Netflix Recommendations

Netflix Recommendations - media opinions

11 Great Moments In Netflix Recommendations
http://www.11points.com/Movies/11_Great_Moments_In_Netflix_Recommendations

collection of posts at Huffington Post
http://www.huffingtonpost.com/news/netflix-recommendations/

http://www.huffingtonpost.com/2012/04/09/netflix-recommendations_n_1413179.html
Good Movies To Watch
http://agoodmovietowatch.com/netflix/

The Netflix Prize Winning Algorithm

The BellKor Solution to the Netflix Grand Prize - Netflix Prize 

http://www.netflixprize.com/assets/GrandPrize2009_BPC_BellKor.pdf

Five Best Movie Recommendation Services

http://lifehacker.com/5884202/five-best-movie-recommendation-services
good sites
ChickFlix
http://chickflix.net/
Rotten Tomatoe

http://www.rottentomatoes.com/
http://www.rottentomatoes.com/about/

WHAT IS THE TOMATOMETER™?

The Tomatometer™ rating - based on the published opinions of hundreds of film and television critics - is a trusted measurement of movie and TV programming quality for millions of moviegoers.

The Tomatometer™ rating represents the percentage of professional critic reviews that are positive for a given film or television show.

Back in the days of the open theaters, when a play was particularly atrocious, the audience expressed their dissatisfaction by not only booing and hissing at the stage, but throwing whatever was at hand -- vegetables and fruits included.

A good review is denoted by a fresh red tomato. In order for a movie or TV show to receive an overall rating of Fresh, the reading on the Tomatometer for that movie must be at least 60%.

A bad review is denoted by a rotten green tomato splat (59% or less).

To receive a Certified Fresh rating a movie must have a steady Tomatometer rating of 75% or better. Movies opening in wide release need at least 80 reviews from Tomatometer Critics (including 5 Top Critics). Movies opening in limited release need at least 40 reviews from Tomatometer Critics (including 5 Top Critics). A TV show must have a Tomatometer Score of 75% or better with 20 or more reviews from Tomatometer Critics (including 5 Top Critics). If the Tomatometer score drops below 70%, then the movie or TV show loses its Certified Fresh status. In some cases, the Certified Fresh designation may be held at the discretion of the Rotten Tomatoes editorial team.

WHAT IS THE AUDIENCE SCORE?

The Audience rating, denoted by a popcorn bucket, is the percentage of all Flixster.com and RottenTomatoes.com users who have rated the movie or TV Show positively.

The full popcorn bucket means the movie received 3.5 stars or higher by Flixster and Rotten Tomatoes users.

The tipped over popcorn bucket means the movie received less than 3.5 stars by Flixster and Rotten Tomatoes users.

The plus sign will appear for movies that do not have audience ratings or reviews. The percentage you see associated with this icon is the percentage of users who added the movie to their Want-to-See list.

mediocre:
Criticker
http://www.criticker.com/
Jinni
http://www.jinni.com/

Metacritic
http://www.metacritic.com/

Netflix Recommendation Algorithm

[PDF] PushTrust: An Efficient Recommendation Algorithm by Leveraging Trust and Distrust Relations

R Forsati, I Barjasteh, F Masrour, AH Esfahanian… - Proceedings of the 9th ACM …, 2015

http://www.egr.msu.edu/waves/people/iman_files/RecSys_15_PushTrust_pres.pdf

Scaling Up Recommendation Services in Many Dimensions

B Németh - Proceedings of the 9th ACM Conference on …, 2015

... After successes in the Netflix Prize competition, the research team transformed into a company
providing recommendation and personalization solutions on a ... Currently he is focused on how
to apply state of the art recommendation algorithms in real world problems to help ...

Recommendation techniques in forensic data analysis: a new approach

M Quintana, S Uribe, F Sánchez, F Álvarez - … Prevention and Detection (ICDP-15), 6th …, 2015

... This technique was widely exploited at the Netflix prize [10], and it is ... provided by past cases, and
they are built over recommendation systems algorithms. ... methods inspire the “Clues
Recommendation Algorithm” while memory based collaborative filtering techniques are the ...

