tag:blogger.com,1999:blog-35189639930582501382024-03-13T05:09:11.838-07:00BBBECKTRONThe Assorted Opinions and Surreal Tales of Becky Bartkowskibecktronbexbeckyhttp://www.blogger.com/profile/10919881162870837083noreply@blogger.comBlogger50125tag:blogger.com,1999:blog-3518963993058250138.post-25567661571727186122009-04-29T22:11:00.010-07:002009-04-30T22:58:27.679-07:00Recommender SystemsInternet users encounter what are referred to as recommender systems on a daily basis. Sites like <a href="http://www.amazon.com">Amazon</a>, <a href="http://www.pandora.com">Pandora</a>, <a href="http://www.netflix.com">Netflix</a>, <a href="http://www.last.fm">Last.fm</a> are a few of the pioneers in employing highly-functioning, advanced and accurate recommender systems. A recommender system takes information from an individual user, whether its purchasing history, habits or reports likes and dislikes, and has the ability to suggest items that the user will likely enjoy. This is achieved through extensive databases that contain information about songs, movies, tastes, genres, artists, possible user traits, etc. The databases are accessed and utilized through algorithms that quickly search through data and generate the recommendations. There are four major types of recommender systems: collaborative filtering, cluster models, search-based, and item-to-item. <br /><br /><img src="http://www.kamishima.net/collaborative_filtering.gif"><br />Each of these systems represent a sort of evolution within the field of recommender systems. Traditionally collaborative filtering bases recommendations on similar users, which does not do much in the way of refining recommendations, leading to recommendations that are less likely to fit the taste of the custom. Cluster models assign users to groups based on a consistent quality that all of the users possess, however this can also lead to less specific recommendations because these users aren't necessarily the most alike. Search-based systems look at user history and recommend items with the same or similar traits. The main constraint in search-based systems is that recommendations have the potential to be extremely limited, and there is not much room for discovery. The fourth most common, and probably the most advanced in recommendations, is the item-to-item system. This focuses on the traits of items and recommending new things to users without the implicit inclusion of other users' traits, but simply based on item traits. <br /><br /><a href="http://s494.photobucket.com/albums/rr304/becktronbexbecky/?action=view¤t=amazon_crave.jpg" target="_blank"><img src="http://i494.photobucket.com/albums/rr304/becktronbexbecky/amazon_crave.jpg" border="0" alt="Photobucket"></a><br />Amazon is the forerunner in e-commerce sites, with its recommendation system to thank for sales and user trust. Amazon operates with an item-to-item recommendation system, which allows for the usability and accessibility to site's vast amount of items for sale. Items are not only classified by what their basic description is, for example a CD, but also classified by the type of music, the artist, the year it was released, to name a few potential traits. This allows users to get in-depth and more tailored recommendations in spite of the gigantic database of items. Recommendations also take into account user-generated item rankings, which can make items more recommended and entice more purchasing. Additionally, Amazon includes some recommendations based upon what other users have purchased when two or more users have a purchase in common. However, this differs from basing recommendations solely on other users because users aren't bound together simply because of a few similarities. <br /><br /><a href="http://s494.photobucket.com/albums/rr304/becktronbexbecky/?action=view¤t=pandora.jpg" target="_blank"><img src="http://i494.photobucket.com/albums/rr304/becktronbexbecky/pandora.jpg" border="0" alt="Photobucket"></a><br />Internet personalized radio service and music recommendation generator Pandora began as <a href="http://www.pandora.com/mgp.shtml">The Music Genome Project</a>, which is essentially a database of songs broken down into descriptions with the smallest of traits, or "genes." There are 2,000 potential traits that can be assigned to songs. Pandora's recommender would also qualify as an item-to-item