# A Flexible Session-Based Recommender System for e-Commerce
## 5 Conclusions and Future Directions  
Nowadays, many retail sales come from e-commerce web applications. Furthermore, e-commerce exploded during the pandemic, a business trend which is expected to last. Subsequently, the effectiveness of e-commerce solutions has become an important challenge for successful e-businesses. For large e-businesses that have tens of thousands or millions of users making repetitive visits and purchases, conventional recommender systems that rely on user ID and products views information are viable and very effective. However, these conventional systems cannot sufficiently address other widespread scenarios such as: Smaller e-businesses, in which it is difficult to collect user ID information for privacy and other reasons. More dynamic shopping environments that need to understand the user's intent and preferences at a certain time point, without being obscured in long-term historical shopping behaviors. Capture a short temporal preference shift in user buying behavior that is represented only in the intrinsic nature of each specific session. The scenarios outlined above show the need for SBRS. In other words, in many e-commerce applications and environments, it is necessary to learn user behavior patterns using sessions as the main transaction unit. Having identified the main requirements of SBRS and the main tasks, our work in the FFRES project was driven by the need to investigate, develop and evaluate several methods for SBRS. After many lab experiments using our SBRS methods, but also testing their operation in real e-commerce application, our main conclusions related to the next-item and next-basket recommender tasks are as follows:  
1. When used in next-item recommendations based on sessions, LSTMs perform noticeably better than embedding approaches. Furthermore, these approaches do not require adjusting and are simpler to implement. Additionally, these methods are easier to apply without much tuning. Furthermore, their dynamic nature, inherently models the behavior of explorative browsing that usually occurs in e-commerce applications; therefore, they are better suited for this task. 2. In our investigation, we used LSTMs, although they were not as effective at predicting the last basket. The performance of Item2 Vec was just slightly superior to our best LSTM implementation (the one that uses the extra embedding layer tailored to RNNs). Figure 7: Click-through rate achieved by our recommender method. Additionally, LSTM approaches exceeded Word2 Vec performance, albeit not on the same level as in the next-item task. 3. Combining several approaches has not always produced satisfying results. It significantly depends on whether textual data (Doc2 Vec) can enhance the value of item-based recommendations. This observation inspires the concept of customized and pluggable recommenders that select the most appropriate recommendation algorithm in real time (for example, between Item2 vec or LSTM). For each user action, we think a model may be trained to dynamically select one recommender technique, or possibly a combination of recommenders. We intend to go deeper into this topic. 4. Practical practices like category-based re-ranking could be useful, but only if recommenders perform poorly. 5. Graph-based methods can be a good compromise between effectiveness and efficiency. They can collect data from ongoing user sessions continually and make them available to the recommendation algorithm directly without any training phase. 6. Efficiency and scalability are critical issues that should be very carefully considered, especially in large-scale applications. Additionally, there is a need for methods supporting incremental training. We plan to investigate this issue further in future research. 7. During our collaboration with the e-shop top produce as our demonstrator, we realized that an important functional requirement for SBRS is to accommodate business rules that are required by the management of an e-commerce application.