The main aim of recommender systems is to stimulate the demand and engage your customers. In short, Recommender systems use customer behaviour to predict the customers’ intent to recommend products or services. Recommender systems are beneficial to both the business and its customers. In fact, 35% of Amazon’s revenues come from their recommender systems and so do 80% of content watched on Netflix.
Let’s take a look at two different filtering approaches applied to the personalization of product recommendations. The first filtering approach is content-based filtering. The second is collaborative filtering.
A content-based recommendation system uses customers’ demographic profiles such as age, gender, and location to generate recommendations.
Collaborative filtering uses customers’ present and past behavior to generate recommendations. It churns out recommendations using visitors’ browsing and preference history to form a visitor-product matrix. For example, if John is interested in product A and product B, and if John falls in a segment that is interested in product A, product B, and product C, the collaborative filtering predicts that John might also be interested in product C and hence recommends it.
Each of the above-mentioned systems has its pros and cons. While content-based recommender systems are cheap, it uses generic information such as age, gender, location, etc. to churn out recommendations, and hence are not personalized.
Collaborative filtering, although personalized, is broad in its recommendations as it does not consider what makes that product attractive to that visitor. Moreover, there is also the problem of a cold start with collaborative filtering (that is, irrelevant recommendations for a new visitor whose interaction has just begun). Also, collaborative filtering is bad at dealing with data sparsity (that is, 100’s of products on the sites and very few visitors’ interactions with many of those products).
Often, the above two recommender systems are combined into a hybrid system. These hybrid systems generate recommendations by combining both content-based and collaborative filtering techniques. Even though combining the systems slightly improves their effectiveness, the fundamental issues in these two systems still remain.
Solving the fundamental issues in these two approaches requires an entirely different approach that is now made possible with the application of cutting-edge AI. These problems can be solved by deep learning. Deep learning-based recommendation systems can take personalization to a different level altogether.
What makes deep learning-based recommendation systems different from the traditional recommender systems is their ability to analyze complex interaction patterns between the visitor and the products and construct additional features automatically, leading to recommendations that precisely match the visitors’ intent and affinity. In short, deep learning-based recommendation systems recommend products that are hyper-personalized for that visitor. Also, Convolutional Neural Networks (CNNs), a field of ANN, can solve the cold start problem. Recurrent Neural Networks (RNN), a field of ANN, can not only help build session-based recommendations for new visitors or visitors who have not logged in but even predict what these visitors can buy next based on their recent click history.
Deep learning is a sub-field of Machine Learning (ML) that uses the Artificial Neural Network (ANN). Some of the characteristics of deep learning that differentiate it from traditional ML are non-linear transformation, sequence modeling, and representation learning.
Although deep learning as a field is quite old (it was discovered in the year 1943), there are many reasons why deep learning-based recommendation systems are recent. First, the traditional recommendation systems require less data as compared to deep learning ones. Second, deep learning needs a high-capacity infrastructure to handle sequential data processing. Third, deep learning needs more data to train. But with the advent of Big Data and massively parallel processing systems, vast and varied data can be collected, stored, and analyzed cost-effectively in real-time.
It is not to say that deep learning-based recommendation systems are without shortcomings. For example, many complain that a deep learning-based recommendation system operates as a black box. Still, the benefits of a deep learning-based recommendation system hugely outweigh its drawbacks by a considerable margin.
Yes, it is also possible to combine collaborative filtering with deep learning algorithms to build a recommendation product that gets the best of both worlds.