recommendation system because recommendations come through similar qualities of songs, not similar qualities in users. Interestingly, Pandora has a partnership with Amazon so that users can purchase albums or MP3s of songs they have heard on their personalized stations. Pandora allows users to rank songs through the labeling of "thumbs up" or "thumbs down," in addition to banning tracks. All of these options affect potential recommendations for each user. The more feedback that Pandora receives, as with most sites generating recommendations, the more personalized recommended songs become. <br /><br /><a href="http://s494.photobucket.com/albums/rr304/becktronbexbecky/?action=view¤t=images-1.jpg" target="_blank"><img src="http://i494.photobucket.com/albums/rr304/becktronbexbecky/images-1.jpg" border="0" alt="Photobucket"></a><br />Last.fm is an online music recommendation service, as well as a social network. It does base a fair amount of its recommendations on similarities between users. The site generates both personalized radio stations, as well as stations based on artists or genres. User likeness is ranked and listeners are grouped together based upon artists listened to that they share. Last.fm, much like Amazon, also tracks user activity, and in this case it is the listening habits both online and offline, plus searches and rankings. This fusion of clustering and item-to-item recommendations, which is more apparent here than at Amazon, hints that Last.fm, like all others, is a work in constant progress. In addition to the Last.fm database, a user can tag artists with traits of the user's choice, which impacts can impact whether or not other users might hear artists. This is dependent on other users' activities on the site, but makes users stakeholders in ensuring that they are tagging responsibly. <br /><br /><a href="http://s494.photobucket.com/albums/rr304/becktronbexbecky/?action=view¤t=images.jpg" target="_blank"><img src="http://i494.photobucket.com/albums/rr304/becktronbexbecky/images.jpg" border="0" alt="Photobucket"></a><br />Netflix is an online DVD rental service with one of the most enviable recommendation systems in existence. Its personalized movie recommendations are very similar to Amazon's system of trait-based databases, while simultaneously employing user-generated ratings. The main operator here is an item-to-item system. Netflix's algorithm for generating recommendations is called Cinematch, and the company is holding a <a href="www.netflixprize.com/">contest</a>, with a $1,000,000 prize, for someone to improve the Cinematch algorithm by 10%. The improvement in the system is not for higher speed or holding more data, but improving the likelihood of a user getting a recommendation that he will actually enjoy by 10%. <br /><br />Netflix's realization that, although they have a highly successful recommendation system, someday the system will be surpassed is truly the future in this field. The brilliance behind holding a contest to improve its own system will prevent people from developing algorithms for their own ventures and keeping Netflix on the cutting edge of recommendation system technology. In this online world of recommendation systems, the user is king. Senseless and inaccurate recommendations have the potential to drive users away. This makes incessant innovation an absolute necessity. As evidenced through the merging of item-to-item filtering and recommendations based on similar users, this field is burgeoning and nowhere near the finish line in terms of any company having a flawless recommendation system. Methods will continue to evolve and bleed into one another, and perhaps all become somewhat antiquated eventually. In seeing how these systems have already morphed into stronger and more reliable ones, its easy to imagine that recommendation systems will be a topic constantly in the stage of innovation. That's absolutely where the field is at the moment.becktronbexbeckyhttp://www.blogger.com/profile/10919881162870837083noreply@blogger.com0tag:blogger.com,1999:blog-3518963993058250138.post-46999290974855852352009-04-23T12:08:00.002-07:002009-04-23T12:44:39.592-07:00The Netflix Prize<a href="http://s494.photobucket.com/albums/rr304/becktronbexbecky/?action=view¤t=netflix.jpg" target="_blank"><img src="http://i494.photobucket.com/albums/rr304/becktronbexbecky/netflix.jpg" border="0" alt="Photobucket"></a><br /><i>"Winning the Netflix Prize improves our ability to connect people to the movies they love."