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.
TL;DR:
- The design of both search and recommendations is to find and filter information
- Search is a "recommendation with a null query"
- Search is "I want this", recommendations is "you might like this"
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#!/usr/bin/env bash | |
{ set +x; } 2>/dev/null | |
IFS=$'\n' | |
set "$@" $(find ~ -name ".*" ! -name ".CFUserTextEncoding" ! -type l -mindepth 1 -maxdepth 1) # dotfiles | |
set "$@" $(find ~ -name "Google *" -mindepth 1 -maxdepth 1) # Google Drive | |
set "$@" ~/git # store on github/etc :) | |
set "$@" ~/node_modules | |
set "$@" ~/Applications # install apps with brew cask |
Benchmarking seems not to be a main focus of any specific academic field, although the problem has been addressed by many different groups in CS.
Some papers I found interesting: