Archive for December, 2009

Earlier this year we simplified the process for monetizing your blog by adding a “Monetize” tab in the Blogger app. We started with AdSense, which allows you to add contextual advertising to your pages; more recently we added AdSense for Feeds to help you generate revenue from the distribution of your blog via RSS and Atom. Today we launched a third option: direct integration with Amazon Associates to search Amazon’s product catalog and add links to products that earn you commissions when your readers buy products you recommend.

With this feature, you can search Amazon directly from the Blogger editor and add pictures and links to Amazon products right into your posts. Your readers will earn you commissions whenever they buy the products you recommend, and if you don’t already have an Amazon Associates account, you can sign up for one for free without leaving Blogger.

If you’ve ever written a blog post about a book, recommended a gadget, or reviewed a toy you bought for your kids, you’ve likely gone through the process of drafting the post, opening up a separate window to go to find a site that sells the product, then going back to Blogger to paste the link to the product into the post editor.

Starting today, you can search the Amazon product catalog without leaving the Blogger interface and insert links to the products you find into your posts. Not only is the process of linking to products more efficient, but Amazon makes it easy for you to earn money whenever your readers actually buy the products you write about. This is known as an “affiliate program”, and it’s designed to let you recommend products you like to your audience — if they buy the product, you’ll earn a commission on that purchase. (For more on affiliate programs in general, here is a good overview at ProBlogger from this summer, and Darren’s “11 Lessons Learned” post about Amazon Associates is a good review of how to get the most out of the program.)

To get started, click on the Monetize tab for your blog and click “Amazon Associates”. Walk through the setup wizard, and add the Product Finder once you’re done.



Now for the fun part: when you are writing a post on Blogger, you’ll see an Amazon gadget to the right of your post editor (the “Product Finder”). You can search the Amazon product catalog from within Blogger — type in the name of the product you are writing about, and insert a link to the product, an image of the product, or an iframe containing the image, price details and a “buy it now” button. Every link that’s created contains your unique Associates ID, ensuring that Amazon will credit you for any purchases that result from readers clicking the link on your blog.



If you’re an existing Amazon Associate, completing this setup simply makes the Product Finder available on Blogger for you — you continue to earn the same referral rate from Amazon. New Associates receive the same referral rate from Amazon that they would have received if they signed up directly. If you’re not interested in earning a referral, you can still install the Product Finder: from the “Amazon Associates” page under the Monetize tab, click “I'll do this later — show me more Amazon options” and then click “Add the Product Finder” button.

A quick note about trust: affiliate programs work well when readers trust you. You should avoid promoting products simply because of the referral fee you might earn — readers may lose some of that trust if they sense your posts exist solely to make you money. You may also want to disclose to your readers that you will earn a commission on their purchase — some readers even prefer knowing that you benefit from their business.

There’s more information about this integration at Amazon.com, and the Amazon Associates blog has some more details. This integration is the result of months of collaboration between the engineers at both companies, and we’re very excited to share the results of this collaboration with you. Happy blogging!

John Resig (@jeresig) of Mozilla, creator of the popular jQuery JavaScript library, stopped by Yahoo! on Friday for a BayJax meetup and delivered a three-part tech talk, “Testing, Performance Analysis, and jQuery 1.4″.

In the first part of the talk, John reviewed the range of tools available to frontend engineers for unit testing and for analyzing the performance of code. In the latter case, he argues for going beyond pure speed-based benchmarks to structural analyses of performance. By looking at structure, the jQuery team was able to identify and correct bottlenecks, resulting in major performance improvements in the upcoming 1.4 release.

In the second part of the talk (beginning at 49:20 in the video), John reviews some of those jQuery 1.4 changes. In the short third section (beginning at 1:03:15), he looks at some interesting trends he’s noticed in the practical application of new HTML 5 elements — especially in older browsers.

If the video embed below doesn’t show up correctly in your RSS reader of choice, be sure to click through to watch the high-resolution version of the video on YUI Theater.

