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Music recommendation and discovery in the long tail

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  1. He emphasizes the user's perceived quality, rather than the system's predictive accuracy when providing recommendations, thus allowing users to discover new music by exploiting the long tail of popularity and promoting novel and relevant material (non-obvious recommendations)
  2. Music Recommendation and Discovery in the Long Tail. Celma, Ò. Music consumption is biased towards a few popular artists. For instance, in 2007 only 1% of all digital tracks accounted for 80% of all sales. Similarly, 1,000 albums accounted for 50% of all album sales, and 80% of all albums sold were purchased less than 100 times
  3. Applications 169 8.1 Searchsounds: Music discovery in the Long Tail 169 8.1.1 Motivation 169 8.1.2 Goals 171 8.1.3 System overview 172 8.1.4 Summary 175 8.2 FOAFing the Music: Music recommendation in the Long Tail 175 8.2.1 Motivation 175 8.2.2 Goals 176 8.2.3 System overview 177 8.2.4 Summary 182 9
  4. Probably the best book about music recommendation that I have yet to read. The author takes you on a journey to the Mordor of music recommendation and throws you into a volcano of algorithms by the long tail. Best read with a quiet glass of wine and some rock music blasting
  5. There is a need to assist people to filter, discover, personalise and recommend from the huge amount of music content available along the Long Tail.Current music recommendation algorithms try to accurately predict what people demand to listen to. However, quite often these algorithms tend to recommend popular -or well-known to the user- music.
  6. Similarly, 1,000 albums accounted for 50% of all album sales, and 80% of all albums sold were purchased less than 100 times. There is a need to assist people to filter, discover, personalise and recommend from the huge amount of music content available along the Long Tail.Current music recommendation algorithms try to accurately predict what.

Music Recommendation and the Long Tail Mark Levy Last.fm Karen House 1-11 Baches Street, London N1 6DL, UK mark@last.fm Klaas Bosteels Last.fm Karen House 1-11 Baches Street, London N1 6DL, UK klaas@last.fm ABSTRACT Using a dataset of 7 billion recent submissions to the Last.fm Scrobble API1, we investigate possible popularity bias i Abstract. Millions of people use online music services every day and recommender systems are essential to browse these music collections. Users are looking for high quality recommendations, but also want to discover tracks and artists that they do not already know, newly released tracks, and the more niche music found in the 'long tail' of on-line music

Music Recommendation and Discovery in the Long Tail. Music consumption is biased towards a few popular artists. [...] Our findings have significant implications for recommender systems that assist users to explore the Long Tail, digging for content they might like. Expand Abstract Music Recommendation and Discovery in the Long Tail Over the last couple of years, I've been lucky enough to get to know Music Information Retrieval researcher Oscar Celma. Oscar and I collaborated on a tutorial on music information retrieva l that we presented at ISMIR 2007 We model the Long Tail curve of artist popularity to predict -potentially- interesting and unknown music, hidden in the tail of the popularity curve. Effective recommendation systems should promote novel and relevant material (non-obvious recommendations), taken primarily from the tail of a popularity distribution @INPROCEEDINGS{Levy_bosteels author = {Mark Levy and Klaas Bosteels}, title = {Bosteels , Music Recommendation and the Long Tail}, booktitle = {WOMRAD 2010 Workshop on Music Recommendation and Discovery, ACM RecSys}, year = {}} Share. OpenURL . Abstract. Using a dataset of 7 billion recent submissions to the Last.fm Scrobble API 1, we.

Music Recommendation and Discovery in the Long Tail. PhD defense // UPF // Feb 16th 2009 music overload • Today (August, 2007) iTunes: 6M tracks P2P: 15B tracks 53% buy music on line • Finding unknown, relevant music is hard! Awareness vs. access to content In 2008, Òscar obtained his Ph.D. in Computer Science and Digital Communication, in the Pompeu Fabra University (Barcelona, Spain). Òscar has a book published by Springer, titled Music Recommendation and Discovery: The Long Tail, Long Fail and Long Play in the Music Digital Age (2010) Find many great new & used options and get the best deals for Music Recommendation and Discovery : The Long Tail, Long Fail, and Long Play in the Digital Music Space by Òscar Celma (2010, Hardcover) at the best online prices at eBay! Free shipping for many products

