created project website

Author: Sukhdeep Gill

ProjectWebsite.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "   # Get Recced: App Recommendation using Reviews and Ratings"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Abstract: \n",
+    "\n",
+    "In this project we examined how recommender systems work (better or worse) if we take advantage of the review texts along \n",
+    "with the review ratings, we aimed to combine latent ratings with latent review topics and analyse the results. \n",
+    "Our assumption was that combining the review text with ratings would help the recommender system make better predictions. \n",
+    "Hence, we compared different models on Amazon Apps dataset and calculated RMSE. \n",
+    "\n",
+    "We Compared and evaluated the following recommendation models:\n",
+    "  Baseline model \n",
+    "  Collaborative Filtering\n",
+    "  Latent Dirichlet Allocation (LDA)\n",
+    "  Hidden Factors as Topics (HFT)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
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+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.14"
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+ "nbformat_minor": 2
+}