Bing blogs

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Search Quality Insights Blog

  • April
    21

    Assessing Image Quality

    A few months ago we provided a behind the scenes look into how Bing is improving image search quality. In this post we wanted to explore some additional techniques we are employing to deliver high quality image results. My colleague Eason Wang will give you a closer look at how we are incorporating aesthetics to deliver more beautiful image results in Bing. - Dr. Jan Pedersen, Chief Scientist, Bing and Information Platform R&D As you can see below, two images about the same topic can be very different. A picture I took of my cat and one taken by a professional, have subtle yet important differences. If someone is simply searching for images of cats, the quality of the images we showcase make a big difference in whether a search session is successful. In this post we will explore the characteristics and techniques we employ to ensure that we are doing all we can to surface high quality images without compromising the relevance and context of your searches. Example of high quality image: Example of amateur image: Understanding Image Quality Image understanding is how we describe the overall approach that Bing employs to understand images. Below, we outline... Read More A few months ago we provided a behind the scenes look into how Bing is improving image search quality. In this post we wanted to explore some additional techniques we are employing to deliver high quality image results. My colleague Eason Wang will give you a closer look at how we are incorporating aesthetics to deliver more beautiful image results in Bing. - Dr. Jan Pedersen, Chief Scientist, Bing and Information Platform R&D As you can see below, two images about the same topic can be very different. A picture I took of my cat and one taken by a professional, have subtle yet important differences. If someone is simply searching for images of cats, the quality of the images we showcase make a big difference in whether a search session is successful. In this post we will explore the characteristics and techniques we employ to ensure that we are doing all we can to surface high quality images without compromising the relevance and context of your searches. Example of high quality image: Example of amateur image: Understanding Image Quality Image understanding is how we describe the overall approach that Bing employs to understand images. Below, we outline... Read More
  • November
    22

    Deep Learning for Image Understanding in...

    A few months ago we provided a behind the scenes look into how Bing is improving image search quality. In this post we wanted to take the opportunity to further that discussion highlighting some recent outreach we did with researchers to explore new approaches to improving image search quality. My colleague Eason Wang will give you a closer look at how we are taking advantage of Deep Learning and entity understanding to deliver more relevant, useful and beautiful image results in Bing. - Dr. Harry Shum, Corporate Vice President, Bing R&D Our colleague Meenaz Merchant gave an overview of Bing image search a few months ago. Today we will talk about the technology behind our approach. Rooted in the fundamental techniques of web search, traditional image search has relied heavily on text information such as surrounding text, title and URLs to deliver relevant results. However, in the age of social media and self-expression this approach has become less effective. A large percentage of images are shared with little or no text information (as shown in the example below). This presents new challenges propelling us to examine the properties of the image itself to provide more relevant... Read More A few months ago we provided a behind the scenes look into how Bing is improving image search quality. In this post we wanted to take the opportunity to further that discussion highlighting some recent outreach we did with researchers to explore new approaches to improving image search quality. My colleague Eason Wang will give you a closer look at how we are taking advantage of Deep Learning and entity understanding to deliver more relevant, useful and beautiful image results in Bing. - Dr. Harry Shum, Corporate Vice President, Bing R&D Our colleague Meenaz Merchant gave an overview of Bing image search a few months ago. Today we will talk about the technology behind our approach. Rooted in the fundamental techniques of web search, traditional image search has relied heavily on text information such as surrounding text, title and URLs to deliver relevant results. However, in the age of social media and self-expression this approach has become less effective. A large percentage of images are shared with little or no text information (as shown in the example below). This presents new challenges propelling us to examine the properties of the image itself to provide more relevant... Read More
  • August
    23

    A Behind the Scenes Look at How Bing is...