Finally we describe a new service explicitly designed to make recommendations from the long tail, and analyse popularity effects across the recommendations which it suggests. Using a dataset of 7 billion recent submissions to the Last.fm Scrobble API, we investigate possible popularity bias in Last.fm's recommendations and streaming radio. Music Recommendation in the Personal Long Tail: Long Tail business model [1], contrary to only products that were in demand b eing sold in stores. However, as a result, although WOMRAD 2010 Workshop on Music Recommendation and Discovery, colocated with ACM RecSys 2010 (Barcelona, SPAIN 2010: Springer Book Music Recommendation and Discovery: The Long Tail, Long Fail and Long Play in the Digital Music Space 2009: Co-founder of EuroMM, the European Chapter of the SIGMM ; 2008: PhD, Music Recommendation and Discovery in the Long Tail, Department of Information and Communication Technologies, UP Music Recommendation and Discovery: The Long Tail, Long Fail, and Long Play in the Digital Music Space By Òscar Celma (auth.) 2010 | 194 Pages | ISBN: 3642132863 | PDF | 3 M Donald Byrd and Tim Crawford. 2002. Problems of music information retrieval in the real world. Information processing & management 38, 2 (2002), 249--272. Google Scholar Digital Library; Òscar Celma. 2009. Music recommendation and discovery in the long tail. Ph.D. Dissertation. Universitat Pompeu Fabra. Google Schola

Music Recommendation and Discovery: The Long Tail, Long Fail, and Long Play in the Digital Music Space: Celma, Òscar: Amazon.com.au: Book Music Recommendation and Discovery [electronic resource] : The Long Tail, Long Fail, and Long Play in the Digital Music Space / by ร{146}scar Celma Imprint Berlin, Heidelberg : Springer Berlin Heidelberg, 201 Buy Music Recommendation and Discovery: The Long Tail, Long Fail, and Long Play in the Digital Music Space 2010 by Òscar Celma (ISBN: 9783642132865) from Amazon's Book Store. Everyday low prices and free delivery on eligible orders Find many great new & used options and get the best deals for MUSIC RECOMMENDATION AND DISCOVERY: LONG TAIL, LONG FAIL, By Oscar Celma **NEW** at the best online prices at eBay! Free shipping for many products

SYSTEMS AND THE DISCOVERY OF LONG TAIL 1BHUVANESWARI A, 2KARTHIKEYAN M P, 3LAKSHMINARAYANAN T R about any movie, music, video, the internet users come across a bewildering number of options to fetch long tail recommendation system. 4.1. Video Popularity based Rating Video items have to be binned based on th Levy M, Bosteels K (2010) Music recommendation and the long tail. In: Proceedings of the workshop on music recommendation and discovery (WOMRAD), pp 55-58. 20. Papadimitriou C, Tamaki H, Raghavan P, Vempala S (1998) Latent semantic indexing: a probabilistic analysis File Type PDF Music Recommendation And Discovery The Long Tail Long Fail And Long Play In The Digital Music Space February 14, 2016 Music Theory Books - GET THESE FOUR

Music Recommendation and Discovery - The Long Tail, Long

Read Free Music Recommendation And Discovery The Long Tail Long Fail And Long Play In The Digital Music Space The question of which music streaming service to fund with your £10-£20 each month should have been long settled, right Music recommendation: audio neighbourhoods to discover music in the long tail. Craw, Susan; Horsburgh, Ben; Massie, Stewar It is because of Spotify's music discovery features that I have found some of my favorite songs of all time. In fact, my top song of 2020 was one that I found on my Discover Weekly playlist. I have always appreciated Spotify's extensive library that has provided me an outlet to listen to my favorite artists in the Long Tail System for Music in the Long-Tail Keywords Music recommendation hybrid recom- 1 Introduction Music discovery and consumption has changed dramat-ically in recent years. According to recent. User Modeling and User-Adapted of audio signals. IEEE Transactions on Speech and Interaction, 12:331-370, 2002. Audio Processing, 10(5):293 - 302, 2002. [2] O. Celma. Music Recommendation and Discovery in the Long Tail. PhD thesis, Universitat Pompeu Fabra, Barcelona, Spain, 2008. [3] S. C

