.
Beginnings
The use of modern technology in the human search for good advice is said to have started in 1979 (see Benedetto, 2023) when Carnegie-Mellon University PhD student, Elaine Rich, completed her doctoral dissertation on ‘Grundy’, a system she designed to help her friends and other users to find books they might like. Elaine later became Director of the Microelectronics and Computer Technology Corporation (MCC) AI Laboratory, author of two books on AI, and Distinguished Senior Lecturer at The University of Texas at Austin, and her concept of the recommender system has similarly gone on to achieve better things.
Development
The next major milestones recorded in the development of the recommender system occurred in the 1990s when the emergence of the Internet seemed to spark renewed interest in the potential of technology to personalise the information seeking process. From the beginnings of the Internet through to its commercialisation in the mid-1990s, four major developments occurred for the recommender in the research and development setting of the USA, primarily conducted by academic teams from MIT, the epicentre of late-20th-century computing research.
In 1992, a team of researchers at Xerox Palo Alto Research Centre (PARC) led by computer scientist, David Goldberg, created an experimental email filtering system called Tapestry (Goldberg et al., 1992), which is said to have introduced the concept of collaborative filtering (Jannach et al., 2021). At this time, ‘electronic mail’ was only starting to gain popularity, and the filter worked by allowing users to set rules and criteria for email they wanted to receive, and the criteria were matched to descriptions left by other users on dial-up bulletin board systems where views, opinions, and information about emails, news, music, and other topics were shared.
Two years later a joint research team between MIT and the University of Minnesota led by Paul Resnick (Resnick et al., 1994) sought to automate the collaborative filtering process of Tapestry with a news recommender called GroupLens. GroupLens is described as one of the first systems based on user community ratings and deploying ML to determine the likelihood of a user wanting to view certain items (Jannach et al., 2021).
While all this was happening, then MIT Associate Professor Pattie Maes was leading research into autonomous agents and recommender systems which produced successful products of the time such as the 1992 Helpful Online Music Recommendations (HOMR) prototype which in 1994 became the Ringo email-based music recommender (see Johnson, 2022), and Firefly in 1995, the first commercial, peer-to-peer music recommender, sold to Microsoft in 1998.
State of the Art
In 1994 a Sting CD was purchased from NetMarket, unwittingly creating the first secure online transaction (see Fast Company, 2015), and modern ecommerce was born (Pew Research Centre, 2014), bringing a third generation of recommender systems. With their benefit of scale, big data, and the resources and motivation to invest in creating the best, ecommerce startups at the time and thereafter picked up the recommender ball and have been running with it since.
Amazon, an early leader in online retail, introduced its first recommender system in 1998 (Smith and Linden, 2017), applying item-based collaborative filtering to allow the user to discover items they might like but had not yet seen (ibid.). In 2024 it launched Rufus, a GenAI-powered expert shopping assistant that can make recommendations about shopping needs, products, and comparisons (Mehta and Chilimbi, 2024). By November 2025, Rufus had helped over 250 million customers, who were 60% more likely to make a purchase (AWS, 2025), and around the same time Amazon used its Bedrock platform to give Rufus agentic capabilities.
Other ecommerce platforms that launched in the late 1990s to 2010s have followed Amazon’s lead with AIML-driven recommenders, including eBay, Alibaba, Shopify, Salesforce, and Adobe. Shopify was one of the first to adopt the advantages of GenAI, launching Shopify Magic in 2023 (Shopify, 2023), followed by the Salesforce Agentforce platform in 2024 (Salesforce, 2024), and the incorporation of Adobe’s Sensei into Adobe Commerce, enabling capabilities such as Product Recommendations (Adobe, 2024).
From another angle, GenAI platforms themselves have been quick to establish a position in ecommerce and recommender systems. In 2023 OpenAI assisted Klarna to become the first fintech firm to launch a ChatGPT plugin, later developed into a chat-based shopping assistant (OpenAI, 2024), before launching shopping research for ChatGPT (OpenAI, 2025) which can ask clarifying questions, conduct deep research, and “deliver a personalised buyer’s guide in minutes”. Anthropic launched ‘tool use’ in 2024 (Anthropic, 2024), and Perplexity launched an AI-powered shopping assistant in 2024 (Perplexity, 2024) which learns the user’s preferences through ongoing interaction and now provides in-chat checkout via PayPal (Perplexity, 2025).
Possibilities
Each of these efforts takes us another step closer to having our own AI shopping agents, and Bain Capital Ventures (BCV) refers to this prospect as the “Agentic Commerce Era” (Friend, 2024), highlighting a period that could be as disruptive as the beginnings of ecommerce itself, where the brands who stay ahead of the increasingly accelerating pace of technological development, creative ideas, and time-to-market will be best placed to gain a competitive advantage, shaking out those who lag behind. It is suggested that brands will need to invest more in new capabilities, technologies, and infrastructure to optimise the benefits and manage the risks of implementing AI agents (ibid.), and it is those with the agility to act quickly who will come out ahead.