    In this post we wanted to take the opportunity to give you a behind the scenes look at the ongoing work we are doing to improve image search quality at Bing. This blog will give you an overview of the many years of work done in Bing Research and Development in collaboration with Microsoft Research in our quest to deliver the most relevant images possible. My colleague Meenaz Merchant will give you a closer look at our approach and how that compares to Google in order to showcase the respective strengths and challenges moving forward. We hope this post will serve to spark a conversation on image search that helps the search industry move forward and in the end benefit you when you’re searching for images. - Dr. Harry Shum, Corporate Vice President, Bing R&D Every day Bing receives millions of searches from across the Web, and nearly 10 percent of those searches are for images. With 40 percent of search results including some kind of visual component, we know that people are more likely to click on a web page that includes images or videos. People search for a variety of images ranging from celebrities, artwork, products, fashion, attractions, and landscapes to clip-art... Read More In this post we wanted to take the opportunity to give you a behind the scenes look at the ongoing work we are doing to improve image search quality at Bing. This blog will give you an overview of the many years of work done in Bing Research and Development in collaboration with Microsoft Research in our quest to deliver the most relevant images possible. My colleague Meenaz Merchant will give you a closer look at our approach and how that compares to Google in order to showcase the respective strengths and challenges moving forward. We hope this post will serve to spark a conversation on image search that helps the search industry move forward and in the end benefit you when you’re searching for images. - Dr. Harry Shum, Corporate Vice President, Bing R&D Every day Bing receives millions of searches from across the Web, and nearly 10 percent of those searches are for images. With 40 percent of search results including some kind of visual component, we know that people are more likely to click on a web page that includes images or videos. People search for a variety of images ranging from celebrities, artwork, products, fashion, attractions, and landscapes to clip-art... Read More
  • August
    08

    Large Scale Experimentation at Bing

    Experimenting at large scale is fundamental for improving Bing. Last June, we published a blog in this Search Quality Insights series titled Experimentation and Continuous Improvement at Bing , which covered a specific type of experiments known as interleaving. In this blog, Dr. Ronny Kohavi describes our broader online experimentation efforts at large scale and includes compelling examples that illustrate the power of these efforts, e.g., he shows how a controlled experiment at large scale of a relatively small feature change can lead to many millions of dollars in revenue. Ronny’s blog is a brief summary of a research paper titled Online Controlled Experiments at Large Scale which will he will present next week at the international conference on Knowledge Discovery and Data Mining ( KDD 2013 ). The paper has already received positive feedback by well-known experts in this field, and we’re sharing their comments in this blog with their permission. Dr. Harry Shum, Corporate Vice President, Bing R&D Microsoft’s Bing search engine steadily increased US market share and improved financial performance in the four years since its launch. What the numbers don’t... Read More Experimenting at large scale is fundamental for improving Bing. Last June, we published a blog in this Search Quality Insights series titled Experimentation and Continuous Improvement at Bing , which covered a specific type of experiments known as interleaving. In this blog, Dr. Ronny Kohavi describes our broader online experimentation efforts at large scale and includes compelling examples that illustrate the power of these efforts, e.g., he shows how a controlled experiment at large scale of a relatively small feature change can lead to many millions of dollars in revenue. Ronny’s blog is a brief summary of a research paper titled Online Controlled Experiments at Large Scale which will he will present next week at the international conference on Knowledge Discovery and Data Mining ( KDD 2013 ). The paper has already received positive feedback by well-known experts in this field, and we’re sharing their comments in this blog with their permission. Dr. Harry Shum, Corporate Vice President, Bing R&D Microsoft’s Bing search engine steadily increased US market share and improved financial performance in the four years since its launch. What the numbers don’t... Read More
  • August
    07

