Recommender Systems
Chapter 1
Sara Qassimi
sara.qassimi@uca.ac.ma
L2IS Laboratory , FST Marrakech, Cadi Ayyad University
1
R&D work driven by three dimensions
IT
Dimension
Data Scientist
Domain
Dimension
Scientific
Dimension
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How to make research Successful
● Research = answering question systematiclly
Research Strategy
Answer
Question
How to get from question to answer
Interesting
Compelling
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Research process
1. Identifying the problem.
2. Reviewing literature.
3. Setting research questions, objectives, and hypotheses.
4. Choosing the study design.
5. Deciding on the sample design.
6. Collecting and processing data
7. Analyzing data & Data Modeling ; incorporating ML & DL
8. Discussing Research findings
9. Writing the report
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5W1H Questioning Technique
● A framework that you can use when gathering information and investigating a problem.
● It is an iterative interrogative technique used to explore the cause-and-effect
relationships underlying a particular problem which allows you to understand a
situation, to discern a problem by analyzing all the dimensions from different
perspectives one at a time;
● This framework can help you to expand a discussion, scope your research, and
organize your findings and reports.
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5W1H : Community questions
Who
• The authors of the paper.
When
• The publishing date.
Where
• The Location of lab/team and/or where the paper has being published.
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5W1H : Engineering questions
What
• The Problematic (P)
Why
• The added value/Solution (S)
How
• The proposed Approach (A)
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This table compares the existing Articles (A1, A2,. . . , An) according to the comparison
Criterias (C1, C2,. . . , Cn) which are concluded during the study of the existing works.
Article
C1
C2
C3
C4
A1
A2
A3
A4
A5
A6
A7
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Motivation : The importance of personalization in the post-pandemic market
Consumers expect brands to
demonstrate that they know them
on a personal level
●
Suggest relevant product/
service recommendations
●
Offer me targeted promotions
●
Make it easy for me to navigate
in-store and online
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Recommender System (RS)
● 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.
● RS assists users to cope up with information overload
by suggestion relevant content to users [1].
● RS helps users with four key features:
○ Decide: predicting a rating for a user concerning an item;
○ Compare: rank a list of items in a personalized
way for a user;
○ Explore: give items similar to a given
target item;
○ Discover: provide a user with unknown
items that will be appreciated.
[1] Qassimi S, Abdelwahed EH (2021) Towards a Semantic Graph-based Recommender System. A Case Study of
Pr. Sara Qassimi
Cultural Heritage. JUCS - Journal of Universal Computer Science 27(7): 714-733.
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FST- UCA
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Customers (users) buy products (Items) they like or based on
recommendations from others they trust.
●
Online stores offer hundreds of thousands of items, making it difficult
for users to find the most suitable one.
●
Recommendation systems are used to help users search through items
and find the most suitable ones.
●
Content providers and social networking services also use
recommendation systems to manage and personalize content for
users.
●
Recommendation systems act as an automated user’s assistant,
suggesting not only the items the user asked for but also related items
they might like.
●
Recommendation systems are among the most popular machine
learning services used in business for personalizing content.
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Recommendations
should be relevant to
the user, i.e., they
should invoke their
interests.
Recommend items they
have not seen or used
before, but are entirely
different from what they
might have rated/ liked
before.
Recommend items they
have not seen or used
before, but are similar to
the items they have
rated/ liked before.
Recommend items that
are dissimilar to each
other, that cover different
topics or genres.
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The most common applications of RS
E-commerce: Online shopping websites such as Amazon, eBay, and Alibaba
use recommendation systems to suggest products to customers based on their
purchase history, browsing behavior, and preferences.
Streaming services: Video and music streaming platforms such as Netflix,
YouTube, and Spotify use recommendation systems to suggest movies, TV
shows, and songs to their users based on their viewing or listening history,
ratings, and preferences .
Social networks: Social networking sites such as Facebook, Twitter, and
LinkedIn use recommendation systems to suggest friends, groups, and content
to users based on their interests, activities, and social connections .
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The most common applications of RS
Online advertising: Advertising platforms such as Google AdWords and
Facebook Ads use recommendation systems to suggest relevant ads to users
based on their search history, browsing behavior, and demographics .
Travel and hospitality: Travel and hospitality websites such as Airbnb and
TripAdvisor use recommendation systems to suggest accommodations, activities,
and restaurants to their users based on their search history, ratings, and
preferences .
Healthcare: Healthcare providers and insurers use recommendation systems to
suggest treatments, medications, and health plans to patients based on their
medical history, symptoms, and demographics .
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The key element in building a RS is Data.