There is no need to search for the long tail in the music industry because the core of the music industry is operating so far below its potential that efforts should be focused on that before. The Death of the Long Tail: The Superstar Music Economy 10 recommendation algorithm might be, it needs to get 100,000 tracks better at its job every single month just to be as good as it was the previous month. More tracks paradoxically means less discovery. There is so much choice that there is effectively no choice at all Celma, Music Recommendation and Discovery in the Long Tail, Ph.D. thesis, Universitat Pompeu Fabra, 2008. [25] U. Shardanand and P. Maes, Social Information Filtering: Algorithms for Automating Word of Mouth, Proc. of the SIGCHI Conf. on Human Factors in Computing Systems, pp. 210-217, 1995

Products. mufin's patented music recommendation technology enhances your music recommendation, discovery and visualization application. Please click on the box below to learn more about our products audiogen and audiovision. audiogen offers content-based music discovery and recommendation and semantic search for audio content by sound metrics Even if you're in the long tail, it kind of pushes you back into the head, into the popular items, when making recommendations, because this is where the system is most stable, he says recommendation networks is positively associated with the widely documented phenomenon of the long tail of demand. The observable emergence of these recommendation net-works, new IT artifacts that are as fundamental to electronic commerce as the physical artifacts of retail shelves are to traditional commerce, is a basic way in which e-commerc This long tail is indicative of consumer interest in media beyond the big hits. Amazon has found that more than 50% of their book sales are in the part of the tail not stocked by physical book stores. Amazon's buyers use the site's recommendation system to locate these less broadly popular books. Like the web, television has an informatio

9.1.2 The Long Tail Before discussing the principal applications of recommendation systems, let us ponder the long tail phenomenon that makes recommendation systems neces-sary. Physical delivery systems are characterized by a scarcity of resources. Brick-and-mortar stores have limited shelf space, and can show the custome Find many great new & used options and get the best deals for Music Recommendation and Discovery: The Long Tail, Long Fail, and Long Play in the Digital Music Space by Oscar Celma (Hardcover, 2010) at the best online prices at eBay! Free delivery for many products Mobile music hasn't yet been able to tame the long tail, according to details unveiled at the Popkomm conference in Berlin, but that could be due to cellphone providers' inability to embrace. Nowadays, a large number of people consume music from the web. Web sites and online services now typically contain millions of music tracks, which complicates search, retrieval, and discovery of music. Music recommender systems can address these issues by recommending relevant and novel music to a user based on personal musical tastes. In this paper, we propose a hybrid music recommender. In this paper we propose a hybrid music recommender system, which combines usage and content data. We describe an online evaluation experiment performed in real time on a commercial music web site, specialised in content from the very long tail of music content

Music Recommendation and Discovery: The Long Tail, Long

Authors and musicians wrote minor books and songs that were remaindered...long before there was the idea of the long tail. Today's discovery and recommendation systems could doubtless stand much. Music Recommendation and Discovery: The Long Tail, Long Fail, and Long Play in the Digital Music Space. by Òscar Celma | Sep 5, 2010. 4.0 out of 5 stars 1. Hardcover. $119.99 $ 119. 99. Get it as soon as Mon, Jun 14. FREE Shipping by Amazon. Only 1 left in stock (more on the way) Where's the Long Tail? Spotify Touts Its Artist Discovery. The Spotify company headquarters in Stockholm on Feb. 16, 2015. When streaming proper -- Spotify -- first arrived stateside, music fans. The conclusion of the thesis is that the niche artist is not benefitting from Spotify's music discovery features, because the music recommendation systems are biased by popularity, and therefore not able to guide the listener into the long tail Content discovery. Recommender Discovery. Managing content. Repository dashboard. Support. FAQs. About About CORE Blog Contact us. Exploring Local Music Recommendation in the Long Tail . By Tim Clerico. Get PDF (74 KB) Publisher: Digital Commons @ IC. Year: 2019. OAI identifier: oai:digitalcommons.ithaca.edu:whalen-1660.