    Safer Web Exploration with Bing

    The internet’s massive reach and ever growing accessibility makes it an attractive place for cyber-criminals, who use it to distribute malware to unsuspecting users. To deliver malicious software to the user at large scale, savvy hackers increasingly try to game search engines to amplify the effect of their exploits by targeting frequently visited sites. At Bing our job is to not only deliver relevant results, but also provide a safe searching experience. In this blog my colleague Igor Rondel in our Index team provides an overview of the malware detection and protection technology that we’ve been developing and are constantly improving in order to maximize the safety of your searches. Dr. Harry Shum, Corporate Vice President, Bing R&D Overview Over the past decade both malware and malware detection technologies have evolved by leaps and bounds, pushing each other to innovate and find new ways to circumvent the other. The more advanced malware distributors attempt to evade detection by providing search engines with different content than the people visiting the site. But in order to do this, they need to know the request is coming from a search engine, so the... Read More The internet’s massive reach and ever growing accessibility makes it an attractive place for cyber-criminals, who use it to distribute malware to unsuspecting users. To deliver malicious software to the user at large scale, savvy hackers increasingly try to game search engines to amplify the effect of their exploits by targeting frequently visited sites. At Bing our job is to not only deliver relevant results, but also provide a safe searching experience. In this blog my colleague Igor Rondel in our Index team provides an overview of the malware detection and protection technology that we’ve been developing and are constantly improving in order to maximize the safety of your searches. Dr. Harry Shum, Corporate Vice President, Bing R&D Overview Over the past decade both malware and malware detection technologies have evolved by leaps and bounds, pushing each other to innovate and find new ways to circumvent the other. The more advanced malware distributors attempt to evade detection by providing search engines with different content than the people visiting the site. But in order to do this, they need to know the request is coming from a search engine, so the... Read More
  • June
    12

    Experimentation and Continuous Improvement...

    One technical problem in Web search is how best to measure the quality of search results. Our powerful machine learning systems correct the spelling of your query , interpret your search intent, differentiate quality pages from junk , rank documents from our index of tens of billions of documents, and optimize whole-page layout . These systems, and many more, must all be optimized towards user satisfaction. The problem is that there is no perfect measure of objective user satisfaction, so we use surrogate measures. Surprisingly, once your machine learning systems are powerful enough, your choice of surrogate measure has a strong influence on the kind of results you return. In this blog post Nick Craswell and Filip Radlinski describe some of the issues motivating their award winning research paper on measuring search results which was presented at the international conference WSDM-2013. - Dr. Harry Shum, Corporate Vice President, Bing R&D In information retrieval (IR) research there is a strong focus on relevance measurement. For example this year at SIGIR , which is one of the largest research conferences in the area, there are two sessions dedicated to IR evaluation, plus... Read More One technical problem in Web search is how best to measure the quality of search results. Our powerful machine learning systems correct the spelling of your query , interpret your search intent, differentiate quality pages from junk , rank documents from our index of tens of billions of documents, and optimize whole-page layout . These systems, and many more, must all be optimized towards user satisfaction. The problem is that there is no perfect measure of objective user satisfaction, so we use surrogate measures. Surprisingly, once your machine learning systems are powerful enough, your choice of surrogate measure has a strong influence on the kind of results you return. In this blog post Nick Craswell and Filip Radlinski describe some of the issues motivating their award winning research paper on measuring search results which was presented at the international conference WSDM-2013. - Dr. Harry Shum, Corporate Vice President, Bing R&D In information retrieval (IR) research there is a strong focus on relevance measurement. For example this year at SIGIR , which is one of the largest research conferences in the area, there are two sessions dedicated to IR evaluation, plus... Read More
  • April
    24