</i><br />The Netflix Prize is extremely indicative of how crucial recommendation systems are to the functioning of businesses relying on e-commerce. The contest invites people to create a system that will improve the likelihood of users loving the movies recommended based on their preferences. Improving Netflix's current system, Cinematch, by 10% will get one dedicated winner $1 million. The desire to improve one of the best recommendation systems in existence absolutely indicative of how much competition is involved in making sure that users have a reason to return to a site. I'm trying to get an interview with someone from Netflix to further discuss its recommendation system, and its continuing improvements.becktronbexbeckyhttp://www.blogger.com/profile/10919881162870837083noreply@blogger.com0tag:blogger.com,1999:blog-3518963993058250138.post-65082205800408260312009-04-21T09:50:00.000-07:002009-04-23T10:02:37.795-07:00How Amazon operates: item-to-item<a href="http://s494.photobucket.com/albums/rr304/becktronbexbecky/?action=view¤t=Picture7-1.jpg" target="_blank"><img src="http://i494.photobucket.com/albums/rr304/becktronbexbecky/Picture7-1.jpg" border="0" alt="Photobucket"></a><br />Amazon has created perhaps the most highly functioning and relevant recommendation system developed in-house, called <i>item-to-item collaborative filtering</i>. <br /><br />"Rather than matching the user to similar customers, item-to-item collaborative filtering matches each of the user's purchased and rated items to similar items, then combines those similar items into a recommendation list," according to an industry report describing Amazon's methods. <br /><br />The system revolves around a table of similar items based on what items Amazon users are purchasing at the same time. Each item a user purchases is looked at individually and recommendations are generated from comparing each one to the similar-items table. <br /><br />Item-to-item collaborative filtering generates extremely user-relevant content, and can do so even with little user input because of how it uses similar items instead of similar users.becktronbexbeckyhttp://www.blogger.com/profile/10919881162870837083noreply@blogger.com1tag:blogger.com,1999:blog-3518963993058250138.post-24699721705876852052009-04-20T09:34:00.000-07:002009-04-23T09:50:43.320-07:00Search-Based Methods for Recommendation SystemsThe <a href="http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=1167344">article</a> from Amazon indsiders has been so helpful in my search for explanations of how different recommendation systems work. It's chock-full of information, so I've been breaking it down into little bits, in hopes that I fully understand it and that it makes at least some sense to those of you reading. <br /><br />Search-based methods look at what a user has shown preference to, either through purchase, rating or other habits unique to individual sites, for instance listening on <a href="http://www.pandora.com/">Pandora</a> or <a href="http://www.last.fm">Last.fm</a>. It takes those preferences, looks at their traits and then finds other items that have the same or similar traits. <br /><br />The issue here is that users most likely will not "discover new, relevant and interesting items," because their recommendations are based only on what they already like. The article explains this well: if someone bought <a href="http://www.imdb.com/title/tt0068646/">The Godfather</a>, then their results could range from best-selling drama DVDs or movies all from Francis Ford Coppola. There's either too much of a gap between what's liked and what's recommended, or too little.becktronbexbeckyhttp://www.blogger.com/profile/10919881162870837083noreply@blogger.com0tag:blogger.com,1999:blog-3518963993058250138.post-17022217677271160852009-04-17T10:22:00.001-07:002009-04-23T12:51:36.124-07:00Cluster Models as Recommendation Systems<a href="http://s494.photobucket.com/albums/rr304/becktronbexbecky/?action=view¤t=2630240303007-1.png" target="_blank"><img src="http://i494.photobucket.com/albums/rr304/becktronbexbecky/2630240303007-1.png" border="0" alt="Photobucket"></a>Cluster models are another often-used approach to making a functioning recommendation