Recommendation systems – why you shouldn’t do it on your own
As the Web evolves and data piles up, personalization is crucial. Recommendation systems allow for personalized user experience based on various characteristics. But, it is important to note… One does not simply build a recommendation engine!
Recommendation engines are heavy duty data science systems that provide users with a set of products/items based on different data points. This is not something one can whip out with a couple lines of code. It requires the technical knowledge to transform raw data into meaningful and useful information.
There are two basic approaches to building a recommendation system: collaborative filtering and the content-based approach. The first being a method which makes recommendations based on what other people with the similar interests to the user would have liked. The second is method which makes recommendations based on what products have similar properties to what the user is viewing at the moment. But, sometime they can be used together. This is no easy task! Considering it is merely a feature of product, the amount of resources that this requires to build and maintain can be quite a pain… and it probably still won’t be able to match the state-of-the-art engines. This is where the products from Infinite Analytics come in handy!
At Infinite Analytics we create an ecommerce customers’ Social Footprint based on the user’s social graph, and use predictive analytics on the Social Footprint to personalize the online users’ shopping experience and provide actionable insights for our clients.
Our NLP, machine learning and semantic technologies also helps us establish relationships between users, brands and stars who endorse those brands and that way we build structured data from which we can extract meaningful information for users.
The beauty of our solution is that there is basically no effort for integrating this on the retailer’s end!
It’s most obvious in the digital media space, from click buys to personalized web experiences. For marketing, the AI journey has just kick-started, while in the tech sector it has been applied for a while now. We are still at an early stage where inroads are being made into AI content via chatbots and even some explanatory content creation but what will make anyone jump up and embrace it is when we will start seeing a lot of mainstream content being created by AI.
Prior to joining Infinite Analytics, Richard served as the CFO of CrowdFlower, COO and CFO of Phoenix Technologies, as a member of the board of directors and chairman of the Audit Committee at Intellisync, and previously as CFO and executive vice president strategy and corporate development at Charles Schwab.
Pravin Gandhi has over 50 years of entrepreneurial operational and investing experience in the IT industry in India. He was a founding partner of the first early stage fund India - INFINITY. Subsequently a founding partner in Seedfund I & II. With over 18 years of investing experience, he is extensively well networked in investment and entrepreneurial scene and is an active early stage angel investor in tech & impact space. Pravin holds a BS in Industrial Engineering from Cornell University, and serves on the board of several private corporations in India. He is on the board of SINE, IIT Mumbai Incubator.
Puru has his Masters in Engineering and Management from MIT. Prior to MIT, he worked with Fidelity Investments building electronic trading products and high volume market data processing applications. He has completed his BE from VJTI, Mumbai.
Deb Roy is Professor of Media Arts and Sciences at MIT where he directs the MIT Center for Constructive Communication, and a Visiting Professor at Harvard Law School. He leads research in applied machine learning and human-machine interaction with applications in designing systems for learning and constructive dialogue, and for mapping and analyzing large scale media ecosystems. Deb is also co-founder and Chair of Cortico, a nonprofit social technology company that develops and operates the Local Voices Network to surface underheard voices and bridge divides.
Roy served as Executive Director of the MIT Media Lab from 2019-2021. He was co-founder and CEO of Bluefin Labs, a media analytics company that analyzed the interactions between television and social media at scale. Bluefin was acquired by Twitter in 2013, Twitter’s largest acquisition of the time. From 2013-2017 Roy served as Twitter’s Chief Media Scientist.
Erik Brynjolfsson is the Jerry Yang and Akiko Yamazaki Professor and Senior Fellow at the Stanford Institute for Human-Centered AI (HAI), and Director of the Stanford Digital Economy Lab. He also is the Ralph Landau Senior Fellow at the Stanford Institute for Economic Policy Research (SIEPR), Professor by Courtesy at the Stanford Graduate School of Business and Stanford Department of Economics, and a Research Associate at the National Bureau of Economic Research (NBER).
Akash co-founded IA while studying for his MBA from MIT. Prior to MIT Sloan, he co-founded Zoonga. Before this, Akash was an engineer with Oracle in Silicon Valley. He has completed his M.S from University of Cincinnati and B.E from the College of Engineering, Pune.