veekaybee

Introduction to Recommender Systems: Non-Personalized and Content-Based on Coursera

Information retrieval is the practice of asking questions about large documents.

• It became especially popular when doing discovery for lawsuits
• or AWS in guiding you to the relevant products
• One of the first recommenders was GroupLens for newsnet

Collaborative Filtering: Involves running Ratings and Correlations through a CF engine.

• The goal is to find a neighborhood of users
• Recommendation Interfaces: Suggestion, top n

veekaybee

Systems Performance 2nd edition

See synthesized write-up here

• Do a quick performance check in 60 seconds
• Use a number of different tools available in unix
• Use flamegraphs of the callstack if you have access to them
• Best performance winds are elimiating unnecessary wrok, for example a thread stack in a loop, eliminating bad config
• Mantras: Don't do it (elimiate); do it again (caching); do it less (polling), do it when they're not looking, do it concurrently, do it more cheaply

veekaybee

how to properly select from DuckDB

SELECT review_text,title,description,goodreads.average_rating, goodreads_authors.name 
FROM goodreads 
JOIN goodreads_reviews 
ON goodreads.book_id = goodreads_reviews.book_id 
JOIN goodreads_authors  
ON goodreads_authors.author_id = (select REGEXP_EXTRACT(authors, '[0-9]+')[1] as author_id FROM goodreads) LIMIT 10;

veekaybee

Data source:

https://sites.google.com/eng.ucsd.edu/ucsdbookgraph/home

@inproceedings{DBLP:conf/recsys/WanM18,
  author       = {Mengting Wan and
                  Julian J. McAuley},
  editor       = {Sole Pera and
                  Michael D. Ekstrand and
                  Xavier Amatriain and

veekaybee

Anti-hype LLM reading list

Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts of models in prod eagerly sought.

Foundational Concepts

Pre-Transformer Models