Radio and the Internet: Networked Participative Radio Models
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US V. Entercom Communications Corp. and CBS Corporation
Case 1:17-cv-02268 Document 1 Filed 11/01/17 Page 1 of 10 UNITED STATES DISTRICT COURT FOR THE DISTRICT OF COLUMBIA UNITED STATES OF AMERICA United States Department of Justice Antitrust Division 450 Fifth Street, N.W., Suite 4000 Washington, DC 20530, Plaintiff, v. ENTERCOM COMMUNICATIONS CORP. 401 E. City Avenue Suite 809 Bala Cynwyd, PA 19004, and CBS CORPORATION 51 W. 52nd Street New York, NY 10019, Defendants. COMPLAINT The United States of America brings this civil action to enjoin the proposed acquisition of CBS Radio, Inc. by Entercom Communications Corporation, and to obtain other equitable relief. The acquisition likely would substantially lessen competition for the sale of radio advertising to advertisers targeting English-language listeners in the Boston, Sacramento, and San Francisco Designated Market Areas (“DMAs”), in violation of Section 7 of the Clayton Act, 15 U.S.C. § 18. The United States alleges as follows: Case 1:17-cv-02268 Document 1 Filed 11/01/17 Page 2 of 10 I. NATURE OF THE ACTION 1. Pursuant to an Agreement and Plan of Merger dated February 2, 2017, between Entercom, CBS Radio, Inc. and CBS Corporation, Entercom agreed to acquire CBS Radio in a Reverse Morris Trust transaction valued at over $1.6 billion. CBS Radio is a subsidiary of CBS Corporation. 2. Entercom and CBS Radio own and operate broadcast radio stations in various locations throughout the United States, including multiple stations in Boston, Massachusetts, Sacramento, California, and San Francisco, California. Entercom and CBS Radio compete head- to-head for the business of local and national companies that seek to advertise on English- language broadcast radio stations in these three DMAs. -
Jazz and Radio in the United States: Mediation, Genre, and Patronage
Jazz and Radio in the United States: Mediation, Genre, and Patronage Aaron Joseph Johnson Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Graduate School of Arts and Sciences COLUMBIA UNIVERSITY 2014 © 2014 Aaron Joseph Johnson All rights reserved ABSTRACT Jazz and Radio in the United States: Mediation, Genre, and Patronage Aaron Joseph Johnson This dissertation is a study of jazz on American radio. The dissertation's meta-subjects are mediation, classification, and patronage in the presentation of music via distribution channels capable of reaching widespread audiences. The dissertation also addresses questions of race in the representation of jazz on radio. A central claim of the dissertation is that a given direction in jazz radio programming reflects the ideological, aesthetic, and political imperatives of a given broadcasting entity. I further argue that this ideological deployment of jazz can appear as conservative or progressive programming philosophies, and that these tendencies reflect discursive struggles over the identity of jazz. The first chapter, "Jazz on Noncommercial Radio," describes in some detail the current (circa 2013) taxonomy of American jazz radio. The remaining chapters are case studies of different aspects of jazz radio in the United States. Chapter 2, "Jazz is on the Left End of the Dial," presents considerable detail to the way the music is positioned on specific noncommercial stations. Chapter 3, "Duke Ellington and Radio," uses Ellington's multifaceted radio career (1925-1953) as radio bandleader, radio celebrity, and celebrity DJ to examine the medium's shifting relationship with jazz and black American creative ambition. -
Movie Genome: Alleviating New Item Cold Start in Movie Recommendation
