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Recommender systems are one of the most successful and widespread application of machine learning technologies in business. You can find large scale recommender systems in retail, video on demand, or music streaming.
A Web Base user-item Movie Recommendation Engine using Collaborative Filtering By matrix factorizations algorithm and thus the advice supported the underlying concept is that if two persons both liked certian common movies,then the films that one person has liked that the opposite person has not yet watched are often recommended to him.
A recommender system is a type of information recommend movies to user according to their area of interest. Our recommender system provide personalized information by learning the user‟s interests from previous interactions with that user[2]. In pattern recognition, the knearest neighbours algorithm (k-NN) is a flexible method used for classification. In following cases, the input consists of the k closest examples in given space. If k = 1, then the object is simply assigned to the class of that single nearest neighbour.
Algorithms Implemented
- Content based filtering
- Collaborative Filtering
- Memory based collaborative filtering
- User-Item Filtering
- Item-Item Filtering
- Model based collaborative filtering
- Single Value Decomposition(SVD)
- SVD++
- Memory based collaborative filtering
- Hybrid Model
- Content Based + SVD
Technologies Used
Web Technologies
Html , Css , JavaScript , Bootstrap , Django
Machine Learning Library In Python3
Numpy , Pandas , Scipy
Database
SQLite
Requirements
python 3.6
pip3
virtualenv
Read Before Purchase :
- One Time Free Installation Support.
- Terms and Conditions on this page: https://projectworlds/terms
- We offer Paid Customization installation Support
- If you have any questions please contact Support Section
- Please note that any digital products presented on the website do not contain malicious code, viruses or advertising. You buy the original files from the developers. We do not sell any products downloaded from other sites.
- You can download the product after the purchase by a direct link on this page.
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