[PDF] POI Recommendation: Towards Fused Matrix Factorization with Geographical and Temporal Influences

JB Griesner, T Abdessalem, H Naacke - Proceedings of the 9th ACM Conference on …, 2015

... Matrix factorization techniques have demonstrated since the Netflix challenge [8] to be one of the
most accurate recommendation methods many ... Zhang et al. in [16] have proposed Collaborative
Location Activity Filtering (CLAF) algorithm for generic recommendation. ...

Content recommendation and service costs in swarming systems

D Munaro, C Delgado, DS Menasche - … (ICC), 2015 IEEE International Conference on, 2015

... Netflix alone accounts for up to 34% of North America's downloads during peak hours. ... Some
of the existing P2P systems already have some sort of content recommendation algorithm
embedded in the system [7]–[9], but none of these algorithms was implemented to influence ...

BITCOIN and block chain technology

BITCOIN RESOURCES

https://lopp.net/bitcoin.html

Bitcoin Mining

https://www.bitcoinmining.com/

Understanding LSTM RNN Networks

Unsupervised Learning of Video Representations using LSTMs

Long Short Term Memory (LSTM) networks to learn representations of video sequences.

DEEP LEARNING (DL) RECURRENT NEURAL NETWORKS (RNN) GENERATIVE COMPUTER VISION

We use multilayer Long Short Term Memory (LSTM) networks to learn representations of video sequences. Our model uses an encoder LSTM to map an input sequence into a fixed length representation. This representation is decoded using single or multiple decoder LSTMs to perform different tasks, such as reconstructing the input sequence, or predicting the future sequence.
http://www.cs.toronto.edu/~nitish/unsupervised_video/

A test set for evaluating sequence prediction/reconstruction
Moving MNIST [782Mb] contains 10,000 sequences each of length 20 showing 2 digits moving in a 64 x 64 frame.

The results in the updated arxiv paper use this test set to report numbers. For future prediction, the metric is cross entropy loss for predicting the last 10 frames for each sequence conditioned on the first 10 frames.
Code
PapersUnsupervised Learning of Video Representations using LSTMs [pdf]
Nitish Srivastava, Elman Mansimov, Ruslan Salakhutdinov
ICML 2015.
Updated arxiv version with more details -
Unsupervised Learning of Video Representations using LSTMs [arxiv]
Nitish Srivastava, Elman Mansimov, Ruslan Salakhutdinov
unsup_video_lstm.tar.gz [119Kb]

Understanding LSTM Networks

http://colah.github.io/posts/2015-08-Understanding-LSTMs/

Posted on August 27, 2015

Recurrent Neural Networks

Humans don’t start their thinking from scratch every second. As you read this essay, you understand each word based on your understanding of previous words. You don’t throw everything away and start thinking from scratch again. Your thoughts have persistence.

Traditional neural networks can’t do this, and it seems like a major shortcoming. For example, imagine you want to classify what kind of event is happening at every point in a movie. It’s unclear how a traditional neural network could use its reasoning about previous events in the film to inform later ones.

Recurrent neural networks address this issue. They are networks with loops in them, allowing information to persist.



Deep Learning
Nando de Freitas
26 February 2015
YouTube Oxford CS lectures
https://www.youtube.com/playlist?list=PLE6Wd9FR--EfW8dtjAuPoTuPcqmOV53Fu

Deep Learning Lecture 12: Recurrent Neural Nets and LSTMs
Published on Mar 2, 2015

Slides available at: https://www.cs.ox.ac.uk/people/nando....
Course taught in 2015 at the University of Oxford by Nando de Freitas with great help from Brendan Shillingford.
https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/
https://www.youtube.com/watch?v=56TYLaQN4N8

Deep Learning
Alex Graves
5 March 2015
YouTube Oxford CS lectures
https://www.youtube.com/watch?v=-yX1SYeDHbg

Ilya Sutskever's home page

www.cs.toronto.edu/~ilya/

Ilya Sutskever Research Director of OpenAI. I spent three wonderful years as a Research Scientist at the Google Brain Team. Before that, I was a co-founder of ...

