The “era of abundance”, so vividly demonstrated today, brings a wide array of choices due to the endless list of items on the Internet. The main task of any store is to sell more goods, sell them faster and at a higher price, thereby increasing turnover, bringing profit to the owner.
In offline stores, most of these tasks are solved with the help of sellers and consultants. But on the Internet, everything is different. AI-based recommendation systems play the role of a consultant, helping the client navigate the site, suggesting the product of interest, and thereby increasing the likelihood of a purchase.
In any sector where content is offered to users in one form or another, recommending new material is an essential element of the system. Indeed, even a few percent increase in revenue can generate millions of dollars. McKinsey estimates that 35% of Amazon’s revenue or 75% of Netflix’s revenue comes from personalized products, and this percentage is likely to grow.
Recommender systems are an advantageous alternative to search algorithms. They help users discover items they might not have found otherwise and offer personalized products to the taste of the users. For this reason, any large platform needs a recommendation system algorithm to make the user’s shopping more enjoyable by automating the search process, offering personalized items, and saving their time.
Thus, many businesses have been adopting the “Recommended For You” mantra that has been popularized by the likes of Amazon, Netflix, and YouTube by implementing their own version of recommendations to suit their customer needs.
What Is an AI Recommendation System?
Our primary aim is to figure out what an AI-based recommendation system is. An artificial intelligence recommendation system (or recommendation engine) is a class of machine learning algorithms used by developers to predict the users’ choices and offer relevant suggestions to users.
With the usage of data science and the users’ data, recommendation systems in AI filter and recommend the most suitable items to a specific user. It is said that the content recommendation system resembles an experienced shop assistant who knows the needs and preferences, and requirements of the user and can recommend more appealing products is capable, alongside increasing the conversion rate.
Benefits from Implementing a Recommendation System
It is deemed by many that the implementation of recommendation algorithms is too difficult and requires a global restructuring of the entire process of collecting and processing data, as well as changes in business processes, and so on. These doubts are unfounded because recommendation systems can be useful to almost every business, and to start recommending, often the data that is already collected is enough.
- narrow searches to specific items reduce the time it takes to find products and services;
- aid in the selection process for the undecided customer;
- improve the relevance of search results;
- significantly increase the likelihood of other potentially appealing objects getting into the user’s field of vision;
- contribute to a higher purchase rate, user loyalty, and satisfaction with web services increases;
- encourage users to interact with more products, and this leads to increased consumption and increased profits;
- newsletters, personalized advertisements, and push notifications to encourage users to come back, increase the frequency of visits by repeat users, and reduce customer churn;
- increase CTR meaning that you’ve targeted the right people, you had an offer that was appealing enough that a large percentage of ad viewers are clicking;
- show your users newly-released content based on each user’s preference;
- boost order value and profit margin.
Today, every company needs to establish collecting data and be able to use it effectively in business, optimizing and improving user content, reducing costs, increasing revenue and average receipt, and increasing the profitability of the business.
Building an AI-Based Recommendation System
To achieve higher revenue, implementing a custom recommender system will be the best choice. In developing the most suitable AI-based recommendation system for a particular business, it’s better to adhere to the following order of actions:
1. Initial Analysis
We analyze current figures, data assets, and customer goals, processes, and big data on business. In this step, the team defines the growth points, determines the timeline and budget, and develops all corresponding documentation.
2. Prototype Deployment
We develop a draft of the recommendation engine according to the data gathered during the previous phase. Together with paying attention to the probable risks, we prove the hypothesis and show the efficiency of the recommendation system prototype.
3. Recommender Release and Implementation
We make final improvements on the recommendation system prototype to satisfy the customers’ needs and integrate it with the existing infrastructure.
For a recommendation system to be useful, it should be flexible to new user behavior. In case few customers log in to use your services, use different devices, and search for products anonymously, it would be beneficial to look at a probabilistic model to identify a single user over time and across devices.
Regardless of the product advertised, every company may implement a bespoke recommendation system, considering the specificity of products and the number of items and users. The specialists will help you develop the most suitable and effective recommendation system with tools that can help your business thrive.
Four Phases of Recommendation Engine Processes
In analyzing the data to provide users with the most suitable recommendations, AI-based recommendation systems take the following steps:
1. Collecting Data
AI-based recommendation systems collect user data using a combination of explicit and implicit methods. Examples of explicit data collection include:
- Request to evaluate the object;
- Request to rank a group of objects from best to worst;
- Presenting two objects to the user with the question of which one is better;
- Request to create a list of objects appealing to the user.
Examples of implicit data collection are:
- Monitoring what the user’s search and views in online stores;
- Keeping records of user behavior online;
- Tracking the contents of the user’s computer;
- Analyzing the user’s social network and discovering similar likes and dislikes.
When the system has gathered enough data, it offers more relevant recommendations, enhancing the chances of interest users.
2. Accumulating Data
The usual explanation is the more data the system gathers, the better recommendations it eventually provides. Thus, you should create more data for the algorithms to have more appealing recommendations.
3. Analyzing Data
To find items with computer vision, it is necessary to filter them with the use of various analyzing methods. Depending on the time, needed to analyze users’ data, we may distinguish the following systems:
- Real-time analysis. When every second counts, fast AI recommendation systems will provide recommendations immediately, as soon as the data is created.
