Movies Recommender System with Surpriselib

Movies Recommender System with Surpriselib

Hello everyone! I wanna share about movies recommeder system with one of modeling its collaborative filtering. Before we go to our dataset, let’s see why we need recommender system?

Hello everyone! I wanna share about movies recommeder system with one of modeling its collaborative filtering. Before we go to our dataset, let’s see why we need recommender system?

First, some people might have been confused about choosing a movie to watch. Recommender system, like we know it would be give you recommend movie based on the movie you have watched. The recommender system would be give you recommendation by genre, cast, director, rating movie from that you have been done and many more. Simply, like you watching youtube and the next video _recommendation _would be coming to you after you watching that video. The machine would be learning about what you like and that you watch frequently maybe based on “genre” or “type” of that videos and many more information about that videos.

A recommender system or a recommendation system (sometimes replacing ‘system’ with a synonym such as platform or engine) is a subclass of information filtering system that seeks to predict the __“rating” __or “preference”_ a user would give to an item. They are primarily used in commercial applications. — Wikipedia_

There is four types of recommender system that I know.

  1. Demographic Filtering
  • Decide on the metric or score to rate items on.

  • Calculate the score for every item

  • Sort the item based on the score and output the top results

  • Its offers generalized recommnendations to every user, not personalized recommendation

    Image for post

Difference between content based filtering and collaborative filtering

2. Content Based Filtering

Content-based filtering uses item features to recommend other items similar to what the user likes, based on their previous actions or explicit feedback

3. Collaborative Filtering

Collaborative filtering is based on the assumption that people who agreed in the past will agree in the future, and that they will like similar kinds of items as they liked in the past.

4. Hybrid

Called _“Hybrid” _cause recommender system with hybrid is combination between collaborative filtering and content based filtering.


Exploratory Data Analysis

This dataset, I take from kaggle but really sorry the source (url) has been down, maybe this link or dataset has been deleted by that user. This dataset it’s about every movie in IMDB until first quarter in 2018. This is dataframe info for the first csv.

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 45466 entries, 0 to 45465
Data columns (total 24 columns):
 ##   Column                 Non-Null Count  Dtype  
---  ------                 --------------  -----  
 0   adult                  45466 non-null  object 
 1   belongs_to_collection  4494 non-null   object 
 2   budget                 45466 non-null  object 
 3   genres                 45466 non-null  object 
 4   homepage               7782 non-null   object 
 5   id                     45466 non-null  object 
 6   imdb_id                45449 non-null  object 
 7   original_language      45455 non-null  object 
 8   original_title         45466 non-null  object 
 9   overview               44512 non-null  object 
 10  popularity             45461 non-null  object 
 11  poster_path            45080 non-null  object 
 12  production_companies   45463 non-null  object 
 13  production_countries   45463 non-null  object 
 14  release_date           45379 non-null  object 
 15  revenue                45460 non-null  float64
 16  runtime                45203 non-null  float64
 17  spoken_languages       45460 non-null  object 
 18  status                 45379 non-null  object 
 19  tagline                20412 non-null  object 
 20  title                  45460 non-null  object 
 21  video                  45460 non-null  object 
 22  vote_average           45460 non-null  float64
 23  vote_count             45460 non-null  float64

I’ll try combine csv dataset to build the model, there is the final dataset would be train to our model.

Image for post

Combine some dataframe, one userId may give some movie in this dataset.

I’ll try to cleaning this dataset and make some exploratory data analysis for this dataset. Let’s see what we can take some insight there.

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