HYBRID RECOMMENDER SYSTEM
Hi everyone.
Recommendations that we saw on different platform based on our watched, listened etc. were really interesting for me.
Because how to suggested me new movies or songs that mostly I would like to take a look. It’s nice story to analyse and get information from this subject.
When interesting part is partly over because I gained perspective a little bit about this subject with VBO. After that session, more interesting part has already begun, because it was just an entered to the ocean. It’s really great ocean to surprise me every step that I took.
I want to kindly share with you project that I worked on.
What is the recommender systems and how to work?
Recommender systems aim to predict users’ interests and recommend product items that quite likely are interesting for them.
Data required for recommender systems stems from explicit user ratings after watching a movie or listening to a song, from implicit search engine queries and purchase histories, or from other knowledge about the users/items themselves.
Sites like Spotify, YouTube or Netflix use that data in order to suggest playlists, so-called daily mixes or to make video recommendations,respectively.
Recommender systems use 3 approaches:
1- Content-based systems, which use characteristic information.
2- Collaborative filtering systems, which are based on user-item interactions.
3- Hybrid systems, which combine both types of information with the aim of avoiding problems that are generated when working with just one kind.
1- Content-based filtering systems
The main idea in content-based filtering is to tag products using certain keywords, understand what the user likes, look up those keywords in the database and recommend to users different products with the same attributes.
2- Collaborative-filtering systems
Collaborative filtering is a type of personalized recommendation strategy that identifies the similarities between users to serve relevant product recommendations across digital properties.
The underlying assumption of the collaborative filtering approach is that if a person A has the same opinion as a person B on an issue, A is more likely to have B’s opinion on a different issue than that of a randomly chosen person.
3- Hybrid-filtering systems
A hybrid approach is a mixture of collaborative and content-based filtering methods while making suggestions.
Several studies that compare the performance of the hybrid with the pure collaborative and content-based methods and demonstrated that the hybrid methods can provide more accurate recommendations than pure approaches.
Quick look to project…
Project: Recommend movies to random person who had given ID numbers.
Dataset: Dataset provided by MovieLens recommender company.
Variables:
movieId: Unique movie ID
title: Movie name
userId: Unique user Id
rating: rating score voted by user
timestamp: voted date
Recommended movies
You can also see details about this project and following post’s on my kaggle profile.
Thanks for reading.
