Improving Sequential Recommender Systems with AutoMLP: A Solution to Long/Short-Term Interest Identification

Long/Short-Term Interests

In recent years, sequential recommender systems have emerged as a popular approach for providing personalized recommendations to users. These systems take into account the order in which items are consumed by users, enabling them to identify long-term and short-term interests of users and predict their future preferences accurately.

However, while sequential recommender systems have proven to be effective in some cases, they suffer from several limitations. One of the primary challenges with these systems is the difficulty in identifying and extracting meaningful features from user behavior data. Additionally, these systems struggle to cope with the dynamic nature of user preferences and the ever-changing nature of the items being recommended.

Fortunately, recent advancements in machine learning have led to the development of AutoMLP, an automated machine learning technique that can help to overcome the limitations of traditional sequential recommender systems. AutoMLP uses a combination of deep learning and reinforcement learning techniques to automatically identify the best features and models for a given problem.

By leveraging AutoMLP, we can create more accurate and effective sequential recommender systems that can learn and adapt to the changing preferences of users over time. This approach can help businesses to provide more personalized recommendations to their users, leading to increased engagement, loyalty, and ultimately, revenue.

In this blog post, we will delve deeper into the limitations of traditional sequential recommender systems and explore how AutoMLP can be used to improve the accuracy and effectiveness of these systems. We will also discuss the various applications of AutoMLP in the field of recommendation systems and the challenges associated with implementing this approach in practice. Stay tuned to learn more about this exciting new development in the field of recommendation systems.

Long/Short-Term Interest Identification

Identifying long and short-term interests in sequential data can provide a wealth of insights and opportunities for businesses and organizations. By analyzing patterns and trends in data, it is possible to identify both short-term interests that may drive immediate actions, as well as long-term interests that may shape future behaviors.

Short-term interests refer to the immediate concerns and desires of individuals, which can change quickly and unpredictably. They may be influenced by a wide range of factors, such as current events, social media trends, or personal circumstances. Identifying short-term interests can help organizations respond quickly to changing demands, adjust marketing strategies, and tailor products or services to meet immediate needs.

On the other hand, long-term interests reflect deeper underlying motivations and preferences that may persist over time. These may be shaped by factors such as personal values, lifestyle choices, or demographic characteristics. Identifying long-term interests can help organizations develop more effective long-term strategies, build stronger customer relationships, and improve overall customer satisfaction.

The importance of identifying both short and long-term interests cannot be overstated. By understanding what drives customer behavior in the short and long term, organizations can make more informed decisions and allocate resources more effectively. For example, by identifying short-term interests, a retailer can stock up on popular products or offer limited-time discounts to attract more customers. By identifying long-term interests, a retailer can invest in product development, customer service, or brand building initiatives that will pay off over time.

To identify long and short-term interests in sequential data, organizations can use a range of analytical techniques such as machine learning, data mining, and natural language processing. These techniques can help identify patterns, correlations, and trends in data, and provide insights that might be difficult to discern through manual analysis.

Improving Sequential Recommender Systems with AutoMLP

AutoMLP, or Automated Multi-Layer Perceptron, is a machine learning algorithm that has gained widespread attention in the field of sequential recommender systems. Before delving into how AutoMLP works, it is important to understand the concept of MLP.

MLP is a type of artificial neural network (ANN) that consists of multiple layers of interconnected nodes, each of which performs a mathematical operation. These layers are typically an input layer, one or more hidden layers, and an output layer. MLPs are widely used in supervised learning tasks, such as classification and regression, because they are capable of handling non-linear relationships between inputs and outputs.

Now, coming to the working of AutoMLP, it is a technique that automates the process of designing and training MLPs for a given task. AutoMLP algorithms use a combination of evolutionary algorithms, reinforcement learning, and other optimization techniques to search for the best architecture, hyperparameters, and training procedures for a given dataset.

AutoMLP is a powerful tool for improving sequential recommender systems in a number of ways. First, it can optimize the performance of these systems by automatically finding the best architecture and hyperparameters for multi-layer perceptron models. This leads to faster and more accurate predictions, which can greatly enhance the user experience.

Second, AutoMLP can improve the accuracy of sequential recommender systems in predicting both long and short-term interests of users. By analyzing user behavior over time and across multiple sessions, AutoMLP can identify patterns and preferences that would be difficult for human analysts to detect. This leads to more accurate recommendations and a better understanding of user behavior.

