AI-Driven Recommendation Engines: What They Are and How They Work
Understanding Recommendation Engines
A recommendation engine uses algorithms to predict a user’s choice and then offer suggestions to the user, helping them find the thing that may interest them the most. As a result, these recommendations help optimize the user experience so they get what they want faster. Discussing how a recommendation engine works in a consumer context allows us to better understand its application.
We can see many examples of recommendation engines in our everyday online lives. For example, Netflix recommendations are a great example of a recommendation engine. So are suggested articles in different news apps and which friends show up first in our social media feeds. These are examples of recommendation engines showing the content we are most likely to interact with.
From a business perspective in the B2B world, many organizations seek to improve their knowledge and workflow management. Recommendation engines can help with that.
AI-Driven Recommendation Engines At Work
Imagine using an AI-based recommendation engine to manage requests for proposals (RFPs), one of the most rote yet essential tasks for a sales organization. The Evalueserve team created an AI platform to assist teams by separating RFPs into micro-sections, analyzing them, then the engine recommends a response for each section.
From there, the AI platform helps the company create a zero draft, streamlining the beginning of a much larger project. What once required a human to process the entire document can now be managed by a machine, making things more efficient for the company and more enjoyable for the response teams.
Instead of having to get every single section right, the teams can use the recommendation engine to improve accuracy and help inform their response. AI-driven products and platforms like this empower companies to optimize decision-making and drive sustainable and actionable outcomes.
As this kind of AI engine continues to evolve, what was once only possible by humans now becomes faster, more effective, and more accurate. This is just one example of what AI can do for you.
Many business executives and professionals are excited about the prospect of AI, as they should be — the possibilities are endless. However, being on the forefront of innovation can lead to some confusion, and, as such, I receive many questions about AI and recommendation engines. In the next section, I answer some of the most common ones.
Commonly Asked Questions:
Question: What is a recommendation engine?
Answer: Recommendation engines are AI tools based on user behavior, sometimes they also refer to user profiles to recommend the most relevant item to the customer.
Q: Can you expand on what a user profile is?
A: So, in the B2B domain, when we talk about user profiles, they can be more specific. For example, we have certain clients using our digital platforms. The user profile we are referring to could be the designation, the department, the business unit of the user, as well as the person’s role.
There are certain kinds of things of most interest to them. Of course, the B2C domain is more dynamic. A lot more content can be considered part of the user profile
Q: How do you keep AI recommendation engines on track?
A: There are two parts. From a user perspective, data is created when users click any items on the platform, stay on the page for a certain period of time, or like or dislike content. Actions and behaviors like these are fed back into the system, and help the recommendation engine to learn and improve.
On the other hand, in a business domain, user behavior and the document or content itself may not provide the best results. There could be bias. Having an architecture in place to bring background or domain knowledge to bear will be helpful here. In business situations, expert knowledge contributes to making the engine perform better.
At Evalueserve, our platforms also have mechanisms and configurable parameters that allow us to check the model’s performance regularly and improve the recommendation results. As a result, the AI engine will be continually improving over time.
Q: How would you explain AI to someone who may not fully understand it?
A: Just think about a human. Humans have eyes so that we can see and do things like read text. We can watch videos and see pictures. Humans have ears so that we can hear sound. We have mouths so that we can speak. In all those biological communications processes, we are taking raw data points in, processing them using our brain, analyzing them for useful information, and determining actions.
Instead of using the human brain, we can now use machine learning and deep-learning techniques to process more raw materials, get insights, and then take action. We call this AI, or artificial intelligence.
In cases where you are processing text with AI, we call this technology natural language processing or NLP. If you are processing videos or images, that’s computer vision, image recognition. If you process sound and voice, that’s audio processing. And on and on.
Q: What are some ways can a recommendation engine can be useful to a business?
A: Recommendation engines can recommend the most relevant responses to a user’s query, helping them quickly find what they were looking for, whether that’s a product or the answer to an issue they had. Recommendation engines can help businesses decide which of their services or solutions would be of interest to a particular client.
Recommendation engines also help organizations make the most out of their data assets and are a nice add-on to any knowledge management portal.
Q: When you look into the future, how do you see this technology continuing to evolve? Do you see recommendation engines continuing to need human oversight or operating more independently?
A: This is one area that has good adoption in the market. It’s still an evolving solution in the B2C domain, and we are also seeing the trend in the B2B space. More and more applications are starting to adopt a recommendation engine. Maybe one day it will evolve from something good to have to something businesses must have.
Of course, there are challenges involving improving performance. Many things need to be done to improve recommendation engines, such as combatting deep bias, optimizing the computational cost, personalization, et cetera, et cetera. Resolving those matters will be crucial for these applications to take off in the B2B world.
Regarding human oversight, if you’re referring to the feedback loop, yes. Human intervention or human involvement will always continue. Humans need to be there, as a recommendation system is a two-way system. If you are talking about expert knowledge, it is good to incorporate the knowledge into the system.
Finally, yes, the operation will be more independent. The AI engine will operate more independently. That is already happening as it becomes more sophisticated, and the algorithms mature.
A version of this blog post was originally published in two installments on Evalueserve’s website.