Music Recommendation and Discovery in the Long Tail

It is a music recommendation engine and player and it is the future of discovering the long tail of music. Pandora is a technology based solution. They spend about 20 minutes analyzing the. usage of Internet search and discovery tools, such as recommendation engines, are associated with an increase the share of niche products. We conclude that the Internet's long tail is not solely due to the increase in product selection but may also partly reflect lower search costs on the Internet. If the relationships we uncover persist

:: Oscar Celma :: Music Recommendation and Discovery in

availability and prices. Instead, we find consumers' usage of Internet search and discovery tools, such as recommendation engines, are associated with an increase the share of niche products. We conclude that the Internet's Long Tail is not solely due to the increase in product selection but may also partly reflec [Music Recommendation and Discovery: The Long Tail, Long Fail, and Long Play in the Digital Music Space] [Author: Celma, Ã'scar] [October, 2014]: Celma, Ã'scar: Books - Amazon.c A recommender system, or a recommendation system (sometimes replacing 'system' with a synonym such as platform or engine), is a subclass of information filtering system that seeks to predict the rating or preference a user would give to an item.. Recommender systems are used in a variety of areas, with commonly recognised examples taking the form of playlist generators for video and music. Instead, we find that consumers' usage of Internet search and discovery tools, such as recommendation engines, are associated with an increase the share of niche products. We conclude that the Internet's long tail is not solely due to the increase in product selection but may also partly reflect lower search costs on the Internet

Music Recommendation And Discovery: The Long Tail, Long

In our work, the multi-objective long tail recommendation algorithm MORS consists of three phases, as shown in Fig. 3: . first, in our work, we use prediction ratings as the ability of our one objective function to suggest accurate items, so that accuracy-based recommendation techniques are used to predict the ratings of unknown items for the target user YouTube Extremism and the Long Tail. Unlimited selection is revealing ugly truths about what some Americans want in their politics. By Conor Friedersdorf. Dado Ruvic / R. March 12, 2018. Pandora has quickly become one of the most popular music streaming services built on top of a Java, Scala, and Erlang infrastructure. It features the concept of customized radio stations that allow users to automatically play genres of music. As larger players like Apple and Amazon enter the field, Pandora realized they needed a better recommendation engine to continue building their base of. a way to quantify the longer tail on the Internet. In a subsequent article (Brynjolfsson et al. 2006), they iden tified supply-side explanations (i.e., product availabil ity), along with demand-side explanations, such as search tools and recommendation systems, for the long tail phenomenon. There are two recent papers that used aggregat

Music recommendation and discovery in the long tai

Music Recommendation: Audio Neighbourhoods to Discover

  1. Collecting long-tail data in a repeatable way is a critical capability for most ML teams - usually involving identifying out-of-distribution data in production (either with statistical tests or by measuring unusual model behavior), sourcing similar examples, labeling the new data, and intelligently retraining, often using active learning
  2. g increasingly e ective in lowering search changes in value or piracy of DRM-free music. I provide support for the discovery mechanis
  3. On The Long Tail of Music, Metrics and Recommendations. Some lunch break ego-surfing prompts this piece. Chris Long Tail Anderson tries to use real world data to validate some of his theorizing about music trends. Yours truly is one of the guinea pigs for this exercise in Bringing tha noise as he, and Public Enemy and Anthrax would have it
  4. On Music For Dozens, the site I'm building, we rely on artists to create and maintain helpful metadata about their own music.Accurate tags and descriptions help tremendously in our search-engine listings, so it pays for artists to care for their metadata. Getting a high level of involvement is much harder for sites, like Rhapsody, which republish the standard catalog of major-label music.

[PDF] Music Recommendation and Discovery in the Long Tail

  1. Long Tail, and his analysis has identified many other IT-enabled markets where consumers' preferences have greater depth than what one could find in a typical brick-and-mortar storefront. Examples include consumers' preferences for music at Rhap-sody.com, movies at Netflix and custom news and informatio
  2. ) Tags: long tail, music, Netflix, recommendations product. For this it is important to count the number of links pointing to a product as well as the popularity of the products from which a link originates. Hence, a web page receives a high ranking if the web pages of many other products point to it or if highly ranked pages point to it
  3. Over in the UK, Will Page, who works for the copyright collection society there, presented some interesting data at a music conference last week that suggests that the Long Tail's usual powerlaw shape doesn't fit the sales they're seeing.. I can't find his presentation online [UPDATE: there's more information in a long interview with him here], so I have to go with the good coverage by Yankee.
  4. ed in this project are MySpace, Lastfm, Pandora and Allmusic. In addition, the ways in which independent record labels utilise.
  5. g services like Spotify, we can access music anywhere, and find almost any song