User Modeling and User-Adapted Interaction https://doi.org/10.1007/s11257-019-09221-y Movie genome: alleviating new item cold start in movie recommendation Yashar Deldjoo, et al. [full author details at the end of the article] Received: 23 December 2017 / Accepted in revised form: 19 January 2019 © The Author(s) 2019 Abstract As of today, most movie recommendation services base their recommendations on col- laborative filtering (CF) and/or content-based filtering (CBF) models that use metadata (e.g., genre or cast). In most video-on-demand and streaming services, however, new movies and TV series are continuously added. CF models are unable to make predic- tions in such a scenario, since the newly added videos lack interactions—a problem technically known as new item cold start (CS). Currently, the most common approach to this problem is to switch to a purely CBF method, usually by exploiting textual meta- data. This approach is known to have lower accuracy than CF because it ignores useful collaborative information and relies on human-generated textual metadata, which are expensive to collect and often prone to errors. User-generated content, such as tags, can also be rare or absent in CS situations. In this paper, we introduce a new movie recommender system that addresses the new item problem in the movie domain by (i) integrating state-of-the-art audio and visual descriptors, which can be automati- cally extracted from video content and constitute what we call the movie genome; (ii) exploiting an effective data fusion method named canonical correlation analysis, which was successfully tested in our previous works Deldjoo et al. -
BIO Kate Delaney Is an Emmy-Award Winning Journalist and Speaker Who
BIO Kate Delaney is an Emmy-award winning journalist and speaker who has interviewed more than 15,000 people, including U.S. presidents, top CEOs and Hall of Fame athletes. As a woman hosting a successful, highly rated radio talk show, she became a pioneer in broadcasting. In addition to her popular speaking engagements across the country. Kate has hosted shows on the NBC Sports Radio Network, WFAN in New York City and KRLD in Dallas, Texas. Currently she hosts the Kate Delaney show that includes Forbes Books Radio a weekly feature showcasing movers and shakers in the business world. It reaches three million people. She’s the author of three popular books, Level the Playing Field (2011) and Invade the Man Cave (2016). Her latest business book published by Forbes, Deal Your Own Destiny, Increase Your Odds, Win Big and Become Extraordinary is receiving rave reviews As a successful business owner, she’s most proud of working with the Texas Rangers baseball club on closing a $25 million radio rights deal for CBS radio when she worked as Executive Sports Director. Kate has served as a Keynote speaker and consultant for companies such as: Deloitte, Stanley Oil and Gas, Forbes, UPS, Fox, AT&T, Cisco, and Exxon/Mobil, Kraft and many more. She also gave a TED style talk at the Forbes Insider’s Summit. Kate is the Past President of the National Speakers Association in North Texas and serves on the advisory board of the Dallas Network Bar. DNB is millionaire Phil Romano’s high-tech members only Professional network hub. -
Jasmine Sadry
Jasmine Sadry BIO: Radio/TV/Podcast personality, host, emcee, and social media influencer. Jasmine Sadry is a broadcaster based out of Dallas,Texas and currently co-hosts the "First & 10's Podcast” (a satirical sports talk show which also airs on WJOX-FM in Birmingham, Alabama) and “Locked On Big 12 Podcast” (an entertainment sports talk show that highlights all teams in the Big 12 Conference). Working in media/broadcasting for 19 years, Jasmine has worked for Cumulus, iHeart Media, KVUE-TV, ESPN, CBS Radio, Clear Channel, Fox Sports Radio, and The Dallas Cowboys, doing everything from sports/guy talk to news, music driven formats, anchoring, reporting, emceeing and producing/editing. She has served as co-host to nationally syndicated radio and TV host, Chris Jagger on “The Jagger Show”. She has been a producer, the host of her own shows (one of which was syndicated in Houston and Atlanta) was part of the Dallas Cowboys broadcast team for the official flagship radio station and has done assorted TV work. Her written articles have been featured on ESPN, Forbes, Cosmo, and The Washington Post websites. She has also written and produced comedic bits for her respective shows, complete with character voices and keyboard accompaniment. One of her bits was featured on MTV2's "Ain't That America." She was also seen as a character in an episode of TNT's "Dallas". In addition to a storied broadcasting career, Jasmine has done commercial film work with national partners AutoZone and Top Golf and her voice over work includes national commercials ranging from CNN, Fox, Ideal Image, Apartments.com, Raising Cane’s, Yahoo!, eHarmony, Reliant Energy, Sleep Experts, Cinemark, Chevy, and many local spots for restaurants and local TV stations. -
