In every line of business, anticipating customers' needs puts you in a position to serve them better. The goal of every customer experience and point of interaction should always be to exceed their expectations and encourage them to explore more items to purchase.
The tricky part is to accomplish this conveniently and seamlessly so that it doesn’t feel forced. This is where recommendation systems come in.
Understanding Recommendation Systems
Recommendation systems, powered by artificial intelligence (AI) and machine learning (ML), are the foundation for most personalized customer experiences, especially online. They are computer programs capable of analyzing and matching large groups of users to specific items that would appeal to them. This is known as “discovery,” and it is the most important function of a recommendation system.
The global market size for recommendation engines is predicted to grow at an impressive compound annual growth rate of 32.1% between 2022 and 2031, from a value of $2.7 billion to $43.8 billion. The many ways that recommendation engines can be put to use have contributed to this massive interest in the technology.
Think of every time you found a hidden gem of a TV show thanks to a suggestion from Netflix’s algorithm or the last impulse purchase you made after spotting a tempting recommended deal on Amazon. That’s the power of recommendation systems in action. User behavior helps the program determine user preferences. Being served what they’re looking for draws more users, which in turn grows the dataset and allows the program to make even more accurate recommendations.
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What is Text-Based Recommendation?
The amount of textual data on the Internet grows by magnitudes every day. Hundreds of blog posts, news articles, emails, texts, and other forms of data are posted every minute. To navigate this massive labyrinth of text data, we need software that can search through and provide relevant results in as little time as possible.
Text-based recommendation engines accomplish this by extracting features from textual data. This involves converting words and phrases into numerical values, analyzing them, and delivering search results and recommendations in minimal time.
Analyzing text data allows AI and ML systems to better understand meaning and context. Semantic analysis and natural language processing (NLP) tools help software better understand and process this data.
Text-Based Recommendation in Different Industries
As mentioned earlier, recommendation engines are versatile tools with many possible uses. Text-based recommendation systems can be leveraged in several industries to improve user experience and discovery rates.
Let’s take a look at some examples of how text-based recommendation is being applied in a few different contexts:
Ecommerce
The most common example of text-based recommendation systems is in e-commerce. User profiles, reviews, and product descriptions can be used to recommend useful products to shoppers.
For instance, if you purchase formal leather shoes through Amazon, the recommendation engine can read the product description and learn whether it was sold with a pair of socks. If not, the engine will recommend sock options before you complete your purchase.
Media and Journalism
News websites and publications can implement text-based recommendation systems to better serve content to their readers. These systems can analyze several topics in the articles read by a single reader, compile a list of their interests, and suggest more articles accordingly.
Social Networks
Using text-based recommendation, social media algorithms can connect users who share common interests, attend the same schools, or work at the same place.
Search Engines
Every search engine user’s meta-data, such as location and browsing history, takes the form of textual data, which can be utilized to deliver highly relevant results on user queries.
The examples given above are only a sample of the many diverse applications of text-based recommendation systems. The utility of these tools cannot be understated. However, to learn how they can add maximum value to your business, you need to know a little bit about how they work.
Different Types of Text-Based Recommendation Methods
Recommendation systems have existed since the 20th century. The earliest version that resembles our current definition was the computer librarian software Grundy, launched in 1979. Over the decades, the technology has advanced and become even more streamlined.
Today, there are several different types of text-based recommendation methods:
1. Content-Based Filtering
This method matches keywords that the user searches for with those contained in item descriptions. This initial dataset is used to recommend other relevant items.
Content-based filtering systems primarily rely on product information to craft recommendations for users. This method is fairly straightforward and doesn’t require data from multiple users. It also helps in discovery, as new items similar to older ones are offered as suggestions.
However, the most glaring limitation of content-based filtering systems is that they can only recommend similar items to past purchases.
2. Collaborative Filtering
Collaborative filtering systems collect user activity and behavior data. Users are then grouped on the principle that those with similar histories will choose the same products. An individual user’s recommendations will be influenced by others’ behavior.
This method doesn’t rely on product information. Instead, it compares users and items for similarity function, predicts user ratings for novel items, and generates recommendations based on those predictions. Collaborative filtering methods are great for large groups but require advanced computing power for complex calculations.
3. Hybrid Recommendation Methods
While both content-based and collaborative filtering methods have limitations, their strengths complement each other well. The former focuses on finding similar products, while the latter focuses on finding similar users.
When used together, content-based filtering and collaborative filtering methods can significantly boost discovery rates. This is why many modern recommendation systems use hybrid approaches that combine the best elements of both.
Data Preprocessing for Text-based Recommendations
Converting raw text into relevant information or usable data is a multi-stage process, which we will examine in more detail.
