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A collaborative filter might also, hypothetically, cluster reading patterns in narrow viewpoints. Current Approach. It turns out that straddling both techniques can give us the best of both worlds. We built an algorithm inspired by a technique, Collaborative Topic Modeling CTM, that 1 models content, 2 adjusts this model by viewing signals from readers, 3 models reader preference and 4 makes recommendations by similarity between preference and content. Overview. Our algorithm starts by modeling each article as a mixture of the topics it addresses. We can think of a topic as an unobserved theme, like Politics or Environment, that affects the words observed in the article. For example, if an article is about the environment, wed expect words like tree or conservation. We model each reader based on their topic preferences. We can then recommend articles based on how closely their topics match a readers preferred topics. As an example, we run our algorithm supposing that all New York Times articles published in the last month can be represented as a combination of two topics. Under these constraints, the algorithm identifies these topics, roughly, as Politics and Art. Our algorithm finds an article, America Deepens its Footprint in Iraq Once More, as 1. Politics, and a film review by A. O. Scott as 1. 00 Art. It labels mixtures, too for example, an article about art politics, Frick Museum Abandons Contested Renovation Plan, is labeled 5. Politics, 5. 0 Art. In this PoliticsArt space, we might describe our articles in the following manner Next, suppose that one reader prefers to read about Art 6. Politics 4. 0 of the time. We might represent that reader with the red x. The magical part is that they are spatially close to articles that align with their interests, even if they havent read them yet we recommend articles that are closest to them in the space. There are further questions for us to answer. Can this topic space capture ambiguous word usageAnd how do we best observe the preferences of our readers Clicks are, after all, not robust Im sure that at some point, you have clicked on something you didnt enjoy and missed something you would have found interesting. We tested many options carefully the algorithm we built brings us closer to addressing some of these questions, and gives us a powerful new way to understand The New York Times. This is a three part challenge Part 1 How to model an article based on its text. Part 2 How to update the model based on audience reading patterns. Part 3 How to describe readers based on their reading history. Part 1 How to model an article based on its text. First, our algorithm looks at the body of each article and applies Latent Dirichlet Allocation LDA, a content modeling algorithm. LDA learns the mixture of topics in each article A topic is formally defined as a distribution over a vocabulary. If a document has a certain topic weighted highly, the words seen in the article are more likely to be words weighted highly under this topic. LDA is quick, accurate for our purposes and capable of online inference or learning topics in real time as new articles are published. LDA topics tend to be broad some examples are Middle East, Film and Healthcare, which allows us to relate pieces from differing viewpoints. LDA is based on a graphical model, which can easily be extended to incorporate new assumptions and information. In our case, we extend our model to model not only the text of an article, but also the specific readers reading that article, described in the next section. Part 2 How to update the model based on audience reading patterns. LDA takes words as input, but words are often ambiguous Context, style and voice can adjust their meaning. For example, if Gail Collins writes a piece with the words dog, car and roof, can we tell that shes being allegorical, and not just writing about animals and automobiles Indeed, a purely LDA based approach gives weight to travel, placing her piece at the blue point in the diagram below. However, a large sample of readers reading the piece have also read pieces about Hillary Clinton and Ted Cruz visualized for illustration at the red xs, so wed like our algorithm to adjust it to the green point in the Politics topic. By adding offsets to model topic error, as described in the CTM paper, our algorithm incorporates reading patterns on top of content modeling to create a hybrid approach. The CTM algorithm works by iteratively adjusting offsets and then recalculating reader scores. It runs until there is little change in either. A randomly chosen subset of readers, called our training sample, gives us the information we need. We tested two methods for calculating offsets 1 CTM and 2 an approach called Collaborative Poisson Factorization. In online AB testing, we found CTM to perform better. Part 3 How to describe readers based on their reading history. The methods used for adjusting article topics also calculate reader preference, but they cant scale to all our users. Therefore, we needed a quick way to calculate reader preferences, which can occur after finalizing article topics. A simple approach would be to average together the topics of all articles youve read If youve clicked on one article that is 4. Politics, 6. 0 Art and another that is 6. Politics, 4. 0 Art, youre. PoliticsArt topic space. However, this assumes that clicks perfectly indicate preference. What if you clicked on an article you didnt like, or missed one you would have One way to approach this is to back off a bit, and say that you only 9. This leaves room for mistaken clicks or missed gems. In this diagram, the green dots represent articles a reader has read, and the red dots represent ones they havent. Purple is a Manchesterbased company that specializes in running WiFi hotspots for brands like Legoland, Outback Steakhouse and Pizza Express.The new flick Pokmon I Choose You ditches Brock and Misty, but theres something that might be even more surprising.Warning this article has spoilers.People Agree to Clean Toilets for Wi.Fi Because They Didnt Read the Terms.Tim Berners Lee, the father of the World Wide Web, listed his top worries for the future of his creation earlier this year.One of his biggest concerns is the increasingly dense terms of service agreements that companies ask users to sign.Now, a public Wi.Fi company has demonstrated just how dangerous those complicated agreements can be by inserting absurd conditions that thousands of people unwittingly agreed to.One of the downsides of being a worldwide tech juggernaut with two billion monthly users is thatRead more Read.Purple is a Manchester based company that specializes in running Wi.Fi hotspots for brands like Legoland, Outback Steakhouse and Pizza Express.This week, the company came clean about its two week experiment in which it inserted a Community Service Clause into its terms of service agreement. 2007 Zx10r Special Edition Review here. More than 2. 2,0.Facebook and maybe look up some directions.In a blog post on its site, a Purple spokesperson explains that the agreement requires users to do any of the following, at Purples discretion Cleansing local parks of animal waste.Providing hugs to stray cats and dogs.Manually relieving sewer blockages.Cleaning portable lavatories at local festivals and events.Painting snail shells to brighten up their existence.Scraping chewing gum off the streets.There was also a prize offer for anyone who contacted the company and pointed out the clause.Only one person received a prize.Its unclear if Purple would even be legally allowed to enforce the clause, but it says it wont even try.This was simultaneously a campaign to raise awareness about the necessity of reading the terms of service, and a marketing stunt to announce that Purple is the first Wi.Fi provider to be compliant under the new General Data Protection Regulation GDPR.The GDPR guidelines will become enforceable on May 2.European Union. The new regulations are intended to simplify terms and conditions as well as provide more transparency for consumers to understand how their personal data will be used.Here in the US, fancy consumer protections are anathema to freedom and capitalism.So, you might want to be careful.Purple via Geeks are Sexy.Kotaku. Players take the fight to the Burning Legions home world in World of Warcraft 7.Shadows of Argus.The massive update will launch on live servers starting with North America on August 2. |
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