‎Publications - ‎RNN text generation demo - ‎Award

Recurrent Batch Normalization
http://arxiv.org/abs/1603.09025

Tim Cooijmans, Nicolas Ballas, César Laurent, Çağlar Gülçehre, Aaron Courville
(Submitted on 30 Mar 2016 (v1), last revised 4 Apr 2016 (this version, v3))
We propose a reparameterization of LSTM that brings the benefits of batch normalization to recurrent neural networks. Whereas previous works only apply batch normalization to the input-to-hidden transformation of RNNs, we demonstrate that it is both possible and beneficial to batch-normalize the hidden-to-hidden transition, thereby reducing internal covariate shift between time steps. We evaluate our proposal on various sequential problems such as sequence classification, language modeling and question answering. Our empirical results show that our batch-normalized LSTM consistently leads to faster convergence and improved generalization.

Ross Goodwin
http://rossgoodwin.com/
https://medium.com/@rossgoodwin/3505ae7a17e7

Adventures in Narrated Reality, Part II
Ongoing experiments in writing & machine intelligence

By Ross Goodwin

[DRAFT]

Due to the popularity of Adventures in Narrated Reality, Part I, I’ve decided to continue narrating my research concerning the creative potential of LSTM recurrent neural networks here on Medium. In this installment, I’ll begin by introducing a new short film: Sunspring, an End Cue film, directed by Oscar Sharp and starring Thomas Middleditch, created for the 2016 Sci-Fi London 48 Hour Film Challenge from a screenplay generated with an LSTM trained on science fiction screenplays.

Escape from Freedom?

1. Stanley Milgram experiments at Yale university
https://en.wikipedia.org/wiki/Milgram_experiment

Milgram summarized the experiment in his 1974 article, "The Perils of Obedience", writing: 

The legal and philosophic aspects of obedience are of enormous importance, but they say very little about how most people behave in concrete situations. I set up a simple experiment at Yale University to test how much pain an ordinary citizen would inflict on another person simply because he was ordered to by an experimental scientist. Stark authority was pitted against the subjects' [participants'] strongest moral imperatives against hurting others, and, with the subjects' [participants'] ears ringing with the screams of the victims, authority won more often than not. The extreme willingness of adults to go to almost any lengths on the command of an authority constitutes the chief finding of the study and the fact most urgently demanding explanation.

Ordinary people, simply doing their jobs, and without any particular hostility on their part, can become agents in a terrible destructive process. Moreover, even when the destructive effects of their work become patently clear, and they are asked to carry out actions incompatible with fundamental standards of morality, relatively few people have the resources needed to resist authority.

People were agreeing to trigger electrical shocks to other people because they were asked politely.
Why defyance is not a norm?

2. Freedom Of Press
https://twitter.com/FreedomofPress

POLITICAL HUMOR

http://www.americanrhetoric.com/speeches/avigdorliebermanunitednationsspeech.htm

Avigdor Lieberman
Address at the 65th Session, United Nations General Assembly
delivered 28 September 2010, New York, NY

five Jews who changed the way we see the world:
Moses: Law is everything;
Jesus: Love is everything;
Marx: Money is everything;
Freud: Sex is everything;

Einstein: Everything is relative.