1. Text Tokenization
One of the most critical steps in analytics is text tokenization, which involves breaking down a body of text into smaller units known as tokens. For example, a senternce will be divided into words, a paragraph will be split into sentences, and so on. The goal is to create meaningful segments for analysis.
2. Text Cleaning
To enable efficient NLP functions, textual data must also be cleaned up before processing. Cleaning text for processing involves several steps:
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Case conversion: converting all letters to lowercase
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Stemming and Lemmatization: reducing words to their base forms by removing modifiers like affixes and suffixes, and considering their usage in context
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Stopword removal: eliminating superfluous words, such as "the," "a," "and," words from the text reduces the size of the dataset, removes distractions, and reduces noise
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Punctuation removal: removing unnecessary characters from the text data, making it cleaner and easier to process
3. Feature Engineering
This process involves creating features that NLP models can rely on to classify raw data. Common features in text analysis include the number of words, number of punctuation marks, use of rare words, average sentence length, etc.
More features help a machine learning approach develop greater accuracy when classifying items.
Modeling Approaches
Today, text-based recommendation systems are powered by AI. To understand how AI works while performing text analysis and generating recommendations, we need to examine the modeling approaches used.
Machine Learning Models
These AI models are trained on raw data, which is transformed into vectors that help the software identify information about the text and classify it accordingly. Then, the AI can use inferences from its training data when confronted with unseen texts and accurately predict their features.
ML methods can be supervised or unsupervised, depending on the algorithms used.
Deep Learning Models
Going a step beyond ML models are deep learning models. They take data inputs from various sources and compute them through a hidden layer of artificial neural networks (ANNs), which are algorithms that extract information and learn from new data. They rely on much larger sets of training data than traditional ML models, so they generate much more accurate and semantically rich text analysis and recommendations.
There are two main points of difference between traditional ML and deep learning models:
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ML models may require human intervention; deep learning models are entirely independent and rely on ANNs.
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Traditional ML models use thousands of data points for training data, but deep learning models operate on a much larger scale, using millions of data points.
Evaluation Metrics
Judging a recommendation system's performance in a vacuum can be difficult. A clear set of calculable metrics will demonstrate whether a given system meets expectations and aids users' discovery.
There are several ways to measure the efficacy of a recommendation system, and here are some of the most important metrics to track:
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Precision: Measures how man
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y recommended items were relevant to the user. It calculates the percentage by dividing the number of selected and relevant items by the number of selected items.
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Recall: Also known as sensitivity, this measures how many relevant items were selected by users. It can be calculated by dividing the number of selected and relevant items by the number of relevant items.
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F1-Score: A recommendation system’s F1-score measures the balance between precision and recall percentages. The higher the F1-score, the more likely it is that any given recommendation list will contain the most directly relevant item and all other relevant items.
Other metrics that can also be measured include, but are not limited to:
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Accuracy
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Click-through rate (CTR)
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Specificity
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Diversity
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Serendipity
When evaluating the performance of your recommendation engine, it’s important to study which metrics are most important to your business goals.
Challenges and Future Directions
An effective text-based recommendation system can do wonders for your business by increasing sales, improving customer experience, and attracting new buyers. However, there are some challenges to achieving excellence with these complex engines.
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Data Sparsity: In addition to being unstructured, raw text data can also be incomplete in many cases. This presents challenges for recommendation systems trying to make predictions based on this data.
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Cold Start Problem: A recommendation engine needs a wealth of training data to make an accurate prediction. This can be a problem in the early stages of implementation when there simply isn’t enough user activity to train the AI model.
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Scalability: A recommendation engine that works well for a startup with 5,000 customers may not be suitable for an enterprise catering to an audience of 500,000 or more. Finding the right recommendation engine that can grow with a business is often a challenge.
Further advances in AI and ML technologies promise to sharpen recommendation system capabilities in the coming years. As generative AI and Natural Language Processing algorithms evolve, these recommender systems will be able to craft their own qualified responses to user queries and deliver results without relying on human intervention.
The Recommendation System in Action
You can observe a powerful recommender system at work all over the internet. Several of today’s most popular websites rely on text-based recommendations to parse through their raw data and deliver a quality experience to their customers.
Netflix is one of the best examples. Its algorithm leverages user behavior, including viewing history and ratings, collaborative filtering, and content-based filtering based on genres watched and credited performers to push certain movies and TV shows to their users.
Spotify is a popular app that uses collaborative filtering to recommend songs based on the listening activity of similar users. It also uses convolutional neural networks to extract audio features from songs, which allows it to compare songs to find similarities and offer new recommendations.
The world’s biggest social media platforms, such as YouTube and Instagram, also have powerful recommendation engines in place.
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Text-Based Recommendation Methods for Your Business
Every time your business communicates with a customer, they offer up text data that can be used to tailor their experience. Text-based recommendation systems are powerful tools to add value to your customer experience, especially if your business website uses a chatbot or live chat service.
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