An online recommendation engine, commonly referred to as a recommender system, is a piece of software that examines the data at hand to propose items that a website visitor might find interesting, such as books, videos, or jobs, among other things. Let’s deep dive ans see what Is an online recommendation engine.

Any online recommendation engine’s primary objective is to increase demand and involve users. Therefore, recommendation engines are primarily a part of an eCommerce personalization strategy that dynamically adds different products to websites, applications, or emails to improve the user experience.

Advanced data filtering systems, called recommendation engines, make predictions about which material, goods, or services a client will likely use or interact with. Recommendation engines do more than only enhance customers’ product experiences. According to an Epsilon poll, 80% of consumers are more likely to purchase from companies that provide personalized experiences.

How Does an Online Recommendation Engine Work?

Being a subclass of machine learning, the recommendation engine mainly deals with ranking or rating products and users. Broadly speaking, recommender systems predict ratings a user might give to a specific item or product, which are later ranked and returned to the user. This ultimately increases engagement with the users and the platform.

For example, Spotify makes recommendations for songs similar to the ones the user repeatedly listened to and liked. Amazon makes recommendations and suggests products to various users based on the data collected from those users. Companies use recommender engines to generate predictions for the user for products or items that he needs and wants but is unaware of.

There are many different ways to build a recommender system and make online recommendations. The most commonly used approaches are modeling-centric approaches such as collaborative filtering, content-based, and link prediction, while other companies use algorithmic and formulaic methods like Page Rank.

All these approaches vary in complexity, and different approaches are suitable for different businesses. Complexity does not necessarily mean “good” performance. Sometimes simple solutions and implementations of types of recommendations yield the most robust results.

Recommendation engines leverage predictive analytics and thus help businesses to anticipate customers’ wants and needs. The combination of machine learning and statistical modeling in the creation of algorithms analyzes historical and behavioral data and customer item rankings.  

Phases of Processing Data

Recommender systems are most accurate when there’s a significant volume of data at the business’s disposal. The more active users a product or service has, the more data there is to compare behaviors and preferences across demographics.

However, not all of the collected data is relevant and reliable. That being said, building a recommender system on lousy data results in inaccurate and unproductive recommendations.

The first step in creating an effective recommendation engine is acquiring a proper data management strategy and analytics method. Additionally, the recommender function is one of the crucial components of an online recommendation.

A typical eCommerce recommendation system works in four steps. Since data fuels the recommendations, the input necessary for the model training is crucial in making predictions. The initial phase involves gathering relevant data to create a user profile or model on which will be based the recommendations. Recommendation engines mostly rely on data such as user attributes, behaviors, ratings or content of the user accesses’ resources in understanding consumer preferences.

Data collection is a process during which three types of data are collected: 

• Explicit data

This dataset is gathered from explicit signals from users about their preferences. The explicit data consists of user input data, such as ratings for products/services and reviews about how the users liked or disliked a product/service.

• Implicit data

The implicit dataset represents gathering data from user online activity, with no explicit feedback. The implicit feedback is suggested but not stated clearly and represents the user’s interests. Implicit data consists of all types of behavior data, including browsing history, clicks, queries, and watches (count of times a song is played or the movement of the mouse through a webpage) to infer preference.  

• Psychographics

This dataset consists of the user’s attitudes, interests, personality, values, opinions, activities, and lifestyle. This data type is incredibly valuable for marketing purposes and use cases in opinion research, prediction, and broader social research.

Data storage in scalable resources in order to take in the increasing amount of information gathered from user-item interactions. Data analysis is conducted to gain insights: real-time, near-real-time, and batch analyses. The last step in the process is data filtration, using mathematical formulas to segment the data into scripts easily recognized and analyzed by engines. 

Recommendation engines analyze the specific information about the user and make predictions regarding the rating that the user might assign to an item. The feature to predict user ratings of an item, even before the user has provided one, makes the recommender systems a powerful tool.

The specialized recommendation algorithms, along with implemented methods for data filtering, intelligently select which filters and algorithms to apply in the given situation for the specific user.

Typically, the recommendation engine processes data through the following phases:   

Collection

The first step in creating a recommendation engine is gathering data. There are two types of data: explicit and implicit data. Explicit data consists of data inputted by users, such as product ratings and comments. Implicit data consists of order history/return history, cart events, pageviews, click thru, and search logs. This data set is created for each user visiting the site.

Behavior data is easy to collect since the company can keep a log of user activities on its website. Moreover, collecting this type of data is straightforward since there is no need for additional action from the user (the user already uses the application). The downside of this approach is the difficulty of analyzing the data. For instance, filtering the necessary logs from the irrelevant ones can be unwieldy.