Challenge The&content&distribuCon&process&today 5- Discovering non-main stream cultural, scientific or educational content is challenging - Expertise of memory organisation remains untapped, since not part of the distribution process - Users are disconnected from the experts The Data Mem. Organisations The Data The Distribution Process the User. The long tail is a problem but is also why many recommender systems exist. The items in the long tail are rare, obscure items, they are not very popular. Online vendors, unlike retailers can have these items in stock, using an ample meaning for. There are many examples of such re-discovery in the music world, but Anderson himself started off the Long Tail bandwagon with just such an example from the book world, in the story of how Touching The Void became a hit when it was on the verge of going out of print. Anderson puts this example down to Amazon's recommendation systems, but they. The phrase 'death of the long tail' refers to the theory that the long tail will prosper, rather than to the actual presence of it. Jan on March 8, 2014 at 8:49 am said: Mark, what Scott says (I think) is that many more people offer their music for money nowadays (because releasing music on the web just doesn't bring any financial risks) A constrained recommendation algorithm that considers both strong and weak social ties may be used to amend the weights of the recommendation of long-tail items. Overall, the research argument is that incorporating long-tail items into the recommendation mechanism may increase the diversity and novelty of recommended items without the typically.

Dealing with the Long Tail: Providing Uniformity to Compound Tags Sue Yeon Syn The Catholic University of America Department of Library and Information Science 620 Michigan Ave., N.E., Washington D.C. 20064 syn@cua.edu ABSTRACT In using social tags, it has been a challenge to deal with the long tail of the tag distribution. A good portion of ta Purpose: The purpose of this paper is to investigate users' knowledge and use of social networking sites and folksonomies to discover if social tagging and folksonomies, within the area of independent music, aid in its information retrieval and discovery. The sites examined in this project are MySpace, Lastfm, Pandora and Allmusic. In addition, the ways in which independent record labels. Online news is a crowded field, and personalized news is becoming the Holy Grail for news publishers facing decreased revenues and outdated business models. The challenge in personalizing the news: matching what people want with what they get. I believe that successful personalizing the news on the long tail requires three approaches with their own unique sources of power: curation, search.

Music recommendation and discovery in the long tail - COR

Deep content-based music recommendation; Collaborative Variational Autoencoder for Recommender Systems; DLTSR: A Deep Learning Framework for Recommendation of Long-tail Web Services; Online news recommender based on stacked auto-encoder; Representation Learning with Pair-wise Constraints for Collaborative Rankin when an alternate form of curation happens, we will see the long tail get a much higher share (then now) A man March 7, 2014 You have music blogs - some of them actually curate and promote new.

Bosteels , Music Recommendation and the Long Tai

  1. The long tail theory, first postulated in 2004 by writer Chris Anderson, is based on the notion that as retailers use the internet to offer a greater number of products at less cost, they will no longer have to rely on big hits to prop up their sales. In other words, the demand curve moves away from the head and flattens toward the tail. But a research paper coauthored by Wharton operations.
  2. mance of recommendations is greatly improved, the Matthew ef-fect becomes increasingly evident. While the head contents get more and more popular, many competitive long-tail contents are difficult to achieve timely exposure because of lacking behavior features. This issue has badly impacted the quality and diversity of recommendations
  3. Music recommendation sites like Last.fm and Pandora, once consigned to the discussions of a few offbeat tech bloggers, are now heralded as the key to halting the downward slide of record sales
  4. The long-tail keywords you find could be used as the title and main topic for a highly targeted blog post or article (or video, or infographic, or any other type of content), or you could use them as variations to better optimize a longer guide or article targeting one primary keyword - for example, by using the long-tail keywords in your.
  5. YouTube's Long Tail Model Contributing to Radicilzation . Unlike traditional market economies where consumers are enticed to the most sought after products, digital distributors of entertainment and their respective recommendation engines drive them to increasingly more obscure content that they wouldn't have potentially found otherwise; down the long tail

Discussions of the long tail that I have seen or heard in the library community strike me as somewhat partial. Much of that discussion is about how libraries contain deep and rich collections, and about how their system-wide aggregation represents a very long tail of scholarly and cultural materials (a system may be at the level of a consortium, or a state, or a country) The Long Tail theory suggests that, as the Internet makes distribution easier — and uses state-of-the-art recommendation systems that allows consumers to become aware of more obscure products. This is a cynic's checklist for the implementation of long-tail ideas. Unless you master these issues, you are telling the wrong tale. Chris discusses most of these items in his book. However, in the after-glow of reading all the long-tail success stories, most people will forget the tactical items it takes to succeed. Low-cost production A growing number of startups are experimenting with new ways of monetizing music curation, both at the head and at the long tail of digital influencers. While business models vary widely, there.

