# A Flexible Session-Based Recommender System for e-Commerce
## 2 Methods and Literature Review
### Purchase Intent Task  
In all likelihood, the first techniques tested to determine whether or not a user session in an e-commerce application is likely to end with a purchase were multilayer perceptron classifiers and simple Bayes classifiers [27]. [PERSON] [28] used data from an online book-store to evaluate SVM using a variety of factors (23), with a similar goal of classifying user sessions as either browsing-only or purchasing-related. The most effective SVM classifier showed high performance. It achieved a likelihood of predicting a purchasing session of about 95% and an overall prediction accuracy of 99%.  
Association rules and a k-nearest neighbor (k-NN) classifier were used by [29] to enhance their study and estimate the likelihood of a purchase. To predict purchase likelihood for two client groups--traditional consumers (accuracy 90%) and more diverse-novel customers--they employed basic association rule mining and other behavioral knowledge (accuracy 88%).  
The hidden Markov model (HMM) is another method that was tested. In fact, there are many web usage mining research efforts that have considered HMM to predict user behavior in several settings and for different tasks. Examples of its uses include deciding if a web search session was successful or not, establishing recommender systems [13], or making suggestion for the next point of interest in tourism websites. [PERSON] [2] presented a research study that was more related to our work; however, it primarily made use of HMM to understand customer intent in real-time in order to make web page adaption.  
Using user activity data, participants in the RecSys2015 competition attempted to estimate the assortment of goods that would be purchased during a session. The state of-the-art (SotA) solution for this issue remains the two-stage classifier provided by the competition winners [30]. While the second classifier predicts the things that will be purchased, the first classifier predicts whether at least one item will be purchased during the session or not. In this work, the session and click dates, click counts for individual things, and other category features of the sessions and objects were employed.  
Recurrent neural networks (RNN) were utilized by [31] to capture event dependencies and connections for user sessions of arbitrary length, both within and across datasets. Results from the RecSys15 challenge indicate that their solution performed admirably well. The key benefit of their approach is that it needs reduced domain- or dataset-specific feature engineering.  
Another RNN-LSTM-based system for analyzing online shopping behavior was present-ed by [9]. It had two parallel-operating components. The first predicted consumers' tendency to shop; however, this module employed machine learning classifiers such as random forest, support vector machines, and multilayer perceptron. RNN-LSTM is only used in the second module, which predicts the likelihood that users would leave a website without buying anything. In relation to the SotA, their purchasing intent module performed much worse. The accuracy of the second module, which predicted website desertion after a short window of three-user action, was almost 75%.  
A full connected long short-term network (FC-LSTM) for modeling the interactions between customers was tested by [32]. The same network models the nonlinear sequence correlations and cumulative effects between customer's browsing behavior. However, to attain better predictions, they use more features from user profiles, including purchase history and demographics.