Seth-Cantor.Pdf
196 West Madison Avenue #16C • Dumont, NJ 07628 C: (845) 536-1705 • [email protected] • www.sethcantor.com ____________________________________________________________________________________________________ On-Air ENTERCOM COMMUNICATIONS Update Anchor • Update Anchor – SiriusXM Satellite Radio CBS Sports Radio / WCBS-880 • Update Anchor – CBS Sports Radio, WCBS-880, WFAN, 1010-WINS, Bloomberg 2017 – 2018, 2019 - Present • Update Anchor – KOMO AM/FM in Seattle and NBC News Radio WFAN 66-AM & 101.9-FM / 1010-WINS • St. Francis College Division I Basketball Play-by-Play 2019 – Present • Eleven Sports’ National Ivy League Football Coverage – Sideline Reporter • New York Boulders Can-Am League Baseball (with $3+ million in sales revenue) • Army West Point Basketball fill-in SIRIUSXM SATELLITE RADIO • Cornell University Football and Lacrosse fill-in Update Anchor • Pace University Football and Basketball NFL, MLB, NBA, Mad Dog Sports Radio • MSG Varsity Lacrosse 2018 – Present • United States Merchant Marine Academy Football • City University of New York Athletic Conference Basketball • St. John’s University Men’s Lacrosse and Women’s Basketball fill-in NEW YORK BOULDERS BASEBALL • Manhattan College Men’s Basketball fill-in Play-by-Play, Business Development • Ithaca College Football, Basketball and Baseball (Sports Director – WICB) Pomona, NY 2011 – 2018, 2019 - Present References David Brunner, Agent President – DB & Associates LEARFIELD / IMG COLLEGE (484) 695-5187; [email protected] General Manager, Multimedia George Mason Sports Properties Crys Quimby 2018 – 2019 Former National Programming Director – iHeartMedia’s NBC News Radio, 24/7 News (917) 846-7065; [email protected] IHEARTMEDIA’S 24/7 NEWS David Mayurnik Sports Update Anchor Executive Producer – CBS Sports Radio / Entercom New York NYC’s WCBS-880, NBC News Radio (212) 352-2444; [email protected] Bloomberg Radio, KOMO News Radio 2016 – 2017 Glenn Braica Head Men’s Basketball Coach – St. -
Intelligent User Interfaces for Music Discovery
Knees, P., et al. (2020). Intelligent User Interfaces for Music Discovery. Transactions of the International Society for Music Information 7,60,5 Retrieval, 3(1), pp. 165–179. DOI: https://doi.org/10.5334/tismir.60 OVERVIEW ARTICLE Intelligent User Interfaces for Music Discovery Peter Knees*, Markus Schedl† and Masataka Goto‡ Assisting the user in finding music is one of the original motivations that led to the establishment of Music Information Retrieval (MIR) as a research field. This encompasses classic Information Retrieval inspired access to music repositories that aims at meeting an information need of an expert user. Beyond this, however, music as a cultural art form is also connected to an entertainment need of potential listeners, requiring more intuitive and engaging means for music discovery. A central aspect in this process is the user interface. In this article, we reflect on the evolution of MIR-driven intelligent user interfaces for music browsing and discovery over the past two decades. We argue that three major developments have transformed and shaped user interfaces during this period, each connected to a phase of new listening practices. Phase 1 has seen the development of content-based music retrieval interfaces built upon audio processing and content description algorithms facilitating the automatic organization of repositories and finding music according to sound qualities. These interfaces are primarily connected to personal music collections or (still) small commercial catalogs. Phase 2 comprises interfaces incorporating collaborative and automatic semantic description of music, exploiting knowledge captured in user-generated metadata. These interfaces are connected to collective web platforms. Phase 3 is dominated by recommender systems built upon the collection of online music interaction traces on a large scale. -
Recommender Systems User-Facing Decision Support Systems