Each user has different likes or dislikes about a specific product; as a result, the data set will most likely be distinct. Over time, as more data is collected and the engine analyzes a larger scale of data, it gets more and more precise with its recommendations. As a result, users are more likely to engage, click and buy the items.    

Storing

As mentioned earlier, a more extensive data set analyzed by the algorithms means better recommendations. Over time, the company will gather more and more data, and the recommendation project might turn into a big data project.

The data used to create recommendations is crucial in deciding the type of storage the company should use. The most common databases are the NoSQL database, standard SQL database, or even object storage. Each option is feasible, but several factors must be considered. For example, depending on whether the company aims to capture user input or behavior or factors such as ease of implementation, the amount of stored data, integration, and portability.

It is essential to mention that when storing user ratings or comments, a scalable and managed database minimizes the number of required tasks, ultimately improving the recommendation process.     

Analyzing

Gathered data commonly contains similar user engagement data. The question is: how to find the items in a set of identical user data? The answer is simple: filtering the data using different analysis methods. For example, an agile analysis is needed if the aim is to provide immediate recommendations while the user is still viewing the product.

Some of the common ways of data analysis are:

Real-time systems have the ability to process data as it’s created. Usually, this type of system consists of tools that process and analyze event streams and provide in-the-moment recommendations.

Batch analysis demands periodical processing of the data. This approach requires enough gathered data to make an analysis and offer relevant predictions, such as daily sales volume. This type of system is suitable for sending e-mails at a later date.

The near-real-time analysis allows quick data gathering and enables refreshing the analytics in a short period (every few minutes or seconds). This type of analysis works best for providing recommendations during the same browsing session.

Filtering

The last phase of processing data is filtering the data to get relevant information and thus provide recommendations to the user. The challenging part is to choose an algorithm that best suits the company’s needs and provides relevant predictions. The most commonly used recommender systems are:

• Collaborative Recommender system
• Content-based recommender system
• Hybrid recommender system  

Types of Online Recommendation Engines

The main objective of the recommender system is to provide the best user experience. The recommender engines make suggestions about which product should be bought, which article should be read next, and which movie should be watched, basically creating a stickiness factor to any product or service available online. Companies implement these algorithms to connect potential users with the most relevant items and captivate them to their content. Recommendation systems are designed to predict users’ interests, but not every engine uses the same methodology to form predictions. Recommenders typically use one of the following data filtering methods:

• Collaborative Filtering
• Content-Based System
• Hybrid Model Approach

Collaborative Filtering

The collaborative filtering method is based on collecting and analyzing data that contains information about users’ behavior, activities, and preferences and making predictions about what users would like based on their similarity with other users.

This method is established on the assumption that users who agreed in the past will agree in the future and will like similar types of items as they liked in the past. For example, if user A likes items 1, 2, and 3, and user B likes items 2, 3, and 4, they have similar interests (items 2 and 3), so the algorithm predicts that user A might like item 4, and user B might like item 1. 

The main advantage of this filtering approach is that it does not rely on machine-analyzable content, meaning collaborative filtering can make accurate recommendations even for complex items, such as movies, without requiring an “understanding” of the item itself.

Furthermore, there are several types of collaborative filtering algorithms:

User-user: this collaborative filtering method searches for lookalike users and makes recommendations for items based on what other lookalike users have chosen. While being very efficient, this algorithm takes a lot of time and resources. In order to filter the data, the user-user collaborative filtering method requires computing every customer pair information, which requires much time, and for big base platforms, it takes much work to implement it. 

Item-item: this collaborative filtering method is similar to the previous algorithm, with one difference: instead of searching for lookalike users, this method finds lookalike items. Once the matrix is filled with lookalike items, the engine can easily make recommendations for items to users who have purchased any item from the store. Unlike the user-user collaborative filtering method, the item-item algorithm requires much fewer resources and less time to process the data (there is no need for similarity scores between users).

Content-Based System

Content-based filtering is a method that makes recommendations based on the description of an item and a profile of the user’s preferred choices. In a content-based recommendation system, the items are described with keywords, and the user profile is built to express the type of items the user likes. This means the algorithms try to make recommendations for items similar to the ones the user has liked in the past. In other words, the main idea behind this filtering method is that if the user likes a particular item, they will probably like a similar item. This can be best explained with movie and song recommendations since users tend to select movies and songs from a similar genre.  