Explicit Data
●
Explicit data is
usually in the form
of a number given
by a user to an item
(e.g. 5-star ratings,
user feedback).
Item description
Implicit Data
●
Implicit data refers to
data that captures
user interactions with
available items,
observable user
behaviors like, the
number of clicks on
product links .
●
●
Item description data Item Metadata.
Pre-processing is
necessary to extract
relevant information
from unstructured data,
such as the list of cast
members in a movie on
Netflix.
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Major issues
● Sparsity:
○ Insufficient required data to extract descriptive metadata, rating, and contextual
information about items.
● Cold Start:
○ When a user or an item is new to the system which has insufficient ratings or
records at the start.
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Recommendation system Generations
1st Generation
2nd Generation
Content-Based
Matrix Factorization
Collaborative Filtering
Web usage mining
based
Hybrid
3rd Generation
Collaborative Filtering
using Deep Learning
stochastic artificial
neural network
Personality-based
Other AI-based Models
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AI & RS
User Augmentation & Empowering
▪ Spectacular Development of
▪ Data-Enabled Systems and Application
Enable to add value by enhancing the recommendations
to empower the user decision making
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●
Interactive
Recommendation
with voice feedback
●
Provide personalized recommendations based on user
preferences and engage in a natural conversation with users
through voice interactions.
These systems often employ natural language processing
(NLP) and voice recognition technologies.
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Interactive Recommendation with voice feedback
Book
Recommendation
Voice-Enabled
Shopping
Assistant
User: "Recommend a mystery novel with a strong female lead."
System: "Certainly. Are you looking for something recent or a classic?"
User: "I'd prefer a recent release."
System: "I suggest 'The Silent Witness' by Fiona Barton. Would you like
to hear more about it?"
User: "Help me find a stylish winter coat."
System: "Of course. What's your preferred color and size?"
User: "I like black, and I wear a medium."
System: "Great choice. Here are some black winter coats in medium
sizes available in your favorite stores."
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●
Image recommender
system mapping images
and user
●
aims to recommend images to users based on their
preferences, behavior, or specific needs.
These systems can be used in various contexts, including
e-commerce, social media, and content discovery.
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Image recommender system
Travel
Destination
Image
Recommender
Event and Activity
Recommender
Platform: A travel planning website or app.
Functionality: Users can input their travel preferences, such as beach
destinations, historical sites, or adventure travel. The system
recommends images of destinations that match these preferences.
Platform: An event discovery app.
Functionality: Users can upload images of events or activities they've
attended. The system recommends similar events happening in the
user's area based on visual cues from the images.
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●
Profiling users from
multimedia data
●
Profiling users from multimedia data involves creating detailed
user profiles based on their interactions with multimedia
content, such as images, videos, and audio.
This information can be used for various purposes, including
content recommendation, targeted advertising, and
personalization.
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Profiling users from multimedia data
Image and Audio
Profiling for Ad
Targeting
Platform: Online advertising platforms.
Data Source: User interactions with ads, including clicks and comments.
Profiling: Analyzing the types of images and audio in ads that lead to
user engagement and conversions. Detecting sentiments and emotions in
the user’s comments. Tailoring ad content to match user preferences.
Image-based
Social Media
Profiling
Platform: Social media like Instagram.
Data Source: User's uploaded images, liked images, and captions.
Profiling: Analyzing the content of images, such as objects, scenery, and
people, to determine the user's interests. Analyzing image captions and
comments to understand their preferences and sentiments.
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●
Neural collaborative
filtering
●
an approach that combines neural networks with collaborative
filtering techniques to build recommender systems.
NCF models are particularly effective for recommendation tasks
because they can capture complex user-item interactions and
provide highly personalized recommendations.
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Neural collaborative filtering : NCF
Travel Destination
Recommendation
Problem: Recommending travel destinations and experiences to
travelers.
Application: Travel planning websites can use NCF to examine a user's
past trips, preferences, and travel history to suggest new destinations,
accommodations, and activities.
NCF (Neural Collaborative Filtering)
CF (Collaborative Filtering)
suitable for travel destination recommendation
when highly personalized and accurate
recommendations are needed, and when
non-linear user-destination interactions are
significant.
It excels in handling data sparsity and providing
fine-grained personalization.
can be a simpler and more computationally
efficient choice for travel destination
recommendation, particularly when data is sparse.
It relies on user similarity and is effective in
situations where personalized recommendations
are less critical. However, it may struggle with
capturing complex, non-linear relationships.
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●
Deep Matrix Factorization
extends the conventional matrix factorization by introducing deep
neural networks. It seeks to discover hidden user and item
characteristics by decomposing a user-item interaction matrix into
latent factor representations.