Political Campaigns and Donors

Politicians, their campaigns and finances

1. Marco Rubio
http://www.ontheissues.org/Economic/Marco_Rubio_Tax_Reform.htm
http://www.ontheissues.org/2016/Marco_Rubio_Health_Care.htm


Larry Ellison gave $3 million to Marco Rubio
By Joe Garofoli
August 4, 2015
http://www.sfchronicle.com/business/article/Larry-Ellison-gave-3-million-to-Marco-Rubio-6425154.php

https://www.opensecrets.org/politicians/contrib.php?cycle=2016&cid=N00030612

http://www.politico.com/story/2015/10/marco-rubio-sheldon-adelson-donors-2016-214680

2. Bernie Sanders
Bernie Sanders on Tax Reform
Socialist Jr Senator; previously Representative (VT-At-Large)

http://www.ontheissues.org/2016/Bernie_Sanders_Tax_Reform.htm

3. Hillary Clinton
Hillary Clinton on Tax Reform
Secretary of State; previously Democratic Senator (NY)

http://www.ontheissues.org/celeb/Hillary_Clinton_Tax_Reform.htm

http://www.ontheissues.org/2016/Hillary_Clinton_Health_Care.htm

4. Donald Trump

http://www.ontheissues.org/Celeb/Donald_Trump_Tax_Reform.htm

http://www.ontheissues.org/2016/Donald_Trump_Health_Care.htm

Angel and Venture Investors

October 12 2015

TEC Club pitch presentations by alumni of Israel's famous 8200 EISP accelerator .

Participating founders (8200 EISP Alumni):

Yael Vizel, Founder of Zeekit - http://site.zeekit.co
Racheli Batish, Founder of Zuznow - http://www.zuznow.com
Micha Berkuz & Shay Darin, Founders of Giftedd - http://www.giftedd.com
Roe Stimler, Founder of SportJobz - http://www.sportjobz.com
Guy Gaash, Founder of Shopnfly- http://www.shopnfly.com/

Confirmed investors:

Sanjit Dang, Intel Capital
Mohammad Islam, DFJ
Oded Hermoni, Rhodium
Jay Boddu, Juniper Networks
Mark Sue, RBC Capital Markets
William Kim, Varsity Ventures
Zhao Tiemin, IPV Capital
Shruti Gandhi, Array Ventures
Vivek Ladsariya, Fenox Venture Capital
Monali G Jain, Angel Investor,Keiretsu Forum
Mary Jo Potter, Healthcare Angels
Ephraim Lindenbaum, Advance Ventures
Tirto Adji, Palapa Ventures
Sergey Patsko, PV Fund I
Assim Gupta, Angel Investor,Keiretsu Forum
Vito Bialla, Angel Investor
Max Shapiro, Angel Investor,Keiretsu Forum

Have questions about TEC Pitch - Israel's 8200 EISP Accelerator?
Contact The Entrepreneurs' Club

Angel Investing in Israel and Beyond
Venture Capital and Entrepreneurship in the Silicon Valley
http://www.jhtc.org/venture-capital-and-entrepreneurship-in-the-silicon-valley

Kleiner Perkins

Here’s a list of questions investors will ask themselves after you pitch a company to them. http://www.kpcb.com/blog/how-to-craft-a-concise-pitch-investors-will-care-about

Over the course of many years working in venture capital and the video game business, I’ve listened to hundreds of pitches for new companies and products.

After almost every one of those pitches, once the entrepreneur has left the room, a post-game review ensues. There are consistent patterns in those discussions; aspiring entrepreneurs, pay attention.

The first and most important element of your presentation is not a slide: it’s you. Most investors base their decisions on the passion, charisma and clarity of the founder and the rest of the senior team. If you don’t demonstrate true fervor for your idea and opportunity, don’t expect anyone else to, either. Beyond that here’s what you should consider when crafting your pitch.
Make The First Slide Count

Initial ideas don’t necessarily prevail, but lack of focus leads to failure and underachievement. Which also means: time is of the essence. Don’t waste a slide on a lame table of contents or meeting agenda. A business pitch is not like Disneyland; potential investors shouldn’t have to wait for the good stuff.

You need to keep your slides tight. As soon as somebody presents a single extraneous slide, people drift. You can lose the room on the first slide. Skip the background materials. The longer it takes you to get to the meat, the more it feels like you’re covering up for something.

Describe your business in simple declarative statements, with strong verbs and valuable nouns. Skip the adjectives and adverbs. Work at boiling complex opportunities into clear and simple actions. Keep it simple.

