Anime Recommender

A recommender system, or a recommendation system, is a subclass of information filtering system that seeks to predict the “rating” or “preference” a user would give to an item.

There are majorly six types of recommender systems which work primarily in the Media and Entertainment industry:

• Collaborative Recommender system
• Content-based recommender system
• Demographic based recommender system
• Utility based recommender system
• Knowledge based recommender system
• Hybrid recommender system

In this blog we will see about Content-based recommender system, Collaborative Recommender system and Hybrid recommender system.

About Dataset

Content Based Recommender System

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

Content-based filtering does not require other users’ data during recommendations to one user.

This type of recommendation system can only make recommendations based on existing interests of the user. In other words, the model has limited ability to expand on the users’ existing interests.

TFID
Tf-idf is short term for term frequency-inverse document frequency. It is a numerical statistic that is intended to reflect how important a word is to a document in a collection or corpus. It is used as a weighting factor in information retrieval and text mining. The tf-idf value increases in proportion to the number of times a word appears in a document. It is offset by the frequency of the word in the corpus, that helps to adjust for some words which appear more frequently in general.

I used weighted rating to construct Top anime chart. We will use 60th percentile as our cutoff. In other words, for a movie to feature in the charts, it must have more votes than at least 60% of the movies in the list.

Collaborative Recommender System

To address some of the limitations of content-based filtering, collaborative filtering uses similarities between users and items simultaneously to provide recommendations. Hence, collaborative filtering models can recommend an item to user A based on the interests of a similar user B. Furthermore, the embeddings can be learned automatically, without relying on hand-engineering of features.

Matrix Factorization
Matrix factorization is a class of collaborative filtering algorithms used in recommender systems. Matrix factorization algorithms work by decomposing the user-item interaction matrix into the product of two lower dimensionality rectangular matrices.

Hybrid recommender system

Most recommender systems now use a hybrid approach, combining collaborative filtering, content-based filtering, and other approaches.

A hybrid approach is taken between context based filtering and collaborative filtering to implement the system. This approach overcomes drawbacks of each individual algorithm and improves the performance of the system.

Kaggle Link: kaggle.com/gnitch/anime-recommender