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Deep Matrix Factorization : DeepMF
Job
Recommendation
Restaurant or
Food
Recommendation
Problem:Recommending job listings to job seekers.
Application: In a job search platform like LinkedIn, DeepMF can analyze a
user's job search history, skills, and career interests to recommend relevant
job openings and networking opportunities.
Problem: Recommending restaurants, dishes, or recipes to users.
Application: On food delivery apps like Uber Eats, DeepMF can consider a
user's order history, cuisine preferences, and dietary restrictions to suggest
new restaurants and menu items.
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●
Convolutional Neural
Network
●
CNNs are particularly useful when the recommendation problem
involves structured or visual data.
are primarily designed for image analysis tasks, but they can be
adapted creatively for certain aspects of recommender systems,
especially when dealing with visual or image-related
recommendations.
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Convolutional Neural Network : CNN
Fashion
Recommendation
Problem: Recommending clothing and fashion items to users based on
their preferences.
Application: CNNs can be employed to analyze the visual attributes of
clothing items, such as style, color, pattern, and texture. User preferences
and browsing behavior can be combined with CNN-based image analysis
to recommend outfits or individual fashion items.
Art and
Photography
Recommendation
Problem: Recommending artworks, photographs, or visual content to art
enthusiasts.
Application: CNNs can analyze the visual features of artworks and
photographs. Users' past interactions and preferences are considered, and
recommendations are made based on the visual similarity of artwork and
users' historical preferences.
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Convolutional Neural Network : CNN
Recipe
Recommendation
Problem: Recommending recipes to users based on their dietary
preferences and visual appeal.
Application: In recipe apps or websites, CNNs can be used to assess the
visual appeal of dishes based on images. User dietary restrictions and past
recipe choices can be combined with image analysis to recommend visually
appealing recipes that align with users' preferences.
Home Decor and
Interior Design
Problem: Recommending home decor items and interior design ideas to
users.
Application: In interior design platforms websites, CNNs can analyze
images of furniture, decor, and interior designs. Users' style preferences
and room layouts can be taken into account, and CNNs can help
recommend visually appealing decor items and design inspirations.
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●
Graph Neural Network
●
GNNs are a powerful tool for solving recommendation problems
where data can be represented as graphs.
GNNs can capture complex relationships and patterns within
graph-structured data, making them suitable for various
recommendation scenarios.
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Graph Neural Network : GNN
Social
Network-Based
Recommendation
Problem: Recommending content or connections in a social network.
Application: In social media platforms, GNNs can be applied to model the
user-user interaction graph. By analyzing the connections, user behavior,
and content interactions, GNNs can suggest relevant content, such as
posts, articles, or friends, to users.
Collaborative
Filtering with
Knowledge
Graphs
Problem: Recommending items or resources in a knowledge-based
system.
Application: GNNs can be used to incorporate knowledge graphs into
collaborative filtering. By connecting users, items, and their attributes in a
knowledge graph, GNNs can make recommendations that consider both
user-item interactions and domain-specific knowledge.
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Cross-domain
recommender system
●
Employ transfer learning techniques aim to leverage knowledge
learned from one domain to improve recommendation performance
in another domain.
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Cross-domain recommender system
E-commerce
Cross-Domain
Recommendations
Problem: Recommending products to users in different e-commerce
domains (e.g., electronics, fashion, home decor).
Application: TL can be applied to learn user preferences and behavior in
one domain and adapt this knowledge to make recommendations in other
domains. For instance, a user's interactions and purchase history in the
electronics domain can be used to recommend fashion items based on
shared preferences like brand affinity or price range.
Multilingual Movie
and TV Show
Recommendations
Problem: Recommending movies and TV shows across different
languages.
Application: TL can be employed to create recommendation models in one
language and then fine-tune or adapt these models to other languages.
This ensures that users receive personalized recommendations, regardless
of their language preferences, based on their historical interactions.
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Cross-domain recommender system
News and Content
Aggregation
Problem: News and Content Aggregation.
Application: Transfer learning can be used to analyze user interactions
with news articles in one platform and apply the learned knowledge to
recommend content in another platform. For instance, user preferences
and click behavior in a news app can be transferred to suggest relevant
blog posts or videos on a different website.
Adaptive Music
Recommendation
Problem: Recommending music that adapts to users' changing moods and
activities.
Application: Transfer learning can be used to extract features from a
user's historical listening data and adapt music recommendations for
different contexts. For example, a user's preferences for upbeat music
during workouts can be transferred to recommend relaxing music during
meditation.
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Collective
matrix factorization
●
is a technique that combines matrix factorization with transfer
learning to enhance recommendation systems. It leverages
information from multiple related domains or sources to improve
recommendation quality.
