Aid for the perfect choice: Recommendation Engine
Every day we encounter situations where we have multiple options to choose from. So how do we select the one that perfectly suits our requirements? The one that is tailored to our taste? The most common answer would be either to attain enough details on each option available and then see which one matches our preferences or ask someone for recommendations. However, going through every detail is tiring, and when we ask another person the dataset is limited.
What if a system could go through hundreds of options within seconds and provide us with the options that best suit our needs? Yes, this is possible through the use of a recommendation engine (also known as a recommendation system).
What is a recommendation engine?
A recommendation engine is a powerful filtering tool that uses algorithms and data analysis to recommend the most relevant items/services to a particular user. It works on the principle of finding patterns in the user behavior and user interaction data which can be obtained directly or indirectly.
We encounter recommendation systems every day in our lives. For example, Spotify uses them to generate personalized playlists, Amazon uses them for product recommendations, Instagram uses them for reels and feeds filtration, etc. While they all use recommendation engines for slightly different purposes, they all have the same objective: increasing sales, enhancing engagement and retention, and providing more personalized customer experiences.
How does a recommendation engine work?
Though different models have different purpose and functioning, all recommendation systems have a standard four-step procedure:
1. Data Collection
Data can be collected by two means:
Explicit: Includes data provided intentionally by the user like movie ratings.
Implicit: Includes data that is gathered from other sources such as search history.
2. Data storage
Once the data is collected, it is stored systematically. The storage type depends on the type of data being stored.
3. Data Analysis
The data is analyzed to find patterns and similar user interactions to help improve the user recommendations. A few of the methods used for this are batch analysis, real-time analysis, and near-real-time system analysis.
branch analysis: Data is processed periodically.
real-time analysis: Data is processed immediately.
near-real-time analysis: Data processing takes a few minutes.
4. Data filtering:
Depending upon the type of recommendation system, various algorithms are used to reach the final recommendations.
Types of Recommendation Engines
Some of the most commonly used Recommendation Engine designs are:
- Collaborative filtering
Collaborative filtering suggests things by matching with other users that have similar interests. It collects user reviews in the form of ratings given by them for specific items and looks for matches in the rating patterns to identify groups of users with similar preferences.
For example, Amazon uses collaborative filtering to suggest products based on its user’s purchasing habits as well as customer reviews
- Content-based filtering
A content-based recommender system suggests products that have similar features to items that the user has previously liked. A standard Content-Based recommender will first build a user profile based on user reviews and item ratings. The user profile is then compared to the features of the items, and the products that fit are thus recommended.
LIBRA is an example of a content-based recommender system. It recommends books based on book descriptions derived from Amazon.com webpages. It learns the user profile and recommends books based on user reviews and features extracted from these web pages.
- Hybrid recommender systems
In the hybrid approach, two or more filtering methods are combined to achieve better efficiency than the Content-Based and Collaborative Filtering approaches when used separately. Several organizations have merged Content-Based and Collaborative Filtering methods to improve performance and mitigate the limitations of these approaches.
For example, Nextflix uses Hybrid recommender systems. It provides suggestions by comparing similar users’ watching and browsing activities (i.e., collaborative filtering) as well as by providing movies that share characteristics with films that the user has rated highly (ie content-based filtering).
Advantages of using a Recommendation Engine.
The advantages can be summarised as below
- An increase in traffic on a website can be achieved through personalized Ads, emails, etc.
- By analyzing the user data, a recommendation engine delivers relevant content to the user.
- It Provides reports to the sellers/ owners of the site about the customers/ audiences. This aids them in quick and efficient decision-making.
- Recommendation engines also help increase customer engagements and increase product sales.
- The use of recommendation systems makes various processes, like searching for a novel to read or a movie to watch, easy, quick, and accurate.
Few Challenges faced by Recommendation Engines
- Lack of data
One of the biggest challenges for a recommendation system is the amount of data it requires. Insufficient/inaccurate data leads to inaccurate recommendations.
- Cold Start Problem
This issue arises as new customers are introduced to the system or new items are added to the catalog. In such situations, neither the taste of the new users is known nor enough information about the new products for it to be classified or purchased by the users. This results in less reliable recommendations.
- Synonymy
Synonymy arises when the same item is known by two or more different names.
- Privacy
Feeding personal information to recommender systems improves recommendation services but can raise concerns about data privacy and security.
Fun Activity
How to make your own recommendation model using python using the MovieLens Dataset
link:
https://www.analyticsvidhya.com/blog/2018/06/comprehensive-guide-recommendation-engine-python/
References
- https://www.appier.com/blog/what-is-a-recommendation-engine-and-how-does-it-work/
- https://www.analyticsvidhya.com/blog/2018/06/comprehensive-guide-recommendation-engine-python/
- https://towardsdatascience.com/what-are-product-recommendation-engines-and-the-various-versions-of-them-9dcab4ee26d5
- https://en.wikipedia.org/wiki/Recommender_system#Collaborative_filtering
- https://marutitech.com/recommendation-engine-benefits/#What_is_a_Recommendation_Engine
- https://www.certona.com/article/benefit-of-recommendation-engines/
- https://www.researchgate.net/publication/294860665_Recommender_Systems_Issues_Challenges_and_Research_OpportunitiesConclusion