    Ten Blue Links No More: Dynamic Page Sizing

    Most search engines still provide a fixed number of web links on the search results page, typically 10 blue links in addition to instant answers. But are 10 blue links the right number of links for all queries and scenarios? Intuitively it doesn’t seem so. For example, showing fewer search results in navigational queries may enable users get to their desired URL faster. Also, users may benefit from seeing more blue links when they return to a search results page after clicking on the browser’s back button. In this blog my colleague Ronny Kohavi explores the benefits of having a dynamic number of search results and provides valuable insights that led Bing to challenge the status quo. - Dr. Harry Shum, Corporate Vice President, Bing R&D At Bing, our central mission is to help users spend less time searching and more time getting things done. To that end, we run over 100 concurrent controlled experiments at a given point in time, looking for novel ways to improve the user experience. While most ideas fail, and the statistics are humbling (e.g., see this talk ), this blog showcases an interesting example of ongoing changes we make to Bing. Since the early days of... Read More Most search engines still provide a fixed number of web links on the search results page, typically 10 blue links in addition to instant answers. But are 10 blue links the right number of links for all queries and scenarios? Intuitively it doesn’t seem so. For example, showing fewer search results in navigational queries may enable users get to their desired URL faster. Also, users may benefit from seeing more blue links when they return to a search results page after clicking on the browser’s back button. In this blog my colleague Ronny Kohavi explores the benefits of having a dynamic number of search results and provides valuable insights that led Bing to challenge the status quo. - Dr. Harry Shum, Corporate Vice President, Bing R&D At Bing, our central mission is to help users spend less time searching and more time getting things done. To that end, we run over 100 concurrent controlled experiments at a given point in time, looking for novel ways to improve the user experience. While most ideas fail, and the statistics are humbling (e.g., see this talk ), this blog showcases an interesting example of ongoing changes we make to Bing. Since the early days of... Read More
  • April
    03

    A Deeper Look at Autosuggest

    Autosuggest enables people to select a query from a pull-down menu of suggestions when they start typing a query. You often find the query you want in the menu and select it, saving you extra keystrokes while reducing a chance of misspelling. It is a feature that people may take for granted but upon examination it involves a tremendous amount of technical sophistication and computational horsepower. In this blog Antonio Gulli who is our development manager for Autosuggest discusses how we implement the feature focusing on a new Autosuggest advancement we recently shipped called “ Ghosting ” which further optimizes the user experience. - Dr. Harry Shum, Corporate Vice President, Bing R&D As we’ve all experienced, sometimes search can be a hit-or-miss experience. You enter a query, scan through the results and refine your search until you find what you’re looking for. Autosuggest attempts to accelerate the process by providing a list of suggestions as soon as you start typing. Let’s look at the following example: In a previous post , we highlighted how Ghosting in Autosuggest lets you get more done in less time. In this follow up, we would... Read More Autosuggest enables people to select a query from a pull-down menu of suggestions when they start typing a query. You often find the query you want in the menu and select it, saving you extra keystrokes while reducing a chance of misspelling. It is a feature that people may take for granted but upon examination it involves a tremendous amount of technical sophistication and computational horsepower. In this blog Antonio Gulli who is our development manager for Autosuggest discusses how we implement the feature focusing on a new Autosuggest advancement we recently shipped called “ Ghosting ” which further optimizes the user experience. - Dr. Harry Shum, Corporate Vice President, Bing R&D As we’ve all experienced, sometimes search can be a hit-or-miss experience. You enter a query, scan through the results and refine your search until you find what you’re looking for. Autosuggest attempts to accelerate the process by providing a list of suggestions as soon as you start typing. Let’s look at the following example: In a previous post , we highlighted how Ghosting in Autosuggest lets you get more done in less time. In this follow up, we would... Read More
  • January
    03

    Building a State-of-the-Art Speller

    Bing’s Speller processes tens of millions of data points mined from searches, web pages, clicks and user actions to help you find the right “Schwarzenegger.” In close collaboration with Microsoft Research we have developed advanced machine learning models to build a great speller. The team working on speller relevance runs thousands of experiments every week – ranging from improving data freshness to improving ranking fundamentals – to deliver a better search experience. The core to building a better speller is marrying speed with relevance. In order to achieve this, Bing’s Speller handles tens of thousands of queries per second and computes corrections within tens of milliseconds. These spell corrections can be so good that they sometimes feel like magic! Would you have guessed the query “ terramazu ” is so delicious?  Read More Bing’s Speller processes tens of millions of data points mined from searches, web pages, clicks and user actions to help you find the right “Schwarzenegger.” In close collaboration with Microsoft Research we have developed advanced machine learning models to build a great speller. The team working on speller relevance runs thousands of experiments every week – ranging from improving data freshness to improving ranking fundamentals – to deliver a better search experience. The core to building a better speller is marrying speed with relevance. In order to achieve this, Bing’s Speller handles tens of thousands of queries per second and computes corrections within tens of milliseconds. These spell corrections can be so good that they sometimes feel like magic! Would you have guessed the query “ terramazu ” is so delicious?  Read More
  • September
    06