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Collective matrix factorization : CMF
Cross-Domain
News
Recommendation
Problem: Recommending news articles across different news publishers.
Application: CMF can factorize user-news interaction matrices for each
publisher while sharing latent factors across publishers. Transfer learning
allows the system to understand user interests and preferences regardless
of the news source, leading to more diverse and personalized news
recommendations.
Cross-Platform
Social Media
Recommendations
Problem: Recommending content or connections to users across multiple
social media platforms
Application: CMF can factorize user-content interaction matrices for each
platform separately. By sharing latent factors among these platforms,
transfer learning ensures that user preferences and content relationships
learned from one platform can be applied to recommend content or
connections on other platforms. This approach enhances cross-platform
user experiences and engagement.
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User behaviour
●
RL can be applied to recommender systems to model user
behavior and provide personalized recommendations. In RL-based
recommender systems, the user is typically treated as an agent,
and their interactions with the system are seen as a sequential
decision-making process.
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User behaviour
Exploration vs.
Exploitation
Click-Through
Rate (CTR)
Optimization
Problem: Balancing exploration of new items with exploiting known
preferences.
Application: In e-commerce, music streaming, or video streaming platforms,
RL can help decide when to recommend items that are similar to a user's past
choices (exploitation) and when to introduce new or diverse items to
encourage exploration. RL agents learn optimal exploration-exploitation
trade-offs to improve user satisfaction.
Problem: Maximizing the click-through rate for recommended items.
Application: In online advertising or content recommendation systems, RL can
model user interactions with recommended ads or articles. The agent (system)
learns to select items that maximize user clicks or engagement over time. User
clicks are treated as rewards, and RL algorithms like Q-learning or deep
reinforcement learning (DRL) are used to optimize the recommendations.
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Markov Decision
Process
●
MDPs can be applied to recommender systems to model the
decision-making process of recommending items to users. In this
context, the recommender system acts as an agent that makes
sequential decisions to optimize user satisfaction or other relevant
objectives.
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Markov Decision Process
E-commerce
Product
Recommendation
Game
Recommendation
Problem: Recommending products to maximize user engagement.
Application: In an e-commerce setting, the recommender system can model
user behavior as an MDP. States represent user preferences, browsing history,
and available products. Actions correspond to recommending specific
products. Rewards can be defined based on user interactions (e.g., clicks,
purchases). The MDP agent learns a policy that optimizes product
recommendations to increase user engagement and conversion rates.
Problem: Recommending video games to maximize user engagement.
Application: MDP can model user behavior as they explore and interact with
different games. States include user gaming preferences and game availability.
Actions correspond to recommending specific games. Rewards can be defined
based on user engagement metrics (e.g., playtime, in-game achievements).
The MDP agent learns a policy that maximizes user engagement by
recommending games aligned with user interests.
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●
Active learning is a machine learning approach that involves iteratively
selecting and labeling the most informative data points to train a model.
●
In the context of a recommender system, active learning can be used to
improve the performance of the recommendation model by actively
choosing which user-item interactions to label or acquire for training.
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How Active Learning Recommender System Works
●
●
●
Donec risus dolor porta venenatis
●
Start with an initial recommender system model trained on a
Pharetra luctus felis
small labeled dataset.
Proin in tellus felis volutpat
●
● porta
Define
a query strategy or selection criteria to identify which user-item
Donec risus dolor
venenatis
Pharetra luctus felis interactions to label next. The goal is to choose interactions that are
expected to provide the most valuable information to improve the model.
Proin in tellus felis volutpat
01
01
Initial
Model
Initial
Model
02
02
Lorem ipsum dolor sit amet at
Query Strategy / selection criteria ●
nec at adipiscing
●
03
03
Lorem ipsum dolor sit amet at
User-Item Interaction Selection
nec at adipiscing
●
●
●
●
Use the query strategy to select a set of user-item interactions for which
Donec risus dolor porta venenatis
the model's predictions are uncertain or where there is a potential for
Pharetra luctus felis
improvement. These interactions are typically those that the model is
Proin in tellus felis volutpat
unsure about or has low confidence in.
04
04
Lorem ipsum dolor sit amet at
Labeling / Feedback
nec at adipiscing
●
●
●
● porta
Request
labels or feedback from users for the selected interactions. This
Donec risus dolor
venenatis
Pharetra luctus felis can involve asking users to rate items or provide explicit feedback on their
preferences.
Proin in tellus felis volutpat
05
05
Lorem ipsum dolor sit amet at
Model Update
nec at adipiscing
●
●
●
Donec risus dolor porta venenatis
●
Incorporate the newly labeled data into the training dataset and retrain the
Pharetra luctus felis
recommender model.
