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| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 4, | ||
| "metadata": { | ||
| "collapsed": true, | ||
| "slideshow": { | ||
| "slide_type": "skip" | ||
| } | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "from IPython.display import Image" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": { | ||
| "slideshow": { | ||
| "slide_type": "slide" | ||
| } | ||
| }, | ||
| "source": [ | ||
| "# Data Mining\n", | ||
| "\n", | ||
| "# Lecture 7b: Applications\n", | ||
| "\n", | ||
| "" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": { | ||
| "slideshow": { | ||
| "slide_type": "slide" | ||
| } | ||
| }, | ||
| "source": [ | ||
| "## Why Now?" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": { | ||
| "slideshow": { | ||
| "slide_type": "subslide" | ||
| } | ||
| }, | ||
| "source": [ | ||
| "" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": { | ||
| "slideshow": { | ||
| "slide_type": "subslide" | ||
| } | ||
| }, | ||
| "source": [ | ||
| "" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": { | ||
| "slideshow": { | ||
| "slide_type": "subslide" | ||
| } | ||
| }, | ||
| "source": [ | ||
| "" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": { | ||
| "slideshow": { | ||
| "slide_type": "subslide" | ||
| } | ||
| }, | ||
| "source": [ | ||
| "" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": { | ||
| "slideshow": { | ||
| "slide_type": "subslide" | ||
| } | ||
| }, | ||
| "source": [ | ||
| "### This Part\n", | ||
| "\n", | ||
| "- Recommender Systems" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": { | ||
| "slideshow": { | ||
| "slide_type": "slide" | ||
| } | ||
| }, | ||
| "source": [ | ||
| "## Recommender Systems" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": { | ||
| "slideshow": { | ||
| "slide_type": "subslide" | ||
| } | ||
| }, | ||
| "source": [ | ||
| "### What Would You Like?\n", | ||
| "\n", | ||
| "- Facebook\n", | ||
| "- Amazon\n", | ||
| "- Spotify\n", | ||
| "- Netflix\n", | ||
| "\n", | ||
| "" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": { | ||
| "slideshow": { | ||
| "slide_type": "subslide" | ||
| } | ||
| }, | ||
| "source": [ | ||
| "### Collaborative Filtering\n", | ||
| "\n", | ||
| "\n", | ||
| "\n", | ||
| "Pretty standard DM task: given this history, predict recommendation for user X." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": { | ||
| "slideshow": { | ||
| "slide_type": "subslide" | ||
| } | ||
| }, | ||
| "source": [ | ||
| "### Why Does This Work?\n", | ||
| "\n", | ||
| "- Users that like the same content are deemed to be similar.\n", | ||
| "- Content that is liked by the same kind of users is deemed to be similar.\n", | ||
| "- Content doesn't matter: books, music, pictures, etc." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": { | ||
| "slideshow": { | ||
| "slide_type": "subslide" | ||
| } | ||
| }, | ||
| "source": [ | ||
| "### Where Does It Fail?\n", | ||
| "\n", | ||
| "- Relies on data:\n", | ||
| " - Popular items have a lot of that.\n", | ||
| " - If this $\\neq$ then that.\n", | ||
| " - Cold start problem: new user / new item.\n", | ||
| " \n", | ||
| " \n", | ||
| "" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": { | ||
| "slideshow": { | ||
| "slide_type": "subslide" | ||
| } | ||
| }, | ||
| "source": [ | ||
| "### Content-based Filtering\n", | ||
| "\n", | ||
| "- Uses content or meta-data for items.\n", | ||
| " - Text from books.\n", | ||
| " - Audio from music.\n", | ||
| " - Stills from films.\n", | ||
| " - Titles, tags, actors, year, theme, etc." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": { | ||
| "slideshow": { | ||
| "slide_type": "subslide" | ||
| } | ||
| }, | ||
| "source": [ | ||
| "### Interpeting Content: Audio Example\n", | ||
| "\n", | ||
| "By Van den Oord, Dieleman, and Schrauwe (2013). See [blog](https://benanne.github.io/2014/08/05/spotify-cnns.html).\n", | ||
| "\n", | ||
| "[](https://benanne.github.io/2014/08/05/spotify-cnns.html)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": { | ||
| "slideshow": { | ||
| "slide_type": "subslide" | ||
| } | ||
| }, | ||
| "source": [ | ||
| "### Observations From the Netflix Prize\n", | ||
| "\n", | ||
| "\\$1M to beat Netflix' RMSE for predicting user's movie ratings. Intuitive solutions?\n", | ||
| "\n", | ||
| "- Standard features:\n", | ||
| " - Predict according to relative quality.\n", | ||
| " - User's tendency of voting (critical users).\n", | ||
| " - Compatibility with the movie.\n", | ||
| " \n", | ||
| " \n", | ||
| "" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": { | ||
| "slideshow": { | ||
| "slide_type": "subslide" | ||
| } | ||
| }, | ||
| "source": [ | ||
| "- Time-based features:\n", | ||
| " - People who rate in batch tend to rate older movies.\n", | ||
| " - Different rating criteria (nostalgia).\n", | ||
| " - Movies sometimes don't age well.\n", | ||
| " - Present time also plays a role.\n", | ||
| " - Same movies rated differently friday vs. monday.\n", | ||
| " - Moods are up on sundays, raising ratings." | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "celltoolbar": "Slideshow", | ||
| "kernelspec": { | ||
| "display_name": "Python 3", | ||
| "language": "python", | ||
| "name": "python3" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 3 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3", | ||
| "version": "3.5.2" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 1 | ||
| } |