More about Machine Learining
For a search engine to get optimal user satisfaction and improved product publicity, a machine learning model needs to be played out. A perfect machine learning model would find the optimal balance between the user intent of search and products to be recommended. This model should be able to get the straight line of y=mx+c between user intent and products and only then will the search engine be able to project the products that are very relevant to users and gain the attention of users by directing them in required direction.
To get a better understanding of generating the optimal machine learning model that extracts relevant products to search engines based on user intent, let's look through different models of machine learning.
Linear Model: This model is straightforward and works based on a linear combination of features. Training data is used to understand the relationships between different data components and these relationships will be used to predict the data. As the model itself is very simple, it is very easy to understand as well as understand the data. It is also less prone to unwanted data. But for the model to achieve near perfection results, it needs to process large amounts of training data to completely understand the data relationships. This model is typically used for credit risk scoring, forecasts of sales, estimates for price changes of products etc., Google play also uses this model for pushing out the apps to be displayed based on user behavior.
For a real world machine learning model several components are needed to make it more ideal and usable, Most important components are Configurations, Data collection, Feature extraction, Data verification, Machine resource management, Analysis tools, Process management tools, Serving infrastructure, Monitoring
Constant monitoring is needed for machine learning models once the model is launched to verify its results and also to make changes as necessary. For a model which is based on user intent of search and product, the user picking the suggested product on the search engine’s results page is a confirmation that the prediction of the machine learning model is accurate.
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