The roots of the content-based approach are information retrieval and information filtering research. However, there is one major issue with this filtering method: the ability of the system to learn user preferences from user actions about one content source and replicate them to make recommendations for different content types. When the recommender engine makes predictions for similar content and content of the same type, and the user is already using it, the practicality of the recommender system is substantially decreased when other content types of other services can be recommended.   

Hybrid Model Approach

The practical use of hybrid model approaches showed that combining the best features of collaborative and content-based filtering allows the companies to get the best of both methods.

Hybrid approaches implement both predictions simultaneously, aiming to suggest a broader range of items to users and more accurate recommendations. One of the options is to make separate predictions and then combine the results. Furthermore, the hybrid model can be created by adding content-based capabilities to a collaborative-based approach and vice versa; or by unifying the methods into one model and overcoming the common problems in recommendation systems, cold start, and data scarcity problems.    

Challenges Involved With Online Recommendation Engines

The recent revolutionary technology in the internet domain has enabled static web pages to become omnipresent online web services through the social networking web. Simultaneously, and since web pages and recommender systems closely resemble each other, these engines matured while tackling the dynamic challenges of the increased number of users and items.

Recommender engines anticipate user requirements, wants, and needs before the user requires them or even thinks about a particular item. The efficiency of the recommender systems is proved by providing appropriate recommendations that match user preferences.

What is an online recommendation engine? A software solution in different online websites and applications that help the user to make decisions and help businesses to increase their revenue. This business tool is applicable in a wide variety of domains, and recommendation engines indeed make a huge difference. However, there is a fair share of challenging research issues to improve the capabilities of recommender systems.    

Synonymous Names

Synonymy, also known as synonymous names in recommender systems, is the tendency of very similar items to have different names or entries. In other words, there are products with different names but the same meaning, and most recommender engines are unable to discover latent associations and treat these products differently. 

For example, most recommender systems find it difficult to make the distinction between closely related items such as baby wear and baby cloth. This particularly refers to the collaborative filtering approach that cannot find the match between these two terms and thus is unable to compute their similarity. Different methods can solve the synonymy problem (automatic term expansion, the construction of a thesaurus, and Singular Value Decomposition (SVD)). However, the shortcoming is that some added terms may have different meanings from what is intended and ultimately might cause degradation of the performance of the recommender engine.

Scalability

Users generate data daily, and new products and services emerge nearly every day. Consequently, computation grows linearly with the increased number of users and items, which is overwhelming for these systems. Therefore, a large amount of computation power is required to calculate recommendations.

Some recommender approaches are practical with a limited dataset and might be unable to generate a satisfactory number of recommendations with an increased dataset volume. That is the reason why it is crucial to apply recommender methods that have the ability to scale up successfully with the increased dataset.   

Scalability is crucial in determining the type of recommender system the business needs. More complex systems require more people, which are harder to hire, and expensive commitments in the long term. However, real-world datasets contain massive changing data of user-item interactions, and advanced large-scale methods are required for this issue.    

Privacy

Websites and online applications with a wide range of content offered to users have extensive databases and require automated recommender systems. In order to offer appropriate products and services to the users, the engines need to provide a personalized selection of relevant items, such as movies, books, and songs. And to generate accurate recommendations, the recommender engines rely on detailed personal data on user preferences, including ratings, search and consumption history, and personal profiles.

Valuable and effective recommender systems rely on user privacy. In the process of creating proposals, these engines gather and process personal data, and the privacy risks associated with the process are often underestimated and ignored.

Many users need to be sufficiently familiarized with how much of their data is collected, where it is stored and for how long it is safely and securely managed, and if and when such data is sold to third parties. Privacy includes keeping a piece of information in its intended scope, which is defined by the size of the audience, the extent of usage allowed, and the duration. A privacy breach occurs when the information is moved beyond its intended scope. 

Among the diversity of information used in recommender systems, some of the most common data gathered are behavioral information, contextual information, domain knowledge, item meta-data, purchase history, recommendations, feedback, user attributes and preferences, and social information.  

Latency Challenges

There are thousands, if not hundreds new products and services released daily on the Internet. Without an effective recommender engine, online-based businesses might find it challenging to advertise new items without overwhelming the customers with unnecessary and repetitive advertising. On the other hand, due to the large number of items available online, users need help finding items on websites and applications that meet their needs. Recommender systems benefit both parties by predicting user preferences and recommending personalized items.  

The latency issue arises when many new items are frequently added to the recommender system database, but only the already existing products are recommended to users. This is because the newly added products still need to be rated. But, a combination of collaborative filtering method and category-based approach with user-item interaction successfully deals with this issue.   

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