• Download video (m4v)
• Download slides
• A high-resolution, transcripted version of this talk is available on the YUI Theater site

Other Recent YUI Theater Videos:

• Luke Smith: Events Evolved — YUI engineer Luke Smith provides a deep introduction to the YUI 3 event system including its support for DOM events, event delegation, synthetic events, and custom events.
• Todd Kloots: YUI 3 Sugar — YUI engineer Todd Kloots presents an indispensable introduction to YUI 3’s powerful suite of tools.
• Douglas Crockford: The State and Future of JavaScript — Yahoo! JavaScript Architect Douglas Crockford discusses the recent ECMA5 development process and efforts to improve the language in the future.
• Brendan Eich: ECMA Harmony and the Future of JavaScript — Brendan Eich, the creator of the world’s most popular programming language, talks about the struggle over the ES4 proposal and how it resulted in a specific set of proposals for ES5.

Subscribing to YUI Theater:

• YUI Theater RSS feed
• YUI Theater on iTunes

Posted by Mike Schuster, Google Research and Kaisuke Nakajima, Google Japan

On December 7th, we launched our new Japanese voice search system (音声検索), which has been available for various flavors of English since last year and for Mandarin Chinese for the past two months. The initial Japanese system works on the Android platform and also through the Google Mobile App on the iPhone as announced in a Japanese blog and a general explanation on how to get started. For developers who want to make use of the speech recognition backend for their own Android applications there is a public API (recognizer intent API) described here.

Although speech recognition has had a long history in Japan, creating a system that can handle a problem as difficult as voice search is still a considerable challenge. Today, most speech recognition systems are large statistical systems that must learn two models from sets of examples, an acoustic model and a language model. The acoustic model represents (statistically) the fundamental sounds of the language, and the language model statistically represents the words, phrases, and sentences of the language. The acoustic model for Japanese voice search was trained using a large amount of recorded Japanese speech, with the associated transcriptions of the words spoken. The language model for Japanese voice search was trained on Japanese search queries.

While speech recognition systems are surprisingly similar across different languages, there are some problems that are more specific to Japanese. Some of the challenges we faced while developing Japanese voice search included:

• Spaces in Japanese text
  As we looked at some popular search queries in Japan we saw that Japanese often doesn't have spaces but sometimes it does. For example, if a user searches for Ramen noodles near Tokyo station they will often type: "東京駅 ラーメン" with a space in between Tokyo station and Ramen -- therefore, we would like to display it in this way as well. Getting the spaces right is difficult and we continue working to improve it.
• Japanese word boundaries
  Word boundaries in Japanese are often not clear and subject to interpretation as most of the time there are not spaces between words. This also makes the definition of the vocabulary (the words that can be recognized theoretically) extremely large. We deal with this problem by finding likely word boundaries using a statistical system which also helps us limit the vocabulary.
• Japanese text is written in 4 different writing systems
  Japanese text as written today uses Kanji, Hiragana, Katakana & Romaji, often mixed in the same sentence and sometimes in the same word, depending on the definition of a word. Try these example queries to see some interesting cases: "価格.com", "マーボー豆腐", "東京都渋谷区桜丘町26-1". We try to display the output in a way that is most user-friendly, which often means to display it as you would write it down.
• Japanese has lots of basic characters and many have several pronunciations depending on context
  To be able to recognize a word you need to know its pronunciation. Western languages in general use only ASCII or a slightly extended set of characters which is relatively small (less than 100 total in most cases). For Japanese the number of basic characters is the union of all basic characters from the four writing systems mentioned above, which is several thousands in total. Finding the correct pronunciations for all words in the very large voice search vocabulary is difficult and is often done using a combination of human effort and automatic statistical systems. This is even more difficult in the Japanese case as the number of basic characters is higher and there are a vast number of exceptions, for example consider the case of: "一人" (hitori) versus "一人前" (ichininmae). Although the phrases look very similar they have completely different pronunciations.
• Encoding issues
  Japanese characters can be written in many encoding systems including UTF-8, Shift_JIS, EUC-JP and others. While at Google we try to use exclusively UTF-8 there are still interesting edge cases to deal with. For example some characters exist in different forms in the same encoding system. Compare for example "カナ"　and "ｶﾅ" -- they both say "kana" and mean the exact same thing, the first in full-width and the second in half-width. There are numerous similar cases like this in Japanese that make normalization of the text data more difficult.
• Every speaker sounds different
  People speak in different styles, slow or fast, with an accent or without, have lower or higher pitched voices, etc. To make it work for all these different conditions we trained our system on data from many different sources to capture as many conditions as possible.