system, according to an <a href="http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=1167344">article</a> by three Amazon insiders. Cluster models divide users into groups, or clusters, with the most similar users. The clusters are generated and each assigned a random user at first, and then other users are compared and classified. <br /><br />The issue with cluster models is that grouping users into these clusters doesn't mean that they are grouped with the most similar other users. This can lead to recommendations of less quality.becktronbexbeckyhttp://www.blogger.com/profile/10919881162870837083noreply@blogger.com0tag:blogger.com,1999:blog-3518963993058250138.post-57425244546917710042009-04-15T21:08:00.004-07:002009-04-23T09:10:54.988-07:00Traditional Collaborative Filtering<img src="http://www.eecs.berkeley.edu/~zhanghao/main/class%20projects/netflix.png"><i>Traditional collaborative filtering</i> is a type of system used to generate user-specific recommendations based on common items between two similar users. Items most highly ranked by similar users come as the most recommended items. The major problem with this type of system is that, while recommendations are very targeted, they are also incredibly limited as far as what a user will find in his recommendations. This is due to the very limited number of similar users to which one is compared. Problems arise when users have a very narrow pool of items to draw from, and then recommendations that should ideally be similar in liking to a user do not fit that user's taste because recommendations are based on such a small amount of information. Basing recommendations solely on the taste of a small group of users with a common interest makes branching out in exposure to new things and things that are most likely to coincide with taste less and less likely.becktronbexbeckyhttp://www.blogger.com/profile/10919881162870837083noreply@blogger.com0tag:blogger.com,1999:blog-3518963993058250138.post-66326490353263743322009-04-14T22:28:00.002-07:002009-04-22T10:02:42.060-07:00Holovaty on the importance of databases<a href="http://s494.photobucket.com/albums/rr304/becktronbexbecky/?action=view¤t=Picture10.jpg" target="_blank"><img src="http://i494.photobucket.com/albums/rr304/becktronbexbecky/Picture10.jpg" border="0" alt="Photobucket"></a><br />Per Adrian Holovaty, of, among many notable endeavors, <a href="http://www.everyblock.com">EveryBlock</a>, made an appearance in my Digital Media Entrepreneurship class a week or so back and we got to discussing databases and recommendation systems. He told me a bit about a site that he co-founded while in college called <a href="http://www.lawrence.com/">Lawrence.com</a>, which is incredibly similar to a project that I'm working on called Collegization. <br /><br />Basically, both projects aim at getting students into nightlife, while heavily relying on databases of information in order to perform that service, and allowing people to find things that they might like based on location and habits. Both databases include restaurants, venues and events. He told me that the key to running this system, and incorporating a recommendation function into it, is having massive amounts of organized data. Holovaty said that the data, while minute in individual form, is incredibly valuable when amassed for the purposes of Lawrence.com and Collegization. Without these tediously formed databases, it would be impossible for either project to function.becktronbexbeckyhttp://www.blogger.com/profile/10919881162870837083noreply@blogger.com0tag:blogger.com,1999:blog-3518963993058250138.post-66320629054235071462009-03-05T16:43:00.003-07:002009-03-05T16:44:46.555-07:00How Internet Cookies WorkI was interested in how sites store information in their databases to remember users and allow them to store preferences. <a href="http://computer.howstuffworks.com/cookie3.htm">Here's</a> what I found.becktronbexbeckyhttp://www.blogger.com/profile/10919881162870837083noreply@blogger.com0tag:blogger.com,1999:blog-3518963993058250138.post-36864267653735497272009-03-05T12:38:00.004-07:002009-04-22T10:04:35.678-07:00This Next<a href="http://s494.photobucket.com/albums/rr304/becktronbexbecky/?action=view¤t=Picture7.jpg" target="_blank"><img src="http://i494.photobucket.com/albums/rr304/becktronbexbecky/Picture7.jpg" border="0" alt="Photobucket"></a><br /><a href="http://www.thisnext.com/">This Next</a> is a site devoted to consumerism. It's main functions are, according to the site, "to explore great product recommendations, get personalized shopping suggestions, and rave about the products you like." Users can see "what's hot" in other major cities, see who is recommending what, and what kind of trends are happening in their own back yards. It also employs a question of the day. For instance today users are asked, "What is your favorite kind of hot chocolate?" This site is very intriguing to me, and I think I'll be exploring it more. The downturn of the economy could put it in a very interesting position, to encourage thrift instead of its perceived devotion to luxury.becktronbexbeckyhttp://www.blogger.com/profile/10919881162870837083noreply@blogger.com0tag:blogger.com,1999:blog-3518963993058250138.post-20542735979968537102009-03-03T15:45:00.000-07:002009-03-05T12:15:59.816-07:00Criticker<a href="http://www.criticker.com/"><img src="http://www.listio.com/web20/logos/www.criticker.com.png"></a><br></br><a href="http://www.criticker.com/">Criticker</a> is a movie recommendation site based on each user's taste. It employs tags, some simple genre tags like horror, comedy and action, while others are significantly more in-depth: b-movie, robots and sequel being just a fraction of the pages upon pages of ways to define, categorize and describe films. The concept of tagging allows for organization based on likeness. If twenty people tag <i>Coffee & Cigarettes</i> with the word "art," then another film tagged twenty or so times with that same word would be deemed a similar film. This, of course, is simplifying the process and more than one tag would have to match up for films to be recommended, but that is basically how the tagging system allows <a href="http://www.criticker.com/">Criticker</a> users to discover new films.becktronbexbeckyhttp://www.blogger.com/profile/10919881162870837083noreply@blogger.com0tag:blogger.com,1999:blog-3518963993058250138.post-72544720333878075362009-02-27T09:17:00.002-07:002009-02-27T09:25:43.915-07:00Helpful articlesI've found a few articles with more detail how exactly some sites take users tastes and make recommendations based on them. First there's <a href="http://www.wisegeek.com/what-is-the-music-genome-project.htm">What is the Music Genome Project</a>, which details <a href="http://www.pandora.com">Pandora's</a> time-consuming database work that took years before they could launch, then <a href="http://www.nytimes.com">The New York Times</a> had an <a href="http://www.nytimes.com/2007/03/29/technology/29basics.html?_r=2&scp=3&sq=last.fm&st=Search">article</a> discussing both <a href="http://www.pandora.com">Pandora</a> and <a href="http://www.last.fm">Last.fm</a>. more from the perspective of what users get out of the sites, and finally an <a href="http://www.guardian.co.uk/technology/2006/nov/04/news.weekendmagazine4">interview</a> with <a href="http://www.last.fm">Last.fm</a> co-founder Martin Stiksel.becktronbexbeckyhttp://www.blogger.com/profile/10919881162870837083noreply@blogger.com0tag:blogger.com,1999:blog-3518963993058250138.post-83600646302134944432009-02-26T11:51:00.000-07:002009-03-05T12:02:48.965-07:00Yelp<img src="http://www.reputationdefenderblog.com/wp-content/uploads/2009/02/yelp.jpg"><br></br><a href="http://www.yelp.com/tempe-az">Yelp</a> is a site that one can personalize to fit a specific location, for instance, as linked, Tempe, Arizona. Once one creates a profile, upon returning to it Yelp recommends <a href="http://www.yelp.com/events/tempe-az">events</a>, <a href="http://www.yelp.com/search?find_loc=85282&radius=10.0&cflt=restaurants&sortby=rating">restaurants</a>, <a href="http://www.yelp.com/search?find_loc=85282&radius=10.0&cflt=shopping&sortby=rating">shopping</a>, <a href="http://www.yelp.com/search?find_loc=85282&radius=10.0&cflt=nightlife&sortby=rating">nightlife</a>, and an array of other activities and places nearby. The most useful part of this is that these items are ranked by popularity over time, so there are lists of what is currently or recently popular and what has long-term popularity, based on Yelp users' consistently positive reviews. The tagline, "Real people, real reviews," truly encapsulates what the site is about, and what it delivers. My main issue with the site