- Near-real-time analysis. When speed is important, but you don’t need it immediately, this system is the best. With an ever-growing Netflix catalog, finding the right content for the audience in near real-time provides the best personalized experience.
- Batch analysis. This analysis can take hours, or perhaps even days. Only after having gathered a considerable amount of data, it presents recommendations, for example, as daily e-mail letters.
4. Filtering Data
When the system has gathered and analyzed enough data to make relevant recommendations, the last step is filtering the data. One can filter the data in different ways, depending on what algorithms form the basis of the recommendation engine.
Content-based filtering algorithms are on the data collected about each specific item and a profile of the user’s preferences. These systems analyze item attributes to recommend other items with similar properties. These methods are preferable when we know information about an item, but not about the user.
Content-based filtering is a simple method in which we consider the characteristics of the content that the user took interest in and look for similar ones. For example, for film recommendations, we use characteristics such as director, actors, film length, genre, and so on to find similarities between movies. If a person has watched the movie “Fast & Furious” on Netflix, the system may recommend other movies with Vin Diesel in the “thriller” genre.
We may highlight several merits available to content-based recommendation systems users:
- This system needs very little information to start;
- Independence from other users’ data;
- There is no “cold start” problem for new items because they can easily find similar items by using item characteristics;
- You can make recommendations even to unfamiliar users, involving them in the service;
- Results are easier to interpret.
Collaborative Filtering Systems
Collaborative filtering uses information about the behavior of all users in the past—for example, information about purchases or ratings. This model is then used to predict items (or ratings for items) that the user may have an interest in.
The underlying idea of this approach is that if A and B buy similar products, A is more likely to buy the product that B bought than the product that a random person bought. Unlike the content-based approach, there are no attributes that match users or subjects.
The greatest merits of collaborative filtering systems include:
- It is capable of accurately recommending complex items such as movies without requiring an “understanding” of the item itself
- It doesn’t require knowing all products or users. The model can help users discover new interests since it might recommend items because similar users are interested in them.
- These systems are more versatile, meaning that they can be applied to any domain, and can provide cross-domain recommendations.
Among the most frequent downsides of collaborative filtering systems, we may point out:
- Any new product or user requires feedback to make accurate recommendations (this is an example of the cold start problem);
- Due to many users and products, a significant amount of computation power is often necessary to calculate recommendations;
- The endless list of lists leaves most of them unrated with zero blanks. Thus, even the most popular items have very few ratings.
Knowledge-based recommendations require knowledge about the subject area (and not about each product). This very additional knowledge allows making recommendations based not on the “similarity” of something, but with more complex conditions. For example, if you bought a camera, the system may offer you a 10% discount on your camera bag.
It might prompt the user to give a series of rules or guidelines on what the results should look like or an example of an item. Here, you can add filters: a house in a city or a village, number of floors and square meters, wall material.
When using this approach, you will not face the situation “Have you just bought a TV? Probably, these 5 TVs will be useful to you too!” Meanwhile, content-based and collaborative filtering systems are likely to make you such an offer. However, the problem with this approach is the high complexity of its development and data collection.
Factors Influencing the Efficiency of AI Recommendation Systems
When building a recommender system, consider the following points that may influence its effectiveness:
a) Diversity—There should be a good trade-off between accuracy and diversity. It leads to user experience for new items.
b) Privacy—Recommender systems (especially collaborative filtering ones) usually have to deal with privacy concerns because users are reluctant to provide authentic information and preferences.
c) Serendipity—To improve user satisfaction, recommender systems should offer serendipitous suggestions: not only relevant items but also significantly different from the items that the user has rated.
d) Recommender persistence—It might be better to let users reopen or re-rate items than to show new ones. A user might miss a recommendation the first time, simply because he was in a hurry and did not pay attention to the recommendation.
e) Trust—the trustworthiness of users must be an important consideration. If users don’t have trust in the recommendation system, they will not provide information, which impairs the performance of the recommender systems. To increase trust, a company may explain the functioning of its recommendation system to its users in simple terms.
f) Long Tail—It includes some popular items, and the rest are in the very tail. There are very few items that everyone knows about, so it’s of little use to recommend them. Making recommendations for less popular items is more useful: there is interesting content that the user is unlikely to find himself.
g) User demographics—Demographics can also influence the user’s satisfaction with recommendations. The systems can analyze the demographic data of a user to determine which items may be appropriate for a recommendation.
h) Offset of custom views—For example, a user watches the first recommended video, not because it is what they want to watch, but because it is the first in the list. This offset must be bypassed in order not to create a feedback loop effect.
The role and popularity of AI-based recommender systems are obvious in the new digital era. Especially with the use of artificial intelligence, fast recommendations are more widespread, which is time-efficient and pragmatic. They help the customer figure out what they need to make a purchase decision faster. As a result, their loyalty increases, and with a high degree of probability, they will return to the store for new purchases.
Ultimately, such systems serve as both a tool to improve the user experience and maximize the efficiency of the company. Therefore, it is vital for your business to make relevant recommendations to be competitive in the market and satisfy customers’ needs.
To build a recommendation system, it is better to consider each algorithm type, choose the most appropriate one or combine them, and, finally, personalize all the details to suit your business activities. There is no need to adapt to a specific recommendation system. You can build a custom recommendation system for your business, which would combine the best characteristics of each recommendation system algorithm.
Let’s Build an AI-Based Recommendation System with InData Labs
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