GROUP PROJECT
A recommendation engine is a class of machine learning which offers relevant suggestions to the customer. Before the recommendation system, the major tendency to buy was to take a suggestion from friends. But Now Google knows what news you will read, Youtube knows what type of videos you will watch based on your search history, watch history, or purchase history. A recommendation system helps an organization to create loyal customers and build trust by them desired products and services for which they came on your site. The recommendation system today are so powerful that they can handle the new customer too who has visited the site for the first time. They recommend the products which are currently trending or highly rated and they can also recommend the products which bring maximum profit to the company. For this particular project we have used collabrotive filtering.
Collaborative-based filtering technique: Recommendation systems in various applications have tried to provide users with an accurate recommendation to meet the needs of the user and to bring higher benefits to companies. Collaborative filtering is an effective and well known technology in recommendation systems. Many web sites, particularly Ecommerce sites, have used collaborative filtering technology in their recommendation systems to personalize the browsing experience for each user.Collaborative-based filtering technique.Recommendation systems in various applications have tried to provide users with an accurate recommendation to meet the needs of the user and to bring higher benefits to companies. Collaborative filtering is an effective and well known technology in recommendation systems. Many web sites, particularly Ecommerce sites, have used collaborative filtering technology in their recommendation systems to personalize the browsing experience for each user.
K-Nearest Neighbor is one of the simplest Machine Learning algorithms based on Supervised Learning technique.K-NN algorithm assumes the similarity between the new case/data and available cases and puts the new case into the category that is most similar to the available categories.K-NN algorithm stores all the available data and classifies a new data point based on the similarity. This means when new data appears then it can be easily classified into a well suited category by using K- NN algorithm.The KNN algorithm at the training phase just stores the dataset and when it gets new data, then it classifies that data into a category that is much similar to the new data.
Cosine similarity metric finds the normalized dot product of the two attributes. By determining the cosine similarity, we would effectively try to find the cosine of the angle between the two objects. The cosine of 0° is 1, and it is less than 1 for any other angle.It is thus a judgment of orientation and not magnitude. Two vectors with the same orientation have a cosine similarity of 1, two vectors at 90° have a similarity of 0. Whereas two vectors diametrically opposed having a similarity of -1, independent of their magnitude.Cosine similarity is particularly used in positive space, where the outcome is neatly bounded in [0,1]. One of the reasons for the popularity of cosine similarity is that it is very efficient to evaluate, especially for sparse vectors.
-> The main aim of our project is to create a system which will improve customer interaction by providing them with the best book suggestions by analyzing their preference and interests.
To create a machine learning model to recommend relevant books to users based on popularity and user interests.
In addition to the ML Model prediction, we also have taken into account the book recommendation for a totally new user.
1. Books – first are about books which contain all the information related to books like an author, title, publication year, etc.
So based on all these three files we can build a powerful collaborative filtering model. let’s get started.
Root Mean square is the standard deviation of the residuals. RMSE is a measure of how spread out these residuals are. In other words, it tells you how concentrated the data is around the line of best fit. Since the errors are squared before they are averaged, the RMSE gives a relatively high weight to large errors. This means the RMSE is most useful when large errors are particularly undesirable.It indicates the absolute fit of the model to the data.Provides average model prediction error in units of the variable of interest.They are negatively-oriented scores, which means lower values are better.
Figure 1: HomePage displays the home page of our website. The page consists of a bar which consists of all the book names available to us in the book_pivot file followed by a recommend button.
Figure 2: Our Chosen Book displays the book selected by the viewer when the person clicks on the recommend button. The book chosen by the person in the above fig was Birthright and hence all the details related to that book get displayed on the screen.