Recommender Systems User-Facing Decision Support Systems Michael Hahsler Intelligent Data Analysis Lab (IDA@SMU) CSE, Lyle School of Engineering Southern Methodist University EMIS 5/7357: Decision Support Systems February 22, 2012 Michael Hahsler (IDA@SMU) Recommender Systems EMIS 5/7357: DSS 1 / 44 Michael Hahsler (IDA@SMU) Recommender Systems EMIS 5/7357: DSS 2 / 44 Michael Hahsler (IDA@SMU) Recommender Systems EMIS 5/7357: DSS 3 / 44 Michael Hahsler (IDA@SMU) Recommender Systems EMIS 5/7357: DSS 4 / 44 Table of Contents 1 Recommender Systems & DSS 2 Content-based Approach 3 Collaborative Filtering (CF) Memory-based CF Model-based CF 4 Strategies for the Cold Start Problem 5 Open-Source Implementations 6 Example: recommenderlab for R 7 Empirical Evidence Michael Hahsler (IDA@SMU) Recommender Systems EMIS 5/7357: DSS 5 / 44 Decision Support Systems ? Michael Hahsler (IDA@SMU) Recommender Systems EMIS 5/7357: DSS 6 / 44 Decision Support Systems \Decision Support Systems are defined broadly [...] as interactive computer-based systems that help people use computer communications, data, documents, knowledge, and models to solve problems and make decisions." Power (2002) Main Components: Knowledge base Model User interface Michael Hahsler (IDA@SMU) Recommender Systems EMIS 5/7357: DSS 6 / 44 ! A recommender system is a decision support systems which help a seller to choose suitable items to offer given a limited information channel. Recommender Systems Recommender systems apply statistical and knowledge discovery techniques to the problem of making product recommendations. Sarwar et al. (2000) Advantages of recommender systems (Schafer et al., 2001): Improve conversion rate: Help customers find a product she/he wants to buy. -
Finding a Partner Model with Broadcasters at Tunein.Com.” Stanford Casepublisher 204-2011-1
S TANFORD U NIVERSITY 2 0 1 1 - 2 0 4 - 1 S CHOOL OF E NGINEERING C ASEP UBLISHER DRAFT Rev. May 17, 2011 DRAFT COPY DO NOT CITE: R. Chen et al. “Finding a Partner Model with Broadcasters at Tunein.com.” Stanford CasePublisher 204-2011-1. 17 May 2010. DRAFT COPY DO NOT CITE FINDING A PARTNER MODEL WITH BROADCASTERS AT TUNEIN.COM T ABLE OF C ONTENTS Introduction 1. Overview of Online Broadcast Industry 1.1. Industry Trends 1.2. Industry Participants 2. TuneIn Background 2.1. RadioTime 2.2. Tunein Radio 3. Business Model 3.1. Metrics 3.2. Market Reaction 4. Conclusions 5. Discussion 6. Questions 7. Exhibits 8. References 9. Authors Professors Micah Siegel (Stanford University) and Fred Gibbons (Stanford University) guided the de- velopment of this case using the CasePublisher service as the basis for class discussion rather than to il- lustrate either effective or ineffective handling of a business situation. S TANFORD 204-2011-01 Finding a Partner Model at TuneIn.com Introduction !Founded in 2002, TuneIn delivers free Internet radio from all over the world. The application streams radio stations from 50,000 local, international, and Internet stations spanning 140 countries and 55 di"erent languages in over 150 products.1) It provides users with access to live radio content through a variety of media including websites, smart- phones, home entertainment devices, and auto in-dash receivers. 2) The application also al- lows users to pause, rewind, and record live radio programs. Although there are numerous online applications that o"er music streaming, TuneIn di"erentiates itself by focusing on radio programming. -
WCBS TV and Radio to Observe Earth Week in Series
V\lCBS-lV~ MR. J. A .. SCmiEIDER ~ 51152/35 ! l Infonnation ~ ~ ~~!~----------------~ April 13, 1970 WCBS-TV TO OBSERVE "EARTH WEEK" IN SERIES OF EDITORIALS AND SPECIAL NEWS REPORTS WCBS-TV's continuing coverage of the growing problem of man- made encroachment on our environment will deal extensively with Earth Week in a series of editorials, special news reports, an IlEye on New York" broadcast and public service announcements, during the week beginning Sunday, April 19~ lt That evening at 6:30-7:00 H1, IIEye On New York will present a special report on several industrial plants in New Jersey whose effluence contributes to metropolitan New Yorkfs pollution. Beginning Mondax, April 20, a series of four speCial reports, liThe Doomsday Dilemma, 11 in which WCBS-TV News Science Editor Earl Ubell and Arts Editor Leonard Harris investigate the effects of air, water and earth pollution on our ecology, will be presented on THE SIX O'CLOCK REPORT and THE ELEVEN O'CLOCK REPORT. Also on that day, the first in a series of eleven WCBS-TV editorials, dealing with garbage in the streets, the filth in our sub- ways, c., will be broadcast. Editorials are presented the con- elusion of THE SIX O'CLOCK REPORT and THE ELEVEN OrCLOCK REPORT. During "Earth Week, 11 WCBS-TV wi also broadcast a number of public service announcements, some of which are produced by the sta.tion and others supplied by governmetlt agencies and private organizations concerned with our environment. (More) weBS-TV ••. 