    Behind the Bing It On Challenge

    As I mentioned in my original post there has never been a more exciting or challenging time to be in the search space. The core to a great search engine has been and will always remain the same: delivering relevant, comprehensive and unbiased results that people can trust. We use thousands of signals from queries to documents and user feedback to determine the best search results and in turn make hundreds of improvements to our features every year, from small tweaks to core algorithm updates. In this series, you have heard from our Chief Scientist Dr. Jan Pedersen who summarized our efforts on whole page relevance , and Dr. Richard Qian who covered the techniques we employ to reduce junk links . More recently, Dr. William Ramsey described how we reduce defects in links and related searches and Dr. Kieran McDonald’s post examined answers quality utilizing specialized classifiers. These are just a few examples of the myriad algorithm changes that we’ve developed to enhance Bing over the years. With all of these changes we’ve made some great progress in Bing search quality for our customers. You will notice we have released a fun, non-scientific tool for customers... Read More As I mentioned in my original post there has never been a more exciting or challenging time to be in the search space. The core to a great search engine has been and will always remain the same: delivering relevant, comprehensive and unbiased results that people can trust. We use thousands of signals from queries to documents and user feedback to determine the best search results and in turn make hundreds of improvements to our features every year, from small tweaks to core algorithm updates. In this series, you have heard from our Chief Scientist Dr. Jan Pedersen who summarized our efforts on whole page relevance , and Dr. Richard Qian who covered the techniques we employ to reduce junk links . More recently, Dr. William Ramsey described how we reduce defects in links and related searches and Dr. Kieran McDonald’s post examined answers quality utilizing specialized classifiers. These are just a few examples of the myriad algorithm changes that we’ve developed to enhance Bing over the years. With all of these changes we’ve made some great progress in Bing search quality for our customers. You will notice we have released a fun, non-scientific tool for customers... Read More
  • August
    20

    Minimizing Answer Defects

    Back in March, Jan Pedersen explored the topic of Whole Page Relevance . In the post, he outlined how Bing ranks media objects to deliver a rich set of results that go beyond just web pages using machine learning techniques. These objects include videos, images, maps, news items and assorted media objects or what we call answers. Today, Kieran McDonald will detail how we ensure that the answers are not defective and the results match the intent of your query. If you are interested in the wider topic of minimizing defects, also read William Ramsey’s recent blog: To Err is Human - Dr. Harry Shum, Corporate Vice President, Bing R&D. The core philosophy guiding our algorithm centers on presenting results that are most useful at the top of the page. In the simplest terms, we do this by ranking them in descending order based on the amount of clicks that a given result receives. Answers in turn must be competitive with the web results that they displace in order to justify their real estate and position within the page. While this may appear straightforward, we occasionally encounter instances where answers do not match user intent even though they receive a sufficient percentage... Read More Back in March, Jan Pedersen explored the topic of Whole Page Relevance . In the post, he outlined how Bing ranks media objects to deliver a rich set of results that go beyond just web pages using machine learning techniques. These objects include videos, images, maps, news items and assorted media objects or what we call answers. Today, Kieran McDonald will detail how we ensure that the answers are not defective and the results match the intent of your query. If you are interested in the wider topic of minimizing defects, also read William Ramsey’s recent blog: To Err is Human - Dr. Harry Shum, Corporate Vice President, Bing R&D. The core philosophy guiding our algorithm centers on presenting results that are most useful at the top of the page. In the simplest terms, we do this by ranking them in descending order based on the amount of clicks that a given result receives. Answers in turn must be competitive with the web results that they displace in order to justify their real estate and position within the page. While this may appear straightforward, we occasionally encounter instances where answers do not match user intent even though they receive a sufficient percentage... Read More
  • July
    02

