A Beginner’s Guide to Sequential Recommendation Systems
There are different types of recommender systems used in a variety of applications which aim to facilitate user interactions with the system and bring multiple benefits to businesses. Among these, sequential recommendation systems model the sequential behavior of users and recommend items in a sequence. In this article, we’ll take a close look at the sequential recommender system and try to understand how it works. The main points to cover in this article are listed below.
- Types of recommendation systems
- What is a sequential recommendation system?
- How are sequential recommendation systems different from others?
- When to use sequential recommendation systems?
- How it works?
- Data characteristics and challenges
- Applications of the sequential recommendation system
Before we move on to sequential recommender systems, let’s understand the types of recommender systems.
Types of recommendation systems
In general, there are two types of recommender systems:
The image above shows the difference between collaborative and content-based filters. Collaborative filtering is the process of predicting your preferences based on the similar interests of other users. This means that if people A and B have a similar interest in the product, then they are likely to have similar interests in other products as well.
Content-based filtering systems predict your preferences based solely on your profile. It tries to match what you liked previously. Consider that if you list songs from a certain singer only on Spotify, they will try to recommend songs from the same singer to you.
What is a sequential recommendation system?
Sequential recommendation systems try to understand user input over time and model in sequential order. User input interaction is primarily sequence dependent. This means that if a person books a flight, they also book a taxi for the destination and book a room. This information is stored in order. If someone else is booking a flight and a taxi, the system will give recommendations for hotel or room reservations. User preferences and article popularity are dynamic or change over time.
For example, we can see that more and more people are buying wireless headphones and some people tend to buy new phones every year. By these trends, the popularity of some phones or headphones or any other product is changing. Such dynamic profiling is of great importance for the precise profiling of a user or item for more precise recommendations and they can only be captured by sequential recommendation systems.
How are sequential recommendation systems different from others?
In traditional recommendation systems such as collaborative filtering and content-based filtering models, item interactions are static and only capture general user preferences. But in the sequential recommender system, the element interaction is a dynamic sequence.
When to use sequential recommendation systems?
In the digital world, where everything is going digital, sequential user behavior is a wealth of information. A sequential recommendation system can be used in the field of electronic commerce to determine a person’s historical behavior and predict a continuous change in preference.
How it works?
Typically, a sequential recommendation system takes a sequence of information from users and tries to predict subsequent user-item interactions that may occur in the near future. Given a sequence of user-element input interactions, the model will rank the best candidate elements. This element is generated by maximizing a utility function value.
Or F is a utility function to produce the ranking score, S is a sequence of user-item interactions and takes the value as a list.
The figure above represents a sequential recommendation system where cI is the type of behavior and oI is an object of behavior. Deep neural networks are mainly used for recommender systems, which is very effective in capturing complete relationships on different entities in sequence.
The figure above shows the categorization of sequential recommendation system approaches from a technical perspective. Some examples include RNN based sequential recommender system, CNN based sequential recommender system, GNN based sequential recommender system.
Data characteristics and challenges
As customer behavior, for example, purchasing is diverse and complex in a real world scenario, different customer data entry characteristics will bring different challenges. Some of them are listed in the table below.
Applications of the sequential recommendation system
Sequential recommender systems add more value to many industries and the system itself is very user friendly as it recommends next steps to be taken.
It is used in the e-commerce industry to recommend the next relevant product to customers.
For example, if Person A purchases athletic shoes, the system will recommend socks. This will help the business to generate more revenue because the recommended product is relevant. It is also used in the tourism / travel industry. When booking a ticket for a vacation, the system will recommend the next step, such as booking the top rated hotels in the area.
In this article, we have a basic understanding of sequential recommendation systems and could understand how it works. We also had a clear understanding of the difference between traditional and sequential recommendation systems. Ultimately, we also discussed the challenges faced by the sequential recommendation system as well as its applications.