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aronwc committed Apr 26, 2016
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"# CS 429: Information Retrieval\n",
"<br>\n",
"\n",
"## Lecture 28: Conclusions\n",
"\n",
"<br>\n",
"\n",
"### Dr. Aron Culotta\n",
"### Illinois Institute of Technology"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## What have we learned?\n"
]
},
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"source": [
"\n",
"![system](files/system.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## What's left?"
]
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{
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"source": [
"**Adaptability**\n",
"\n",
"~25% of search queries are new each day\n",
"\n",
"<br><br><br><br>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Ambiguity**\n",
"\n",
"- apple the company vs. apple the fruit\n",
"- \"man walking a dog\" vs \"a dog walking a man\"\n",
"\n",
"<br><br><br><br>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Personalized Search**\n",
"- If I search for \"government\" in the US vs in Canada\n",
"- If I search for IIT\n",
"\n",
"\n",
"<br><br><br><br>\n"
]
},
{
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"source": [
"**Multimedia Search**\n",
"- Images of cats playing with yarn\n",
"- Videos of dogs playing with cats\n",
"\n",
"<br><br><br><br>"
]
},
{
"cell_type": "markdown",
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"source": [
"** Recommendation Systems **\n",
"- If you liked X you may like Y...\n",
"\n",
"<br><br><br><br>"
]
},
{
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"metadata": {},
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"**Scaling**\n",
"- ~[300 hours of new YouTube videos uploaded per minute](http://www.marketingpilgrim.com/2014/12/in-the-next-60-seconds-300-hours-of-video-will-be-uploaded-to-youtube.html)\n",
"- ~[55 million Facebook status updates](https://blog.kissmetrics.com/facebook-statistics/)\n",
"\n",
"<br><br><br><br>"
]
},
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"![survey.png](survey.png)"
]
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{
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"**Final**\n",
"\n",
"- Tuesday, 5/3, 10:30am-12:30pm, SB104\n",
"- Pencil/pen/eraser only \n",
"- 10 questions (5 T/F, 5 short answer)\n",
"\n",
"**Topics include:**\n",
"\n",
"- Classification\n",
" - $k$-nearest neighbor\n",
" - naive bayes\n",
" - multinomial / binomial\n",
" - smoothing\n",
" - logistic regression\n",
" - gradient descent recipe\n",
" - update rules\n",
" - learning rate\n",
" - Bias / variance tradeoff\n",
" - more parameters -> higher variance, lower bias\n",
" - overfitting\n",
" - Learning to rank\n",
"- Clustering\n",
" - $k$-means\n",
" - problems: shape of clusters; influence of outliers\n",
" - Expectation Maximization\n",
" - word clustering vs. document clustering\n",
" - initialization\n",
" - error functions\n",
" - picking number of clusters\n",
"- Web search\n",
" - PageRank\n",
" - recursive formulation\n",
" - matrix formulation\n",
" - random surfer idea\n",
" - random teleports for spidertraps, dead ends\n",
" - HITS\n",
" - recursive formulation\n",
" - Link farms\n",
" - Crawling\n",
" - Mercator crawler\n",
" - front/back queues for prioritization and politeness\n",
" - Minhash for near-duplicate detection\n",
" \n",
" \n",
"** Types of questions: **\n",
"\n",
"- True / false\n",
"- Computation:\n",
" - e.g., compute PageRank scores, $k$-means error, logistic regression updates, ...\n",
"- New algorithms\n",
" - How can you improve algorithm X to have desired property Y\n",
" - Requires understanding the motivations behind the methods we've discussed\n",
" - Why did we do things the way we did?\n"
]
}
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