2 WCBS-TV recognized the dangers of pollution as early when it broadcast, in september of that year, a half-hour documentary titled, "Something in the Air. -
Pandora's Music Recommender 1 Introduction to Pandora 2 Description of Pandora Problem Space
Pandora’s Music Recommender Michael Howe 1 Introduction to Pandora One of the great promises of the internet and Web 2.0 is the opportunity to expose people to new types of content. Companies like Amazon and Netflix provide customers with ideas for new items to purchase based on current or previous selections. For instance, someone who rented “Star Wars” at Netflix might be presented with “The Matrix” as another movie to rent. The challenge in this strategy is to make suggestions in a reasonable amount of time that the user will mostly like based on the known list of what the user already enjoys. People will only pay attention to the recommendations of a service a finite number of times before they lose trust. Repeatedly suggest content that the user hates and the user will look for new content elsewhere. Also, a user will only pay attention to a service’s recommendation if they arrive in a reasonable amount of time. Make the user wait longer than they are willing and they will again turn elsewhere for content suggestions. Accuracy and speed are critical to a service’s success. Pandora exposes people to music with an online radio station where a user builds up “stations” based on musical interests. The user indicates in each station one or more songs or artists that he or she likes. Based on these preferences Pandora plays similar songs that the user might also like. As Pandora plays the user can further refine the station by giving a “thumbs up” or a “thumbs down” to a particular song. -
Music Recommendation Algorithms: Discovering Weekly Or Discovering
MUSIC RECOMMENDATION ALGORITHMS Music Recommendation Algorithms: Discovering Weekly or Discovering Weakly? Jennie Silber Muhlenberg College, Allentown, PA, USA Media & Communication Department Undergraduate High Honors Thesis April 1, 2019 1 MUSIC RECOMMENDATION ALGORITHMS 2 MUSIC RECOMMENDATION ALGORITHMS Abstract This thesis analyzes and assesses the cultural impact and economic viability that the top music streaming platforms have on the consumption and discovery of music, with a specific focus on recommendation algorithms. Through the support of scholarly and journalistic research as well as my own user experience, I evaluate the known constructs that fuel algorithmic recommendations, but also make educated inferences about the variables concealed from public knowledge. One of the most significant variables delineated throughout this thesis is the power held by human curators and the way they interact with algorithms to frame and legitimize content. Additionally, I execute my own experiment by creating new user profiles on the two streaming platforms popularly used for the purpose of discovery, Spotify and SoundCloud, and record each step of the music discovery process experienced by a new user. After listening to an equal representation of all genre categories within each platform, I then compare the genre, release year, artist status, and content promotion gathered from my listening history to the algorithmically-generated songs listed in my ‘Discover Weekly’ and ‘SoundCloud Weekly’ personalized playlists. The results from this experiment demonstrate that the recommendation algorithms that power these discovery playlists intrinsically facilitate the perpetuation of a star- driven, “winner-take-all” marketplace, where new, popular, trendy, music is favored, despite how diverse of a selection the music being listened to is.