The challenges listed above are just a small portion of what we dealt with while building the Japanese voice search system. Over time, we are committed to improve the system as much as and as quickly as possible to make speech, in addition to the keyboard, a user-friendly input modality on mobile devices. We will push a first set of improved models this week.

Here at Edmunds (Edmunds.com) we just launched a redesign of
Inside Line (InsideLine.com), our automotive enthusiast web site, and we are using the YUI JavaScript library extensively.

Some of the YUI utilities & widgets used on Inside Line:

• Yahoo/Dom/Event
• Animation Utility
• Connection Manager
• ImageLoader
• JSON
• Selector
• Carousel
• TabView

We (the Frontend team) started out with YUI 2.7.0 JavaScript core and built our own JavaScript user interface library on top of it to encapsulate site-specific components and functionality. Our library takes advantage of YUI’s core utilities, including Dom, Event, Connection Manager, and Animation.

We are using Dom and Event extensively to handle DOM interaction, event listeners and custom event handling. The YUI Connection Manager is handling all of our Ajax implementations, including our custom search widgets. We are also using many of the YUI widgets on Inside Line, including TabView and Carousel, with custom skins. The YUI ImageLoader helped us improve page performance and meet our strict performance requirements.

We chose YUI because of its great documentation, thorough testing, and the scope and depth of its offerings. The library is easy to learn, understand, and implement. The modularity of the system fits in well with our design principles, and the API and custom events make it extremely extensible and easy to integrate.

Some Highlights

Multimedia Spotlight (tabview, carousel) from InsideLine.com:

Image and Video Galleries (core, JSON and Carousel):

Ajax Search Widgets (Dom, Event, Connection Manager):

Do you have a YUI Imlementation you’d like to share on YUIBlog? Check out our contribution guidelines — we’d love to hear from you.

I wonder if it bothered you as much as it bothered me that so much of the content in the My Yahoo! Content Gallery did not have a unique icon associated with it. Every time I clicked on “Add Content” and did a search, most of the content coming back had a generic icon. I want my content searching more sexy and fun. Give me a logo! Give me an image! Make it tasty!

So we went ahead and added over 150,000 new icons to the content database. When you do your search for “apple” to find out about the latest on the rumored Apple Tablet or top iTunes songs or for “book reviews” to find that perfect gift for the holidays, you’ll see new icons show up next to content.

 

Apps showcased in this post:

• Add Yahoo! News: Apple/Macintosh News
• Add iTunes Top 10 Songs
• Add Powell’s Books: Review-A-Day 

Michael
- My Yahoo! Team Lead

Posted by Michele Covell, Vision Research Team

Recently, Google's research groups reviewed over 140 grant proposals across sixteen different research areas. During this process, we identified a number of strategic research topics. These topics represent critical areas of research for Google in collaboration with our university partners.