Third, AutoMLP can help in dealing with the cold start problem, which is a common challenge in recommender systems where new users or items have little or no historical data. By automatically designing and training MLPs on such sparse data, AutoMLP can enable recommender systems to make accurate predictions for new users and items.

Finally, AutoMLP can enhance customer satisfaction through personalized recommendations. By analyzing user data and tailoring recommendations to each individual, businesses can create a more engaging and personalized experience for their customers. This can lead to increased loyalty and customer retention.

Benefits of AutoMLP in Sequential Recommender Systems

By automating the process of model selection and hyperparameter tuning, AutoMLP is able to deliver a wide range of benefits that are driving its adoption across industries.

One of the most significant benefits of AutoMLP is that it leads to better recommendations. By leveraging powerful machine learning algorithms, AutoMLP is able to analyze vast amounts of data to identify patterns and trends that can inform recommendations. This results in more accurate and relevant recommendations that are better suited to each individual user.

Another key benefit of AutoMLP is that it enhances the user experience. By providing personalized recommendations that are tailored to the needs and preferences of each user, businesses can create a more engaging and satisfying experience for their customers. This can help to build brand loyalty and increase customer satisfaction.

In addition, AutoMLP has been shown to increase sales and revenue for businesses that use it in their sequential recommender systems. By providing more accurate and relevant recommendations, businesses are able to increase the likelihood that customers will make purchases, leading to higher sales and revenue.

Finally, AutoMLP provides a competitive advantage for businesses that adopt it in their sequential recommender systems. By leveraging cutting-edge technology to deliver better recommendations and enhance the user experience, businesses are able to stay ahead of the competition and differentiate themselves in the marketplace.

Use Cases of AutoMLP

Some of the most notable use cases of AutoMLP are in e-commerce platforms, streaming services, online learning platforms, and social media platforms.

E-commerce platforms are a natural fit for AutoMLP, as they rely heavily on recommendation engines to drive sales. By leveraging AutoMLP to deliver more accurate and relevant recommendations, e-commerce platforms can increase sales and improve customer satisfaction. For example, an online clothing retailer could use AutoMLP to recommend clothing items based on a user’s past purchases, search history, and browsing behavior.

Streaming services are another area where AutoMLP can have a significant impact. By analyzing user behavior and preferences, AutoMLP can deliver personalized recommendations for movies, TV shows, and other forms of digital content. This can help streaming services to retain subscribers and increase engagement.

Online learning platforms can also benefit from AutoMLP, as it can help to deliver personalized recommendations for courses and other learning resources. By analyzing user behavior and preferences, AutoMLP can recommend courses and resources that are better suited to each individual learner. This can lead to increased engagement and improved learning outcomes.

Finally, social media platforms can leverage AutoMLP to deliver more personalized content to users. By analyzing user behavior and preferences, AutoMLP can recommend content that is more likely to be of interest to each individual user. This can increase engagement and retention on social media platforms, leading to higher revenue and greater user satisfaction.

Challenges and Limitations of AutoMLP

Despite the many benefits of AutoMLP, there are several challenges and limitations associated with this technology that businesses must be aware of.

One of the biggest challenges is data availability. To train an AutoMLP model, businesses need to have access to large amounts of high-quality data. This can be a significant challenge, particularly for small businesses or those operating in niche markets.

Another challenge is the time required to train and test AutoMLP models. Depending on the complexity of the data and the size of the dataset, training an AutoMLP model can take days or even weeks. This can be a significant limitation for businesses that require fast, real-time recommendations.

Finally, there is the issue of technical expertise. Developing and deploying an AutoMLP model requires a significant amount of technical expertise, including knowledge of machine learning algorithms, neural networks, and programming languages like Python. This can be a significant barrier to adoption for small businesses or those without in-house technical expertise.

Despite these challenges, many businesses are finding ways to overcome these limitations and leverage AutoMLP to drive business results. Some businesses are partnering with external AI and machine learning consulting services, such as Sinfa Consulting, to develop and deploy AutoMLP models, while others are leveraging cloud-based machine learning platforms to reduce the technical expertise required.


Sequential recommender systems are essential in providing personalized recommendations to customers, but identifying long and short-term interests can be challenging. AutoMLP is a promising solution that optimizes the performance of sequential recommender systems by accurately predicting long and short-term interests. The use of AutoMLP can improve customer satisfaction and increase sales, providing a competitive advantage to businesses.