Summary
As the recommender system rapidly approaches 50 years of development, we can see that vast progress has been made by virtue of the inventive researchers like Elaine Rich who were dedicated to improving human-computer interaction, creative entrepreneurs like Jeff Bezos in the quest to innovate, and the many unsung scientists and engineers working quietly behind the scenes to develop the increasingly sophisticated technologies at their disposal. Yet, there is still much room for the ecommerce recommender system to be improved.
Here, GenAI and AI agents hold much potential in reaching a new level of personalisation that can more fully comprehend the context and nuances of consumer needs through conversation and collaboration, simultaneously reducing cognitive workload and effort in consumer decision-making. The key to unlocking this opportunity lies in not only leveraging AI to personalise the human-computer interaction, but to humanise it, creating more empowering partnerships with consumers. With such significant advancements being made in recommenders over just the past two years, the period up to its half-century looks fascinating.
***This article has been edited on 20 February 2026 to reduce wordcount - improving readability, as well as to update information to reflect technological advancements since original publication in December 2024.
Bibliography
Accenture (2024). The empowered consumer. Retrieved on 29 November 2024 from https://www.accenture.com/au-en/insights/consulting/empowered-consumer.
Adobe (2018). Magento is now Adobe Commerce. Adobe, Blog, The Latest, 19 June 2018. Viewed on 2 December 2024 at https://business.adobe.com/blog/the-latest/magento-is-now-part-of-adobe.
Adobe (2023). Adobe announces new Sensei genAI services to reimagine end-to-end marketing workflows. Adobe, News, 21 March 2023. Viewed on 2 December 2024 at https://news.adobe.com/news/news-details/2023/adobe-announces-new-sensei-genai-services-to-reimagine-end-to-end-marketing-workflows.
Adobe (2024). Introduction to Product Recommendations. Adobe, Documentation, Commerce, Product Recommendations Guide. Viewed on 2 December 2024 at https://experienceleague.adobe.com/en/docs/commerce-merchant-services/product-recommendations/overview.
Anthropic (2024). Claude can now use tools. Anthropic, News, 31 May 2024. Viewed on 2 December 2024 at https://www.anthropic.com/news/tool-use-ga.
Baudry, G., Grudnowski, J., and Pavy, S. (2024). The art and (data) science behind effective customer personalisation. Bain & Company. Viewed on 29 November 2024 at https://www.bain.com/how-we-help/the-art-and-data-science-behind-effective-customer-personalization/.
Benedetto, L. (2023). Recommender systems: benefits and practical guidelines for software professionals. SWForum.eu, May 2023. Viewed on 2 December 2024 at https://swforum.eu/online-sw-forum/software-technology/6/recommender-systems-benefits-and-practical-guidelines.
Britannica Money (2024). Pattie Maes. Britannica Money, Biographies, Entrepreneurs. Viewed on 2 December 2024 at https://www.britannica.com/money/Pattie-Maes.
Fast Company (2015). You’ll never guess what the first thing ever sold on the Internet was. Fast Company, Fast Feed, 26 November 2015. Viewed on 2 December 2024 at https://www.fastcompany.com/3054025/youll-never-guess-what-the-first-thing-ever-sold-on-the-internet-was.
Friend, S. (2024). The dawn of the agentic commerce era: Putting Gen AI to work for shoppers. Bain Capital Ventures. Viewed on 29 November 2024 at https://baincapitalventures.com/insight/the-dawn-of-the-agentic-commerce-era/.
Goldberg, D., Nichols, D., Oki, M., and Terry, D. (1992). Using collaborative filtering to weave an information tapestry. Communications of the ACM 35(12): 61-70. Retrieved on 2 December 2024 from https://www.scirp.org/reference/referencespapers?referenceid=1776081.
Jannach, D., Pu, P., Ricci, F., and Zanker, M. (2021). Recommender systems: Past, present, future. AI Magazine, 42(3), 3-6. Retrieved on 29 November 2024 from https://ojs.aaai.org/aimagazine/index.php/aimagazine/article/view/18139.
Johnson, S. (2022). Intelligent agent: How Pattie Maes almost invented social media. Netguru, Hidden Heroes. Viewed on 2 December 2024 at https://hiddenheroes.netguru.com/pattie-maes.
Kirsner, S. (1998). Firefly: From the Media Lab to Microsoft. Wired, Business, 9 April 1998. Viewed on 2 December 2024 at https://www.wired.com/1998/04/firefly-from-the-media-lab-to-microsoft/.
