and behavior. Recommender systems are widely used in e-commerce,
media, and entertainment industries to enhance user experience and increase revenue1
There are different types of recommender systems that use different filtering methods to generate
recommendations. The most common types are:
Content-based filtering: This method uses the features of the items and the users to find the
similarity between them. For example, a content-based recommender system for movies may use
the genre, director, cast, and ratings of the movies, and the preferences, demographics, and history
of the users, to recommend movies that are similar to the ones the user liked before2
Collaborative filtering: This method uses the feedback and ratings of the users to find the similarity
between them and the items. For example, a collaborative filtering recommender system for books
may use the ratings of the users for different books, and recommend books that are liked by other
users who have similar ratings to the target user3
Hybrid method: This method combines content-based and collaborative filtering methods to
overcome the limitations of each method and improve the accuracy and diversity of the
recommendations. For example, a hybrid recommender system for music may use both the features
of the songs and the artists, and the ratings and listening habits of the users, to recommend songs
that match the user’s taste and preferences4
Deep learning-based: This method uses deep neural networks to learn complex and non-linear
patterns from the data and generate recommendations. Deep learning-based recommender systems
can handle large-scale and high-dimensional data, and incorporate various types of information, such
as text, images, audio, and video. For example, a deep learning-based recommender system for
fashion may use the images and descriptions of the products, and the profiles and feedback of the
users, to recommend products that suit the user’s style and preferences.
For the use case of building a model that will recommend new products to the user based on their
purchase behavior and similarity with other users, the best option is to build a collaborative-based
filtering model. This is because collaborative filtering can leverage the implicit feedback and ratings
of the users to find the items that are most likely to interest them. Collaborative filtering can also
help discover new products that the user may not be aware of, and increase the diversity and
serendipity of the recommendations3
The other options are not as suitable for this use case. Building a classification model or a regression
model using the features as predictors is not a good idea, as these models are not designed for
recommendation tasks, and may not capture the preferences and behavior of the users. Building a
knowledge-based filtering model is not relevant, as this method uses the explicit knowledge and
requirements of the users to find the items that meet their criteria, and does not rely on the
purchase behavior or similarity with other users.
Reference: 1: Recommender system 2: Content-based filtering 3: Collaborative filtering 4: Hybrid
recommender system : [Deep learning for recommender systems] : [Knowledge-based recommender
system]
