The recommender system in e-commerce


A recommender system is a filtering process which consists of suggesting relevant information to users. Rather than showing all possible information to a user at once. In the case of an online store, the purpose of a recommender system is to offer the customer, products or services, adapted to his profile. This process filters the information to a subset based on methods such as Collaborative filtering, Neighbour-based Collaborative filtering, and Content-based filtering. 


Collaborative filtering methods for recommender systems are methods that are solely based on past interactions recorded between users and items to yield new recommendations. The main idea is that past user-item interactions are sufficient to detect similar users and similar items to make predictions based on the estimated proximities. The main advantage of collaborative approaches is that they require no information about users or items and, so, they can be used in many situations. 


Content-based methods, on the other hand, use additional information about users and items. These methods try to construct a model based on the available features on the items, that justify the observed user-items interactions.


Several factors have influenced the use of recommender systems. The growth in digitalization, the increasing use of online platforms, and the abundance of online information has accentuated the importance for businesses and organizations to offer the right information, whether that be a product, a service or content, to the right user at the right time. Recommender systems meet this need, and have many benefits such as improve customer experience, not exclusively through relevant information, they additionally offer the correct advice and direction. Thus, engage and increase user interaction, and create the ability of tailoring and personalizing offers to users, which could ultimately lead to increase revenue depending on the business. 


At Valkuren, we implemented this recommender system for an e-commerce platform to optimize the consumer experience on our client’s website. We used the predictive method to improve the product offering to consumers based on their search using purchase history and estimated proximity.


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How to carry out a data science project? (Part 2)


Step 4: Model Data

We could separate this “model data” step in 4 different steps: 



      1. The feature engineering is probably the most important step in the model creating process. The first thing to define is the term feature: are called feature the raw data as received by the learning model. The feature engineering is therefore, all the actions carried out on the raw data (clearing them, deleting null data, deleting aberrant data) before these data are taken into account by the algorithm, and thus the model. In summary, feature engineering is the extraction of raw data features that can be used to improve the performance of the machine learning algorithm
      2. The model training is the action of feeding the algorithms with datasets to start learning and improving them. The ability of machine learning models to handle large volumes of data can help manufacturers identify anomalies and test correlations while searching the entire data stream for models to develop candidate models.
      3. The model evaluation consists of assessing the created model through the output given by the model after having process data through the algorithm. The aim is to assess and validate the results given by the model. The model could be seen has a black box; you have the input that are given to the model algorithm (the dataset) in the model training and the output that are asses during the model evaluation. After having assess the results, you could optimize your model in the previous step.
      4. The model selection is the selection of the most performing and adapted model from the set of candidate model. This selection depends on the accuracy of the results given by the model. 

Step 5: Interpret results  

The main point about interpreting results is to represent and communicating results in a simple way. Indeed, after having process the previous step results could be heavy and hard to understand.

In order to make a good interpretation of your results, you have to go back to the first step of the data science life cycle that we have cover in our last article, to see if your results are related to the original purpose of the project and if they are any interest in addressing the basic problem. Another main point is to see if your results have sense. If they are and if you answer pertinently to the initial problematic, then you likely have come to a productive conclusion.  

Step 6 : Deployment

The deployment phase is the final phase of the project life cycle of a data science project. It consists in deploying the chosen model and applying new data to it. In other words, putting its predictions available to user or service system is known as deployment

Although the purpose of the model is to increase understanding of the data, the knowledge gained will need to be organized and presented in a way that the client can use and understand it. Depending on the needs, the deployment phase may be as simple as producing a report or as complex as implementing a reproducible scientific data process. 

By following these steps in your data science project process, you make better decisions for your business or government agency because your choices are backed by data that has been robustly collected and analysed. With practice, your data analysis gets faster and more accurate – meaning you make better, more informed decisions to run your organization most effectively. 


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