We'll be examining several of these topics in future posts but we'd like to begin by raising some of the research challenges we face in our multimedia endeavors:

• Large scale annotation: How can we learn from large, noisy sets of image/video data to automatically get human-level accurate models for label annotation?
  The images and videos that are available on the web provide massive data sets. We have some very noisy labels on that set, in terms of possible content. We have labels based on popularity of an item when considered for a particular search, on anchor text and other context, and on labels given to other content that is often associated with each item. The challenge is to make use of the sheer volume of available data to improve our recognition models and to carry appearance models from one media type to another. Further, we must be able to handle the variability in appearance and in the labels themselves.
• Image/Audio/Video Representation: How can we improve our understanding of low level representations of images that goes beyond bag of words modeling?
  Much of the current work in labeling and retrieval is based on fairly simple local descriptions of the content, putting the emphasis on classifier learning from combinations of simple models. While this classifier approach has been useful, we should also examine the basic features that we are using, to see if we can better characterize the content. Providing better inputs into our learning algorithms should reduce the size of the space over which we need to search. Possible examples include shape modeling in images, better texture/color models, and descriptions of soft segmentations of regions.
• Localization of image-/video-level labels to spatial/temporal portions of the content: Can we automatically associate image and video labels with specific portions of the content?
  The most obvious examples in this area are labels like "dog" and "explosion". However, can we also localize more complex concepts like "waves" or "suspense"? Alternately, can we automatically distinguish between labels, based on how well we are able to localize them to a particular place or time within the content?
• Large scale matching / Hashing: Can we identify matching techniques to deal with large datasets?
  We need image, video, and audio matching techniques that can effectively deal with large datasets, embedded in high-dimensional descriptor spaces, in sub-linear time. Of special interest are methods that can efficiently handle a wide range of recall/precision needs without massive increases in the data-structure sizes that are used.

We expect these questions to keep us busy for some time.

Posted by Agustina Dates, Google Books Online Team

In A Room of One's Own, Virginia Woolf poses a question that's had a major impact on discussions of writing and gender over the past and current century: Does writing have a "gender"? Does one’s gender leave a trace in words? Could you tell the gender of a writer just by reading what they've written?

Emily Dickinson was born on this day in the year 1860. And while in most of Dickinson's poems it's very obvious to me that they came from the pen of a woman, in others she seems to make her gender imperceptible.



My first introduction to Emily Dickinson was when reading her work in Spanish, and the translator for the book was another woman: the great Argentinean writer Silvina Ocampo. Here things become more complicated... how do we translate? And if writing has a gender, does a translation have it too? What is the task of a translator? Grammar has its own agenda and changes according to the language, so the traces of gender that appear in the original version of a poem may disappear in its translation. At other times, I've read translations that have unearthed traces of gender that were not evident in the original version.

Google Books has scanned books in over 100 languages, and you can search for titles in a specific language by selecting it on our advanced search options. If you speak a language other than English, why don’t you give it a try and look for versions of your favorite books in different languages? You will see it’s a totally different experience!

At Google, we've found that users are more likely to click on short URLs in the search results than on longer ones. However, if your site is like most, there are multiple URLs that will take users to the same product page, including some that are longer because they include tracking information, category navigation, or other parameters. For example, both

by Chang Kim, Product Manager, Blogger

More and more of you are using Google Chrome (more than 30 million active users now!), and we want to let you know that a Blogger extension is included in the Chrome Extensions gallery. The BlogThis! Chrome extension is available now, one of several hundred extensions to be found in the Chrome extensions gallery.

Using the BlogThis! Chrome extension, you can start writing a blog post in one click. Whenever you are inspired by a web page you are looking at and want to blog about it, just click on the BlogThis! button on your Chrome toolbar, and the Blogger post editor opens up with a pre-populated link to the web page you were on. If you want to include any text in your post, simply highlight it before clicking on the BlogThis! button. Edit the post as you'd like, and publish it instantly or save it as a draft for future posting.


To try out the BlogThis! Chrome extension, first switch to Google Chrome BETA (if you are not already on that version), and install the BlogThis! extension by clicking on "Extensions" on your browser toolbar or visiting the BlogThis! extension homepage. Note: Extensions are only available for Chrome on the PC and Linux; Extension support on Chrome for Mac is under development.