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Introduction to
Recommender Systems
Rami Al-Salman
Information/Choice
Overload
What is a recommender system?
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. (Wiki)
The goal of a Recommender System is to generate meaningful recommendations to a collection of users
for items or products that might interest them. (Melville, Sindhwani)
Recommender systems reduce the information/choice overload by estimate the relevance
Recommender Systems Everywhere
https://docs.google.com/presentation/d/1ksjTJ9HGuMv0Xhy40W0trg8h0uVc70OLkTyMaA6M2Oc/edit#slide=id.p
https://www.slideshare.net/dirkheld/understanding-choice-overload-in-recommender-systems
https://livebook.manning.com/book/practical-recommender-systems/chapter-1/46
Business value of recommender systems
Netflix: 75% of the movies watched
Amazon: 35% sales
Youtube : 38% more clickthrough
https://arxiv.org/pdf/1908.08328.pdf
Sources of information for recommender systems
Explicit ratings e.g, 5-stars, 1 star, etc
Explicit binary ratings (thumbs up/thumbs down)
Implicit information, e.g., how many times was it viewed?
User profiles/preferences
Item features
Recommender System general overview
Recommendations Engine
Id score
6 0.9
2 0.88
555 0.6
666 0.3
Recommendations
Users Profiles &
Contextual parameters
Item Features
Classes of recommendation approaches (Burke, R, 2007)
Content-based
Collaborative filtering
Demographic
Community-based
Hybrid approach
Content-based
When a user liked an item, we try to recommend items that are similar to the the user liked in the past.
The recommended items share some characteristics and features values with the liked item.
If the user watched a horror movie and liked it, then next time we recommend horror movies with similar
features i.e., description.
Watched by a user
Similar movies
Recommended to user
Collaborative filtering (CF)
Recommends to the active user the items that other users with similar tastes liked in the past.
The similarity in taste of two users is calculated based on the similarity in the rating history of the users.
CF is the most used and famous technique
Watched by both users
Similar users
Watched by him, recommended to her
Collaborative Filtering (CF)
Memory-based
● User-User
● Item-Item
Model based:
● Matrix Factorization
● Deep learning
Memory-based (User-User)
Harry Potter 1 Harry Potter 2 Host The Hunt
User1 ? ?
User2 ?
User3 ? ?
User4 ? ? ?
Given users and movies, will user User1 like the Host and The Hunt movies?
Memory-based (User-User)
Harry Potter 1 Harry Potter 2 Host The Hunt
User1 1 1 ? ?
User2 1 1 1 ?
User3 ? 1 1 ?
User4 ? ? ? 1
Get the most similar N-neighbour users. For simplicity, lets N to be 1
Given the most similar user, use what he/she liked to recommend to the source user
For similarity, several functions can be used, e.g., Jaccard Similarity, Cosine similarity, etc
Memory-based (User-User)
Harry Potter 1 Harry Potter 2 Host The Hunt
User1 1 1 ?
User2 1 1 1 ?
User3 ? 1 1 ?
User4 ? ? ? 1
Jaccard Similarity: Sim(A,B) = |A ∩ B| / |A| + |B| - |A ∩ B|
Sim(User1, User2) = 2 / 3
Sim(User1, User3) = 1 / 3
Sim(User1, User4) = 0
Based on that we can recommend Host movie from User2 to User1
1
Model-based (Matrix Factorization)
https://developers.google.com/machine-learning/recommendation/collaborative/basics
Demographic
We recommend items based on the demographic profile of the user.
The assumption is that different recommendations should be generated for different demographic niches.
E.g., adapte language or country for specific e-commerce website
Community-based
This type of system recommends items based on the preferences of the users friends.
This technique follows the epigram “Tell me who your friends are, and I will tell you who you are”.
(Bellotti, V, etl, 2008).
Challenges
Scalability of the algorithms with large and real-world datasets
Proactive recommender systems, i.e., recommenders that decide to provide recommendations even if not
explicitly requested
Privacy preserving recommender systems
Diversity of the items recommended to a target user
Summary
Recommender Systems are everywhere and critical for businesses to success
Several kinds of recommender systems were presented
Collaborative Filtering is most widely used technique
There are still many open challenges to be tackled in the context of the recommender systems
Materials and references
https://www.inf.unibz.it/~ricci/papers/intro-rec-sys-handbook.pdf
https://realpython.com/build-recommendation-engine-collaborative-filtering/
https://www.fi.muni.cz/~xpelanek/PV254/slides/intro.pdf

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