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Haiku Deck is a presentation tool that allows users to create Haiku-style slideshows. The tool encourages users to get started making their own Haiku Deck presentations, which can be shared on SlideShare. In just 3 sentences, it promotes creating Haiku Deck presentations and publishing them to SlideShare.

This document discusses algorithms and why some people deliberately try to manipulate or "game" them. It provides several examples of how algorithms have been gamed, such as Google bombing where people manipulate search results. The document also discusses how algorithms are now ubiquitous and can influence people in subtle ways, functioning as a form of "social control." Some reasons proposed for why people game algorithms are that it's fun, the algorithms are flawed, or to gain some advantage from manipulating the system.

Reynolds-Poeschl

razialx

The document describes an algorithm for finding an optimal matching between two groups using linear programming and the simplex method. It begins with an overview of the weighted bipartite matching problem and introduces the Reynolds-Poeschl Matching Algorithm (RPMA) that converts the problem into a linear program. The algorithm uses the simplex method and implements Euclid's GCD to handle rational numbers efficiently. Several extensions are proposed, including handling different definitions of happiness and relaxing constraints to allow for non-heterosexual preferences.

The autodiscover algorithm for locating the source of information part 05#36

Eyal Doron

Finding Love with MongoDB

MongoDB

The document discusses using MongoDB as the data store for the "Potential Match Generator" component of an online dating service called eHarmony. Some key benefits of MongoDB cited include its scalability, built-in sharding and replication, and flexible schema. The document provides some tips for using MongoDB effectively, such as using real queries and data during testing, minimizing property names, using appropriate indexes, and considering how to shard data.

The use of the internet to meet life partners

Maria Brown

Online dating has evolved from printed personal ads in newspapers in the 1700s to modern algorithms and websites that use variables to match users. It has grown exponentially with the rise of the internet, with over 500 million spent on online dating in the US in 2007 alone. While it provides a large pool of potential matches, online dating also poses dangers like fraud, stalking, and identity theft that users should be aware of when establishing contact.

Decoding The Facebook News Feed

MMI Agency

The document discusses Facebook's News Feed algorithm and how brands can optimize their social media strategies. It provides a timeline of how Facebook's News Feed algorithm has evolved since 2004. It also defines key terms related to how Facebook ranks content and considers factors like user interactions, affinity scores, and time decay. The document recommends a three-step process for brands: 1) start with data to understand audiences, 2) create a content strategy with different types of content, and 3) continuously experiment, measure, and repeat to optimize performance.

Automating Tinder w/ Eigenfaces and StanfordNLP

Justin Long

UX: internal search for e-commerce

Myriam Jessier

1. The document discusses improving internal search experiences for e-commerce websites by better supporting 12 different types of user queries. 2. It provides details on how to support each of the 12 query types by considering the user behavior, implementing appropriate search logic, and handling query spectrums, qualifiers and structures. 3. The document emphasizes that properly supporting these various query types can significantly improve conversions and sales by providing a more helpful search experience for users.

This document provides 9 tips for businesses to avoid getting penalized by Facebook's algorithm update in January 2015. The update will punish overly promotional brand posts. The tips include writing with your brand's tone, using intriguing copy to pull fans to valuable content instead of pushing products, sharing interesting facts about products, asking questions to introduce products subtly, keeping copy short, showing products in use, featuring customers using products, and applying a "will fans find this interesting and valuable?" filter to all posts. Following these tips can help brands continue marketing successfully on Facebook after the algorithm change.

Big Dating at eHarmony

MongoDB

eHarmony uses MongoDB to make it easier for couples to find each other. At the forefront of big data and machine learning, eHarmony’s matching system uses a flow algorithm to process a billion potential matches per day. The Compatibility Matching System® uses bi-directional, user-defined criteria to match members based on a comprehensive set of traits and preferences. The system was originally built on a relational database, but with over 51MM+ users, it took more than 2 weeks for the matching algorithm to run. By switching to MongoDB, eHarmony reduced the time to match by 95% to under 12 hours. Learn about eHarmony's matching system, its technology evaluation process, and how it has used MongoDB to make the happiest couples in the world.

Facebook News Feed Algorithm: Facebook User Awareness

Jakub Ruzicka

Facebook News Feed Algorithm / Facebook User Awareness / Pilot Research Majority of Facebook users is not aware of the Facebook EdgeRank / News Feed Algorithm (excluding social media managers/specialists). Facebook users are rather aware of the influence of their their ‘affirmative‘ actions (liking, sharing, commenting befriending, following, subscribing) than the influence of their ‘private‘ interactions with other users (messages, chatting), their ‘private‘ actions (adjusting privacy settings, creating & editing friends lists, sorting Facebook posts) and/or of their ‘negative‘ actions (hiding, reporting & blocking something). Majority of Facebook users have used at least half of the features affecting their News Feed content shown but more than half of the Facebook users seem to be rather ‘passive‘ consumers of the News Feed content shown (not adjusting it in any way even though they seem to know about some ways of how to do it). In general, more than half of the users have never reported or blocked anything on Facebook. People rather subscribe to notifications from a page than from a person. Majority of Facebook users know about the interest and/or friend list feature but only about 1/3 of them actually use it. There are still some users (even among University students) who have never adjusted their privacy settings.

