Paras Sharma Parassharmaa

Title

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Movies Recommendation:

MovieLens - Movie Recommendation Data Sets http://www.grouplens.org/node/73
Yahoo! - Movie, Music, and Images Ratings Data Sets http://webscope.sandbox.yahoo.com/catalog.php?datatype=r
Jester - Movie Ratings Data Sets (Collaborative Filtering Dataset) http://www.ieor.berkeley.edu/~goldberg/jester-data/
Cornell University - Movie-review data for use in sentiment-analysis experiments http://www.cs.cornell.edu/people/pabo/movie-review-data/

Music Recommendation:

Last.fm - Music Recommendation Data Sets http://www.dtic.upf.edu/~ocelma/MusicRecommendationDataset/index.html

	Latency Comparison Numbers (~2012)
	----------------------------------
	L1 cache reference 0.5 ns
	Branch mispredict 5 ns
	L2 cache reference 7 ns 14x L1 cache
	Mutex lock/unlock 25 ns
	Main memory reference 100 ns 20x L2 cache, 200x L1 cache
	Compress 1K bytes with Zippy 3,000 ns 3 us
	Send 1K bytes over 1 Gbps network 10,000 ns 10 us
	Read 4K randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD

	# https://github.com/matplotlib/matplotlib/issues/881
	# Several of the ColorBrewer maps are "qualitative", meaning
	# they are just a group of colors that can be used together
	# for categories of data. So I remapped Accent to segments
	# instead of continuous:

	# Actually, these should be used with ListedColormap, and
	# the number of colors should depend on the number of
	# categories in the data, with colors removed from the
	# list in a certain order?