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Table of contents
1.
Introduction
2.
Recommendation System
3.
Types of Recommendation Engines
3.1.
Collaborative filtering
3.2.
Content-based filtering
3.3.
Hybrid Model
4.
How does the recommendation engine work?
4.1.
Data Collection
4.2.
Data Storage
4.3.
Data Analysis
4.4.
Data Filtering
5.
FAQs
6.
Key Takeaways
Last Updated: Mar 27, 2024

Recommendation Engine - Introduction

Author Mayank Goyal
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Introduction

There are millions of products available online, and choosing one out of them is difficult. Earlier, we ask recommendations from our acquaintances, now from the online store. So how do these online stores recommend us users?

These e-commerce companies have developed a recommendation system,i.e., a system to recommend products to users based on different factors. These recommender systems predict the most likely product that the consumer is most likely to purchase. Companies like Amazon, Flipkart, Netflix use these systems to provide suggestions to their consumers.

Recommendation System

A recommendation system is a data filtering tool that uses machine learning algorithms to recommend the most relevant items to a particular user. It operates on finding patterns in consumer behavior data, which can be collected implicitly or explicitly.

Suppose a new user visits an e-commerce site. That site will not have the user's history. So how does the site recommend products to the user in such a scenario? One possible solution can be to recommend the best selling products, i.e., the products which are high in demand. Another possible explanation can be to recommend the products that will incur maximum profit for the business.

So if we can recommend items to users based on their needs and interests, it will positively impact the user experience and increase frequent visits. Hence, businesses nowadays are building smart and intelligent recommendation engines by studying the past behavior of their users.

Different online sites use recommendation systems for various purposes. Still, their sole purpose is to increase productivity, like Netflix uses a recommendation system to improve the number of views. In contrast, amazon uses this engine to increase product sales.

We seek recommendations from a salesperson or friends and family in the past. Today, we have passed this task in algorithms' hands or minds. As a marketing tool, we can say that these machines are well-trained in the art of up-selling and cross-selling.

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Types of Recommendation Engines

There are three types of recommendation engines:

Collaborative filtering

Collaborative filtering collects and analyzes user behavior and preferences to predict what a user will like based on their similarity with other users.

It is an intelligent recommender system that works on the similarity between different users and items widely used as an e-commerce website and online. It checks the taste of similar users and makes recommendations. The system efficiency improves if we provide a large volume of information about users and items.

There are two types of collaborative filtering. First, user-based filtering identifies clusters of users and utilizes the interactions of one specific user to predict the interactions of other similar users. Second, the system checks the items identical to the user's items. The similarity between different items is computed based on the items and not the predictions' users. Users X and Y purchased items A and B with similar tastes.

One advantage of collaborative filtering is that it doesn't need to analyze the content or items. Collaborative filtering chooses things to recommend based on what they know about the user.

Content-based filtering

These systems work on the information of the contents of the products rather than the user view. The main idea is if the user likes an item, then the user may also like the "other" similar item. We can use various features to compute the similarity while checking identical content.

The system calculates the distances between the products to check their similarity. There are different metrics for computing the similarity. For numeric data, we use Euclidean distance. For textual data, calculate cosine similarity, and for categorical data, we compute Jaccard similarity.

Particular advantages of these systems are there is not much information about users' data. Secondly, it does not suffer a cold start, where a drawback is feature should be available to compute this similarity.

One downside of content-based filtering is that the system is limited to recommending products similar to the person's purchase history. Its outreach is limited,i.e., with content-based filtering, it can't recommend new unseen products. For example, it can't recommend products beyond electrical gadgets if the customer has only brought those.

Hybrid Model

Both content-based and collaborative filtering algorithms have their pros and cons. In some cases, creating a meaningful content description can be difficult. A content-based filtering model does not select items if the user's past behavior does not have similar purchased products. So, some additional techniques can be used to make recommendations outside the scope of the user's history purchases.

While collaborative filtering cannot provide recommendations for new items if there are no user ratings for prediction, it will take some time to make accurate predictions even if the users start rating the item.

A system that combines both content-based and collaborative filtering could potentially take advantage of both the representation of the content and the similarities among users. A hybrid recommendation system outperforms both methods, emphasizing collaborative and content-based data. 

In a hybrid recommendation system, natural language processing tags can be generated for each product or item, and vector equations are used to calculate the similarity of products. A collaborative filtering matrix can recommend items depending on behavior and preferences. One example of a hybrid recommendation system is Netflix. It considers both the user's interests (collaborative) and the descriptions or features of the movie or show (content-based).

How does the recommendation engine work?

A recommendation engine works as a combination of data and machine learning technology. Data is crucial in developing a recommendation engine – it is the building block from which we derive patterns. The more information it has, the more efficient and effective it will make relevant revenue-generating suggestions

Data Collection

Data collection is the most crucial step for building a recommendation engine. We can collect the data by two means: explicitly and implicitly. Detailed data is provided intentionally, i.e., input from the users, such as movie ratings. Implicit information is not provided intentionally but gathered from available data streams like search history, clicks, order history, etc.

Data Storage

Once data gathering is done, we need to store it. With time, the amount of data will grow to be vast. So we need to have ample, scalable storage. Depending on the type of data we collect, different types of storage are available. Some types of data storage can include a standard SQL database, a NoSQL database, or some object storage.

Data Analysis

We need to analyze the data that is to be used. There are several ways in which you can analyze data.   ,

Data Filtering

The final step is filtering. Different matrixes or mathematical rules and formulas are applied to the data depending on whether collaborative, content-based, or hybrid model recommendation filtering is used. The outcome of this filtering is the recommendations.

FAQs

  1. How do you evaluate a recommendation?
    Mean Average Precision at K is typically the metric of choice for evaluating the performance of recommender systems. 
     
  2. Why do we need a recommendation system?
    A Recommender System refers to a system capable of predicting the future preference of a set of items for a user and recommending the top things. One key reason we need a recommendation system is that we users have too many options to use due to the prevalence of the Internet.
     
  3. Is the recommender system supervised or unsupervised?
    Unsupervised Learning areas of application include market basket analysis, semantic clustering, recommender systems, etc.

Key Takeaways

This article was very comprehensive on recommendation engines. This article focuses on recommendation systems and their types. Moving on, we saw the workflow of the recommendation system.

To be precise recommendation engine has changed the whole outlook of the business model for e-commerce sites. Thus, recommendation engines are playing a significant role in generating revenues.

I hope you all like this article.

Happy Learning Ninjas!

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