is that things that aren't reviewed on the site will most likely never be noticed.becktronbexbeckyhttp://www.blogger.com/profile/10919881162870837083noreply@blogger.com0tag:blogger.com,1999:blog-3518963993058250138.post-79650454667512420462009-02-19T09:20:00.002-07:002009-02-19T10:16:15.882-07:00CompetitionI've recently pitched a project idea that revolves around social networking strictly for teenagers. I envision having features that allow for user feedback, with reviews, ratings and recommendations. Based on the feedback I want to be able to tell people overall what's popular, what's actually "good," and calibrate people's tastes. Essentially, I'd like a charting system similar to Last.fm's, that could also conjure recommendations based on other users who share particular likes or dislikes.becktronbexbeckyhttp://www.blogger.com/profile/10919881162870837083noreply@blogger.com0tag:blogger.com,1999:blog-3518963993058250138.post-85805665502574533732009-02-05T09:12:00.003-07:002009-02-05T09:59:02.669-07:00High Concept Pitches<b>DustBuster</b>: Lugging around a heavy, loud, over-sized vacuum cleaner for the smallest of clean-up jobs will no longer be a problem with a cordless, light, hand-held DustBuster with as much power as a vacuum, but a fraction of the size. Easy to use and transport; perfect for anyone needing a quick, thorough cleanup. Target market: Busy moms, bachelors and college kids. <br /><br /><b>Wite-Out</b>: Wasted paper, illegible copies, letters and documents filled with typos make communication untimely and difficult. Wite-Out makes easy, fast corrections to documents with its quick-drying white liquid solution. Target market: offices, writers, editors, students. <br /><br /><b>Disposable lighter</b>: A safe, easy and modern alternative to dangerous and environmentally unfriendly stick matches. Carry it in your pocket or purse for a guaranteed quick and convenient light. Target market: smokers, parents, 18-24 year-olds.becktronbexbeckyhttp://www.blogger.com/profile/10919881162870837083noreply@blogger.com0tag:blogger.com,1999:blog-3518963993058250138.post-71395537471416757712009-01-29T12:21:00.003-07:002009-02-05T10:12:21.283-07:00Music networking as a function of learning "what's good"This semester, for my course Digital Media Entrepreneurship, I need to become an expert in some technological aspect. I've chosen music sites that operate by taking in user preferences to determine what people like, and what can be recommended based on other users' preferences. My original idea was to focus on <a href="http://www.last.fm">Last.fm</a>, a UK-based site that is my personal favorite for music discovery, widgets and networking based on taste in music. Options within <a href="http://www.last.fm">Last.fm</a> are more plentiful than any other site I've come across. One can download a simple program that allows for "scrobbling," or the taking in of data (songs) that are both played in iTunes and on the iPod. Upon scrobbling, <a href="http://www.last.fm">Last.fm</a> collects the songs and artists played and creates numerical charts that can be posted as self-updating widgets on virtually any other site with weekly charts tallying up a user's most popular artists. <br /><br />A topic I'm interested in pursuing is how sites like Last.fm work with others, namely major label dreamboat <a href="http://www.rhapsody.com">Rhapsody</a>. An intriguing part of the online music world is that people want all of their listening connected, their charts accounting for all music listened to, so as not to create a bias. This results in many users developing their own ways of connecting the sites. A simple Google search will render results on how to scrobble music from one's personal <a href="http://www.rhapsody.com/myrhapsody/feeds.html">Rhapsody feed</a> into <a href="http://www.last.fm">Last.fm</a>, and do the same with sites like <a href="http://www.hypem.com">HypeMachine</a>.becktronbexbeckyhttp://www.blogger.com/profile/10919881162870837083noreply@blogger.com0tag:blogger.com,1999:blog-3518963993058250138.post-82260530740792009052008-12-27T22:04:00.002-07:002008-12-27T22:33:31.687-07:00This year I- Was vandalized by my neighbors<br />- Had neighbors kicked out of their house<br />- Trimmed my hair twice maybe<br />- Learned Final Cut, sort of<br />- Went to CMJ for the second year in a row and left with 