Klarna (2024). Shopping made smarter: Klarna adds more AI features to its assistant powered by OpenAI. Klarna, 19 September 2024. Viewed on 2 December 2024 at https://www.klarna.com/international/press/shopping-made-smarter-klarna-adds-more-ai-features-to-its-assistant-powered-by-openai/.
McKinsey & Company (2023). What is personalisation? McKinsey Explainers, 30 May 2023. Viewed on 29 November 2024 at https://www.mckinsey.com/featured-insights/mckinsey-explainers/what-is-personalization.
Mehta, R. and Chilimbi, T. (2024). Amazon announces Rufus, a new generative AI-powered conversational shopping experience. Amazon, News, Retail. Viewed on 29 November 2024 at https://www.aboutamazon.com/news/retail/amazon-rufus.
MIT Media Lab (2024). Pattie Maes, MIT Media Lab, People. Viewed on 2 December 2024 at https://www.media.mit.edu/people/pattie/overview/.
Negroponte, N. (1996). Electronic word of mouth. Wired, Message 40, 1 October 1996. Viewed on 2 December 2024 at https://web.media.mit.edu/~nicholas/Wired/WIRED4-10.html.
OpenAI (2024). Klarna’s AI assistant does the work of 700 full-time agents. OpenAI, Customer Stories. Viewed on 2 December 2024 at https://openai.com/index/klarna/.
OpenAI (2025). Introducing Shopping Research in ChatGPT. OpenAI, 24 November 2025. Viewed on 20 February 2026 at https://openai.com/index/chatgpt-shopping-research/.
Perplexity (2024). Shop like a Pro: Perplexity’s new AI-powered shopping assistant. Perplexity, Hub, Blog, 18 November 2024. Viewed on 2 December 2024 at https://www.perplexity.ai/hub/blog/shop-like-a-pro
Perplexity (2025). Shopping that puts you first. Written by the Perplexity Team for Perplexity, Hub, Blog, 25 November 2025. Viewed on 20 February 2026 at https://www.perplexity.ai/hub/blog/shopping-that-puts-you-first
Peters, J. (2024). Shopify’s AI ‘Sidekick’ chatbot for merchants is now in early access. The Verge, 24 June 2024. Viewed on 2 December 2024 at https://www.theverge.com/2024/6/24/24183504/shopify-ai-sidekick-chatbot-merchants-summer-edition.
Pew Research Centre (2014). World Wide Web Timeline. Viewed on 29 November 2024 at https://www.pewresearch.org/internet/2014/03/11/world-wide-web-timeline/.
Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., and Riedl, J. (1994). Grouplens: An open architecture for collaborative filtering of Netnews. In Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work, CSCW ’94, 175-86. Retrieved on 2 December 2024 from https://dl.acm.org/doi/10.1145/192844.192905.
Rich, E. (1979). User modelling via stereotypes. Cognitive Science 3, 329-354. Retrieved on 29 November 2024 from https://www.cs.utexas.edu/~ear/.
Rich, E. (1983). Users are individuals: Individualising user models. International Journal of Man-Machine Studies (1983) 18, 199-214. Retrieved on 29 November 2024 from The University of Texas at Austin, https://www.cs.utexas.edu/~ear/.
Robison, K. (2024). Anthropic’s AI now lets you create bots to work for you. The Verge, 31 May 2024. Viewed on 2 December 2024 at https://www.theverge.com/2024/5/30/24167231/anthropic-claude-ai-assistant-automate-tasks
Salesforce (2016). Salesforce completes acquisition of Demandware. Salesforce, News & Insights. Viewed on 2 December 2024 at https://www.salesforce.com/news/press-releases/2016/07/11/salesforce-completes-acquisition-of-demandware/.
Salesforce (2024). Salesforce unveils Agentforce – what AI was meant to be. Viewed on 2 December 2024 at https://www.salesforce.com/au/news/press-releases/2024/09/12/agentforce-announcement/.
Schrage, M. (2022). History’s great recommenders: From ancient times to tomorrow. The MIT Press Reader, MIT Press, 5 December 2022. Viewed on 29 November 2024 at https://thereader.mitpress.mit.edu/historys-great-recommenders-from-ancient-times-to-tomorrow/.
Shopify (2023). Introducing AI-generated product descriptions powered by Shopify Magic. Shopify, Blog, Updates, 20 April 2023. Viewed on 2 December 2024 at https://www.shopify.com/au/blog/ai-product-descriptions.
Smith, B. and Linden, G. (2017). Two decades of recommender systems at Amazon.com. IEEE Internet Computing Vol. 21 Issue 3, pp. 12-18. Retrieved on 29 November 2024 from https://ieeexplore.ieee.org/document/7927889.