And remember, you’re in business. Business runs on salesmanship, creativity and numbers. Make the numbers tell your story. For some investors, it will be the first slide they actually concentrate on, so make it good.
Ask The Questions Investors Will

Once you’ve left the room, we’ll list plusses, minuses, unanswered questions and big risks. Knowing that there will never be more than about a dozen takeaways, you can shape your presentation accordingly.
Do you seem like a great entrepreneur?
Who is the competition and how do you stack up?
What are your team’s assets?
What have you accomplished to date?
How well have you managed your resources?
How big is the market you are targeting?
How protected can your business be?
The Four Slide Shell

Some people will find it difficult, but you need to avoid massive slide decks. Less is more. You can deliver an entire pitch with four slides. Here’s how:
The first slide what I call “the movie poster,” which lays out the core idea behind you business idea in a clear, powerful way. It should be something you can blow up to poster size and hang up in the office lobby. When in doubt, show your target customers living a better life because of your product.
The next slide lays out the five most important strategies you intend to employ. These should be original, or at least an original combination of ideas.
Slide three lays out your five most important assets. These are the things that justify your pre-money valuation: team, code complete, design complete, IP, contracts, traction, revenue growth, etc. Better yet, you can actually benchmark most of these components by asking around. Most of these: valuable. Product/market fit: priceless.
The final slide tells the story in numbers, from where you are now to long-term success.

Four slides, each powerfully structured:
Every slide should have one important topic.
Every bullet point should have one good idea.
There can be no extraneous or unclear words.

The deck isn’t everything; you’re also going to need to do a demo:
Be a missionary for your product. Now is the time.
Start at the start, with the first time user experience. You want your audience to project themselves as users of your great product.
Illustrate the strategy and assets that you claimed on slides 2 and 3.
Your Graphic User Interface (GUI) and wireframe should reinforce your strategies with “ownable” nouns and verbs on the GUI, tabs and buttons (e.g., Friends, Follow, Badges).

Everything else goes in the appendix.

Also, watch your audience. You want them to “lean in” on your movie poster, smile at your strategies, nod at your assets and agree with the assumptions in your numbers. You want their fingers to itch to touch your product as demonstrated. You want them to applaud honestly. And then, like a great businessperson, ask for the order.

If it’s a fit, they will ask you questions you hadn’t thought of, give you advice that helps, and make you a better entrepreneur than when you walked in. It’s not just about the money.

Good luck.

This post by Bing Gordon first appeared in Fast Company, 4/17/14

startup advice

Above the Crowd
BY BILL GURLEY
All Revenue is Not Created Equal: The Keys to the 10X Revenue Club
http://abovethecrowd.com/2011/05/24/all-revenue-is-not-created-equal-the-keys-to-the-10x-revenue-club/

https://www.cbinsights.com/research/top-venture-capital-partners/
https://www.cnbc.com/2018/09/13/what-linkedin-co-founder-reid-hoffman-looks-for-in-a-start-up-pitch.html
https://www.alexanderjarvis.com/2018/01/10/top-vc-shares-guide-to-pitch-deck-outline-for-startups-fundraising/
https://timsanders.com/3-things-i-learned-from-tim-draper-at-the-startup-conference/
https://stripe.com/atlas/guides/ama-marc-andreessen

Startups in SF Bay Area

http://www.deepinstinct.com/
http://comma.ai/
https://www.init.ai/careers
https://www.diux.mil/
https://simility.com/
https://www.menlosecurity.com/

http://trapx.com/
https://databricks.com/
https://www.synack.com/
https://siftscience.com/
https://quid.com/
https://www.kaggle.com/
https://www.palantir.com/

open source project https://shadowproject. io/en/team

startups in San Francisco Bay area from the angellist:

VR
https://angel.co/virtual-reali ty-3

AI
https://angel.co/artificial-in telligence

how to pitch - startup education
https://www.lynda.com/Jana-Trantow/4629058-1.html
https://www.linkedin.com/in/nycjanalee/