Proin in tellus felis volutpat
06
Iteration
●
Repeat steps 2-5 for a predefined number of iterations or until a certain
performance criterion is met.
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Active Learning Recommender System
E-commerce
Product
Recommendation
News Article
Recommendation
- The recommender system starts with an initial model trained on user-product
interactions.
- The query strategy targets users who have shown interest in a variety of
product categories but haven't made many purchases.
- These users are asked to provide feedback or ratings on specific products.
- The model is updated to offer more personalized recommendations across a
broader range of product categories.
- The system begins with a baseline model trained on user-article interactions.
- The query strategy identifies users who have diverse interests and have
interacted with both mainstream and niche articles.
- These users are prompted to rate or provide feedback on a set of suggested
articles.
- The model learns to offer more personalized news recommendations across
a wider spectrum of topics.
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Ratings
●
●
●
Thumbs up and down / Like and Dislike
5 stars ratings
Machine Learning:
○
○
Binary outcomes as classification
5 stars ratings as Regression
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How to recommend?
Simple approach: Just sort by average rating!
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Considering how
confident we are in
that rating.
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Confidence
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Confidence:
Why sort by lower bound?
●
●
●
●
Suppose 2 items have 4 stars on average
Item #1 has 3 ratings, and Item #2 has 100 ratings
Higher # of raters ; higher lower bound
The popularity increases the score
Because Item #2 has more ratings or in other
words, a bigger sample size, we are more
confident in its average rating, so its
confidence interval is narrow.
Item #1 confidence interval is wid.
If we use the upper bound, then Item #1
would be ranked higher than Item #2 .
Item #2
Item #1
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Confidence Intervals
●
●
Given a random variable X, we can calculate the distribution of its
sample mean
The more samples are collected (N), the narrower its distribution
This notation indicates that the random variable X follows a normal
distribution with mean μ and variance σ² . In simpler terms, it means that
the values of X tend to cluster around the value μ, and the spread or
variability of the data is determined by σ².
Central Limit Theorem
a sum of random variables converges to a normal distribution.
So X bar (Sample Mean) is going to be approximately normally distributed.
The more samples are collected, the more confident I should be in my
estimate of the average.
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More problems with average rating
what if N (ratings) is very small, or 0 ?
Naive Solution: Smoothing (or Dampening)
The basic idea is to add a small number to the numerator and denominator so that if
there is zero ratings, we can just default to some predefined value.
3 stars is mostly considered bad rating
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Supervised Machine Learning to RS
●
●
Inputs X and corresponding Targets Y
Y might represent:
○
○
○
○
○
○
●
●
Did the user buy the product?
Click on the ad?
Click on the article?
Sign up for the newsletter?
Make an account?
What did the user rate this item?
If the model is accurate, then we can use it to recommend items for the
users
The user is more likely to buy/ click/ rate highly those recommended
items
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Input Features
●
Common features include demographics:
○
○
○
○
○
○
○
○
○
●
Age
Gender
Religion
Location
Race
Occupation
Education Level
Material Status
Socioeconomic Statuts
E.g pseudo_SQL
Select product_id from products where location=’Morocco’
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Input features
●
Can include any data collected about the users:
○
○
○
○
When the user signed up
Which pages they viewed
Have credit card history
Purchase history
●
Can purchase data from other sites(e.g. surveys, tracking)
●
Supervised model:
○
○
●
Logistic regression
Random Forest
What about the item?
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What about the item?
●
Given a user age and gender; the probability a user will buy an iPhone
is probably different than the probability a user will buy cat food!
●
●
A separate predict model for each item!
Problematic: A lot of data per item for each model
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Add item attributes as model inputs
A single Machine Learning Model:
Classification - Like or unlike| Regression - Predict user’s rating
if we can predict how a user is going to behave, then we can tailor the user experience to perform
the recommendations.
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Difficulty in Getting Data
●
●
●
Privacy- ad and tracking blockers
item data- dependent on vendor
entering data correctly
If free-form, lots of string
parsing needed
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More flexible- latent variable models
●
●
●
●
●
●
●
Instead of explicit features like age; gender;etcs.
Learning features implicitly
Latent variable models- features are learned automatically
“hidden causes”
May not be interpretable or presented neatly defined concepts like age
But confident that they are mathematically optimal
we do not collect these features manually!
(user,item,rating) is enough!
Will revisit when we talk about Matrix Factorization
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There are two main types of recommender systems – personalized and non-personalized.
Recommender System
Personalized
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Pr. Sara Qassimi
FST- UCA
62