The long tail. The area und e r the curve for the long tail is quite large if we can efficiently capture all these long tailed items. A good recommendation system exposes the long tail/hidden gem. The Long Tail concept is illustrated by the right part of the graph below, in orange. This graph maps the formula at the heart of Pareto's law (named after the economist Vilfredo Pareto), which describes a typical 80/20 relationship within social and economic context; e.g. 80% of sales would come from only 20% of the products and vice-versa Thus, the Long Tail makes it critically important that retailers provide tools to facilitate the discovery of products through both active and passive search. Active search tools allow consumers to easily locate products they know they are interested in. Sampling tools, such as Amazon.com's samples of book pages and CD tracks, on the other hand

View Notes - Anatomy of the Long Tail from MKTG 230 at University of Pennsylvania. SUMMER 2006 VOL.47 NO.4 Erik Brynjolfsson, Yu Jeffrey Hu and Michael D. Smith From Niches to Riches: Anatomy of th Research conflicts with 'Long Tail' According to paper by a pair of Wharton researchers, Netflix data shows trends that conflict with some of the main themes of the 2006 book The Long Tail The long tail theory, first postulated in 2004 by writer Chris Anderson, is based on the notion that as retailers use the internet to offer a greater number of products at less cost, they will no.

The role of the recommendation system is to suggest items to users that include popular ones and those on the long tail. From 2006 to 2009, Netflix, a streaming service offering TV shows, movies and documentaries on internet-connected devices, conducted a competition with a grand prize of $1M for the winning recommendation system Recommending long tail items to shoppers is critical because if successful it has the potential of giving ROI on slow-moving inventory. The retail giant's recommendation algorithms are based on seemingly few elements: a user's purchase history, items in their shopping cart, items they've rated and liked, and what other customers have. The Echo Nest's massive database makes it better at understanding the long tail of music, says Whitman, stuff waiting out there to be discovered, but no one knows about it. advertisemen

Myth #3: Long-tail keywords will save me a ton of money and really give me a competitive edge. The quest for long-tail keywords is often sparked by the desire to save money by avoiding the more general keywords (which are often more competitive and thus more expensive). If I were a business owner, I would have the exact same mindset and would. Former Disney and Discovery execs are teaming up to launch a new streaming service called Struum, arriving in the spring, that aims to take the ClassPass model and apply it to the streaming. Keywords: Recommendation systems, Issues, Long tail, Context aware Systems. Introduction: Recommendation systems (RS) serve the right item to the user in an automated fashion to satisfy user and to improve businesses.. Most commercial RS are the collaborative, query less discovery engines. They have become important area of research

Oscar Celma - The Machine Learning ConferenceLastGroupLens Research - Wikipedia

Spectralmind closed the company and open-sourced all software. Dear all, After ramping down development already in 2013, Spectralmind now fully closed the company and stopped all operations. We hugely thank the entire former team of Spectralmind, all collaborators and partners and generally all people who have given us the trust and interest. The Long Tail (Of Financial Advisory Firms) The concept of the long tail was first popularized by Chris Anderson in an article by the same name in Wired magazine in 2004, which in turn was expanded into a book a few years later.. The essence of the long tail concept was that instead of trying to find the next hot product to stock in inventory and sell - the traditional challenge. Understanding The Long Tail Keywords 37 Chapter 2 Key Takeaway: Build Your Master Keyword List 40 that just isn't how people discover music right now. For bands, the Discovery phase of the Fan Journey is probably the RECOMMENDATION ENGINES ALBUM REVIEWS MUSICBLOG INTERVIEWS WORD OFMOUTH SOCIAL MEDIA RADIOAND INTERNETPLAY TOUR DATE The coronavirus outbreak in China may impose a longer-term impact on the bitcoin network's mining activity at a time when an estimated 65 percent of its computing power is located there.. While. When The Long Tail Wags The Dog. One of the hot concepts mentioned frequently when discussing Internet businesses and applications for the last year or two has been that of the Long Tail. It was most recently popularized by Chris Anderson's October 2004 article in Wired called The Long Tail. I've written about some of value of the long tail. iTunes and other online music services thrive on the Long Tail formula, introducing listeners to new bands through recommendations, taking advantage of vast digital databases