50ish new best friends<br />- Returned to The Republic to assist in the production of MetroMix<br />- Stayed at The Republic as a community features writer<br />- Ended my sojourn as Music Director and assumed the role of The Blaze's Station <br /> Manager<br />- Realized that thirds are a bad idea<br />- But tried anyway<br />- Started working as the college marketing rep for Sony<br />- Began a memorable and actually fun stint as a broadcast lab assistant<br />- Crashed my bike and got a major scar on my right knee<br />- Character building post-crash: riding bike the rest of the way home in 100 degrees<br />- Sprained my ankle after my foot fell asleep, the most significantly painful <br /> occurrence of 2008, physical, spiritual or otherwise<br />- Questioned the sanity and humanity of those around me<br />- Lived up to at least one prediction from a psychic<br />- Found something pretty good<br />- Lost some other things<br />- Bid official adieu to the septum piercing<br />- Completed 2.5 years of college, ahead of the game, the curve and whateverbecktronbexbeckyhttp://www.blogger.com/profile/10919881162870837083noreply@blogger.com0tag:blogger.com,1999:blog-3518963993058250138.post-68829505152214903292008-09-23T15:31:00.002-07:002008-09-23T15:33:51.735-07:00Podcast panaceaSo I've been icky sicky for about fourish days now, and I'm pretty sure that Ira Glass is just as effective as modern medicine. Plus, I hate taking actual medicine.becktronbexbeckyhttp://www.blogger.com/profile/10919881162870837083noreply@blogger.com0tag:blogger.com,1999:blog-3518963993058250138.post-68239434146033759222008-09-01T10:22:00.004-07:002008-09-01T10:31:02.168-07:00My Look-Alikes<center><a href="http://www.myheritage.com/collage" title="MyHeritage - free family trees, genealogy and face recognition" alt="MyHeritage - free family trees, genealogy and face recognition" target="_blank"><img src="http://storage.myheritagefiles.com/N/storage/site1/files/72/37/12/723712_4503038a52cb8409en8h10.JPG" width="500" height="574" border="0" ></a></center>becktronbexbeckyhttp://www.blogger.com/profile/10919881162870837083noreply@blogger.com2tag:blogger.com,1999:blog-3518963993058250138.post-16601840705551852742007-12-01T10:18:00.000-07:002007-12-01T10:34:41.385-07:00Once finals are over...This is what I'm going to do:<br /><br />1. Clean my room<br />2. Re-read The Fountainhead<br />3. Get back on the "I'm gonna learn Russian" bandwagon<br />4. Buy my sister a birthday present<br />5. Spend more time doing yoga<br />6. Consider cutting my hair<br />7. Maybe wash my car... but probably not<br />8. Schedule an optometrist appointment<br />9. Bake everybody bread<br />10. Organize my office<br />11. Enjoy not having classes<br />12. Celebrate the end of JMC 301: How to kill a journalism student<br />13. Geek out over my cool classes next semester<br />14. Prove what a super dork I am by being a little excited about taking statistics<br />15. Perfect a redition of the Harlem shake<br />16. Have a Christmas party?<br />17. Start freelancing for the New Times<br />18. Wrangle up material for Grrrl Talk next semester<br />19. Make a list of stuff to shop for post-Christmas<br />20. Think on what a siren I am at least eighty billion times a daybecktronbexbeckyhttp://www.blogger.com/profile/10919881162870837083noreply@blogger.com2tag:blogger.com,1999:blog-3518963993058250138.post-41078802270101427722007-11-09T17:57:00.000-07:002007-11-09T19:00:40.000-07:00Here's What I Did TodayI interviewed for internships with the Phoenix New Times and Village Voice Media, and totally kicked bottom. I went to geology, happily due to my nice test results from Monday's exam. Sidenote: I have a "B" in geology and that sometimes makes me want to die. Back on track! I saw some former co-workers from the Republic, had a nice chat with them and realized how much I miss them. I went to The Blaze and did some music directorly work. I picked up my check! Then, as I was walking to the bank, I saw a tow truck pass by on University, and I thought to myself, "Hey cool, that car looks like Hansel." Sidenote: Hansel is the name of my car. Back on track! Then I realized that it was in fact my Hansel, with a big, fat, yellow paper on him declaring his state of towage. You see, I had been using a decal that a friend of mine found on the side of the road, and successfully