Recommender Systems
- Prediction: What rating will someone give to a particular product?
- Application: Build system to recommend products that people are interested in
- Insights: How are opinions influenced by factors like time, gender, age, and location?
Social Networks
- Prediction: Whether two users of a social network are likely to be friends
- Application: “People you may know” and friend recommendation systems
- Insights: What are the features around which friendships form?
Advertising
- Prediction: Will I click on an advertisement?
- Application: Recommend relevant ads to maximize revenue
- Insights: What products tend to be purchased together, what do people purchase at different times of the year?
Medical Informatics
- Prediction: What symptom will a person exhibit on their next visit to the doctor?
- Application: Recommend preventative treatment
- Insights: How do diseases progress, and how do different people progress through these stages?
What we need to do data mining
- Are the data associated with meaningful outcomes?
- Are the data labeled?
- Are the instances (relatively) independent?
- Is there a clear objective to be optimized?
- How will we know if we’ve modeled the data well?
- Can actions be taken based on our findings?
- Is there enough data?
- Are our results statistically significant?
- Can features be collected?
- Are the features useful/relevant/predictive?
What is covered in this course?
This course aims to teach
- How to model data in order to make predictions like those above
- How to test and validate those predictions to ensure that they are meaningful
- How to reason about the findings of our models
i.e., data mining
But, with a focus on applications from recommender systems and the web
- Web datasets
- Predictive tasks concerned with human activities, behavior, and opinions
i.e., recommender systems
Expected Knowledge (What won’t be taught)
Basic data processing
- Text manipulation: count instances of a word in a string, remove punctuation, etc.
- Graph analysis: represent a graph as an adjacency matrix, edge list, node-adjacency list, etc.
- Process formatted data, e.g. JSON, html, csv files, etc.
Basic mathematics
- Some linear algebra, e.g.
- Some optimization
- Some statistics (standard errors, normal/binomial distributions)
All coding exercises will be done in Python with the help of some libraries (numpy, scipy, NLTK, etc.)
Idea with “expected knowledge” is not that you know all of these things, but rather you learn those that you don’t on your own
The most related classes are:
- CSE 150/151
- CSE 250A
- CSE 250B
Last year’s links: CSE 158
Course Outline
- Fundamentals
- Regression
- Classification
- Recommender Systems
- More advanced
- Content and Structure in Recommendation
- Temporal Data Mining and Recommendation
- Text mining
- (A little bit of visual recommendation)
- Ethics of Personalized Recommendation
Regression
- Linear regression and least-squares
- A little bit of feature design
- Overfitting and regularization
- Gradient descent
- Training, validation, and testing
- Model selection
How can we use features to make predictions about real-valued outcomes?
How can we prevent our models from overfitting by favoring simpler models over more complex ones?
Classification
- Logistic regression
- Multiclass and multilabel classification
- {How to evalutate classifiers, especially in “non-standard” settings
Next we adapted these ideas to binary or multiclass outputs
Recommender Systems
- Collaborative filtering
- Latent factor models and matrix factorization
- Recommender Systems Evaluation
Content and Structure in Recommender Systems
- Recommendation with complex features
- Recommendation in non-standard settings
- Advertising
- Maybe discuss some tools/libraries
Tools and Libraries
- Crawling and parsing data from the Web
- Plotting data
- Tensorflow
Later: More “specialized” material
Temporal & Sequence Data
- Sliding windows & autoregression
- Temporal dynamics in recommender systems
- Temporal dynamics in text & social networks
Text Mining
- Sentiment analysis
- Bag-of-words representations
- TF-IDF
- Stopwords, stemming
- Personalized text models and natural-language explanations
Personalized Visual Models
Ethics
Personalized Machine Learning - McAuley, Julian
Other references from – Bishop: Pattewrn Recognition and Machine Learning
Python Data Products (Coursera)
New Classes
- Fairness & Interpretability of ML (WI25)
- ML for Music (SP25)
Supervised Learning - Regression
What is Supervised Learning?
Supervised learning is the process of trying to infer from labeled data the underlying function that produced the labels associated with the data
Given labeled training data of the form
Infer the function
Solution 1: Design a system based on prior knowledge
Disadvantages:
- Depends on possibly false assumptions about how users relate to items
- Cannot adapt to new data/information
Advantages:
- Requires no data
Solution 2: Identify words that I frequently mention in my social media posts, and recommend movies whose plot synopses use similar types of language
Disadvantages:
- Depends on possibly false asumptions about how users relate to items
- May not be adaptable to new settings
Advantages:
- Requires data, but does not required labeled data
Solution 3: Identify which attributes are associated with positive ratings. Recommend movies that exhibit those attributes.
Disadvantages:
- Requires a (possibly large) dataset of movies with labeled ratings
Advantages
- Directly optimizes a measure we care about (prediction ratings)
- Easy to adapt to new settings and data
Supervised versus unsupervised learning
Learning approaches attempt to model data in order to solve a problem
Unsupervised learning approaches find patterns/relationships/structure in data, but are not optimized to solve a particular predictive task
Supervised learning aims to directly model the relationship between input and output variables, so that the output variables can be predicted accurately given the input