Online Dating Insider Online Dating Summit Keynote Miami 2013

Digicraft

Kdd 2014 Tutorial - the recommender problem revisited

Xavier Amatriain

This document summarizes a presentation given by Xavier Amatriain at the KDD conference in August 2014 about recommender systems. The presentation discusses the evolution of the recommender problem from search to discovery and recommendation. It then covers traditional recommendation methods like collaborative filtering, both memory-based and model-based using matrix factorization. It discusses challenges like sparsity and explores techniques learned from the Netflix Prize like SVD, restricted Boltzmann machines, and ensemble methods.

The secret of Tinder

Dori Adar

Balancing Discovery and Continuation in Recommendations

Mohammad Hossein Taghavi

Our objective for the Netflix recommendation engine is to create a personalized experience for our members, making it easier for them to find a video to watch and enjoy. When a member logs on to the service, she/he may be in one or a combination of different watching modes: discovering a new content to watch, continuing to watch a partially-watched movie or a TV show she/he has been binging on, playing one of the contents she/he had put in her play list during an earlier session, etc. If, for example, we can reasonably predict when a member is more likely to be in the continuation mode, and which videos she/he is more likely to resume, it makes sense to place those videos in more prominent places of the home page. In this talk we focus on understanding the discovery vs. continuation behavior and explain how we have used machine learning to improve the member experience by learning a personalized balance between those two modes. As a case study, we focus on a recent change on the personalization of a row of recommendations called “Continue Watching,” which appears on the main page of the Netflix member homepage on the website and the app and currently drives a significant proportion of member streaming hours.

Factorization Meets the Item Embedding: Regularizing Matrix Factorization wit...

Dawen Liang

The document presents the CoFactor model, which jointly factorizes the user-item click matrix and item-item co-occurrence matrix to improve recommender system performance. CoFactor is motivated by word embedding models like word2vec that learn embeddings from word co-occurrence. It outperforms weighted matrix factorization on several datasets based on quantitative and qualitative evaluations. The authors analyze the model fits and show its benefits from accounting for item co-occurrence patterns in user data.

(Some) pitfalls of distributed learning

Yves Raimond

This document discusses some potential pitfalls of distributed learning. It notes that while distributing training across multiple machines can reduce training time and allow processing of larger datasets, there is also communication overhead between machines that can slow down training. It provides examples of distributing two algorithms, Topic-Sensitive PageRank and Latent Dirichlet Allocation, across Spark and finds that a single-machine implementation can often outperform a distributed one for smaller problems and datasets due to lower communication costs. It concludes that distribution is best suited to problems and datasets too large to fit or train on a single machine.

Genetic Algorithm by Example

Nobal Niraula

Past, Present & Future of Recommender Systems: An Industry Perspective

Justin Basilico

Cualidades físicas

lourdes_rpma

Este documento describe las diferentes cualidades físicas y sus métodos de desarrollo. Define las cualidades como fuerza, velocidad, resistencia y flexibilidad, y describe varios tipos de cada una. También explica métodos para entrenar cada cualidad física, como carrera continua y intervalos para resistencia, levantamiento de pesas y plyometría para fuerza, y estiramientos estáticos y dinámicos para flexibilidad. El documento proporciona detalles sobre cómo realizar los ejercicios de manera segura y efectiva para mejor

Traditional games

lourdes_rpma

Physical activities in the nature

lourdes_rpma

The document provides information about nature and physical activities offered through Columbia University's summer academy program in New York City from June 28th to July 18th. Students can choose from a variety of curricular options including arts, creative writing, science, and sports. The price for the program is 4,870 euros. Physical activities offered for those choosing the sports course include basketball, baseball, swimming, football and more. Evening and weekend recreational activities are also provided.

Worksheet unhealthy habits

lourdes_rpma

The document discusses various unhealthy habits and their consequences. It notes that tobacco will cause 40,000 deaths next year. Smoking is forbidden in hospitals, schools, and public places due to its health risks. Smoking can cause lung cancer, mouth cancer, and heart attacks. Cannabis is a drug that comes from a plant. Taking drugs can reduce coordination and cause concentration problems. Obesity is excessive fat accumulation from overeating. More than 700 million people worldwide will have obesity by 2015. Obesity can be caused by poor nutrition and result in diabetes, cancer, and heart disease. Unhealthy habits like smoking, obesity, drugs, and alcohol can cost one's life and cause serious problems. Maintaining

Worksheet healthy habits

lourdes_rpma

This document discusses healthy habits and lifestyle choices. It addresses the benefits of physical activity like having more energy and improved respiratory and muscle health. Not sleeping well can negatively impact quality of life and attention. The ideal amount of sleep is 7-9 hours per night. Advice for better sleep includes sleeping in darkness, having a fixed wake up time, and avoiding long naps. Carbohydrates are found in foods like cereals, bread, sugar, and fruits and vegetables. Protein is needed to regenerate muscles and is found in foods like milk, fish, and meat. Fats provide energy but should be consumed in moderation to avoid weight gain. Water should be consumed during exercise and throughout the day. Breakfast is

Worksheet healthy habits

lourdes_rpma