gotten away with parking for a few weeks. <br /><br />Well, f-word me for trying to stick it to the man. I had to pay no less than $360 to reclaim my ugly baby Hansel and then walk from the Parking and Transit services building, near Fifth Street and College Avenue, all the way to Curry and Miller roads. Yeah. That's far. Good thing I was intensely infuriated and had generous reserves of energy to expend on that trek. Finally, after filling my quota for sexual harrassment, I arrived at the nasty, dusty lot where those godless beasts took my car. And you know what I had to do? I had to wear a hideous, reflective orange vest. I kind of hate you ASU Parking and Transit. What did I ever do to you?!<br /><br />Then I sold some cds to FYE and applied to work at Zia. Now I have a mountain of homework to attend to because it's that time in the semester where all of my professors decide that excessive assignments will do my body good. Hey fall semester, feel free to end... like now. Which reminds me! I'll be registering for spring classes next Friday and I've got a pretty sweet lineup planned. I'm going to take statistics for social workers, station operations, news editing, media ethics and one of those internships that I interviewed for today. Yowza.<br /><br />If you feel compelled to give me exorbitant amounts of money, give in to your impulse.becktronbexbeckyhttp://www.blogger.com/profile/10919881162870837083noreply@blogger.com2tag:blogger.com,1999:blog-3518963993058250138.post-47445238546368870242007-11-07T08:31:00.000-07:002007-11-07T08:33:39.570-07:00This is a genuine question.How could someone not be in love with me?<br /><br />In all honesty, how?becktronbexbeckyhttp://www.blogger.com/profile/10919881162870837083noreply@blogger.com0tag:blogger.com,1999:blog-3518963993058250138.post-75108532180255857232007-10-06T17:04:00.000-07:002007-10-06T17:13:35.770-07:00Creeps GaloreI went to Safeway and purchased some garlic bread, because I was famished. I drove out of the parking lot and was making a left onto Broadway. Waiting quasi-patiently, I sat in my car, listening to the new Jose Gonzalez, which has made me a complete human, and two guys are attempting to walk by me on the sidewalk. Yes, I'm one of those jerks who pulls into crosswalks and walkways, effectively blocking pedestrians. But, guy #1 peers into my front passenger window, which was rolled down, and says, "Hey sweetie, could we get past?" I said, "Oh yeah, sure." Then guy #1 leans on my car, shoves his head through the window, and says, "You look pretty cute today. My name's Mike. How're you?" Okay, first of all, why in god's name would someone feel like he could just stick his face in my car? Secondly, I look cute today? Today as opposed to what day? Why are you an insane crackhead inside my car? Then he sort of just leaned on my car, expecting me to introduce myself, because intersections are great places to socialize. I said, "I'm trying to drive across the street." He took his creepy face out of my car, grinned, and then joined guy #2 walking eastbound on Broadway. <br /><br />I don't even know.becktronbexbeckyhttp://www.blogger.com/profile/10919881162870837083noreply@blogger.com3tag:blogger.com,1999:blog-3518963993058250138.post-37976084634949455572007-09-21T08:24:00.001-07:002007-09-21T18:49:34.525-07:00Yep, still cooler than youI got paid to go see Pinback, you can read about it <A HREF="http://www.azcentral.com/ent/music/articles/0921pinback.html">here!</A>becktronbexbeckyhttp://www.blogger.com/profile/10919881162870837083noreply@blogger.com0tag:blogger.com,1999:blog-3518963993058250138.post-5800374704821949132007-09-20T20:53:00.001-07:002007-09-20T20:55:10.492-07:00For the past few days, I've sort of started spouting words in random languages while attempting to speak English. It's pretty awesome. I think I should sleep more and stop thinking in languages that aren't my mother tongue.becktronbexbeckyhttp://www.blogger.com/profile/10919881162870837083noreply@blogger.com0tag:blogger.com,1999:blog-3518963993058250138.post-43636712636396538622007-09-17T15:03:00.000-07:002007-09-17T15:05:03.824-07:00My Pinback inteview is up on azcentral! You can read it <a href="http://www.azcentral.com/ent/music/articles/0917pinback.html" target="_blank">here!</a>becktronbexbeckyhttp://www.blogger.com/profile/10919881162870837083noreply@blogger.com0