    Friends and Experts

    The foundation of web search has been built on keywords, links and clicks pointing to pages. This approach is great for finding web sites but search is more than about simply finding pages. With the help of social networks, people are able to share nearly everything they do in digital form and offer their opinions on almost every conceivable topic. From real-time streams to social conversations, connections are created that present the opportunity to bring people into the search equation. Today Paul Yiu, Principal Group Program Manager for Bing Social , provides an overview of how we incorporated people into our latest release. No matter what query you submit to Bing, you may be amazed that some of your friends, influentials or experts often know something that you are searching for, in addition to high quality web documents that you always count on. - Dr. Harry Shum, Corporate Vice President, Bing R&D The goal here is to bring you a list of people that might be able to help you get more done. For instance, here is an example of how the Sidebar might help you plan the perfect adventure in Hawaii. Friends Who Might Know Depending on what your friends have been paying... Read More The foundation of web search has been built on keywords, links and clicks pointing to pages. This approach is great for finding web sites but search is more than about simply finding pages. With the help of social networks, people are able to share nearly everything they do in digital form and offer their opinions on almost every conceivable topic. From real-time streams to social conversations, connections are created that present the opportunity to bring people into the search equation. Today Paul Yiu, Principal Group Program Manager for Bing Social , provides an overview of how we incorporated people into our latest release. No matter what query you submit to Bing, you may be amazed that some of your friends, influentials or experts often know something that you are searching for, in addition to high quality web documents that you always count on. - Dr. Harry Shum, Corporate Vice President, Bing R&D The goal here is to bring you a list of people that might be able to help you get more done. For instance, here is an example of how the Sidebar might help you plan the perfect adventure in Hawaii. Friends Who Might Know Depending on what your friends have been paying... Read More
  • June
    21

    To Err is Human

    At Bing we’re committed to delivering you the best possible results. While teams of researchers, machine learning experts and data miners continually improve our core spelling and ranking components, the reality is that some defects have historically slipped through the cracks. This is partly because search relies on people to teach the system and, since people make mistakes, some defects are introduced. Our recent release, takes a step forward in addressing many of the most common defects that we find in the system. In this post, my colleague Bill Ramsey, Development Manager for Bing , will examine three categories where we’ve reduced the occurrence rate and severity of defects: URL queries, Recourse Links and Related Searches. Dr. Harry Shum, Corporate Vice President, Bing R&D One of the major sources of defects pertains to what we call URL queries. These are queries like “facebook.com” or “yahoo.com/mail” where the query looks like a URL. At first glance, you might think this is a simple problem for a search engine. After all, we have billions of URLs – how hard can it be to find a match on one? In reality, this type of query is... Read More At Bing we’re committed to delivering you the best possible results. While teams of researchers, machine learning experts and data miners continually improve our core spelling and ranking components, the reality is that some defects have historically slipped through the cracks. This is partly because search relies on people to teach the system and, since people make mistakes, some defects are introduced. Our recent release, takes a step forward in addressing many of the most common defects that we find in the system. In this post, my colleague Bill Ramsey, Development Manager for Bing , will examine three categories where we’ve reduced the occurrence rate and severity of defects: URL queries, Recourse Links and Related Searches. Dr. Harry Shum, Corporate Vice President, Bing R&D One of the major sources of defects pertains to what we call URL queries. These are queries like “facebook.com” or “yahoo.com/mail” where the query looks like a URL. At first glance, you might think this is a simple problem for a search engine. After all, we have billions of URLs – how hard can it be to find a match on one? In reality, this type of query is... Read More
  • June
    21

    Scientific Research Contributions (I)

    Researchers and engineers in Microsoft Research (MSR) and Bing R&D have been working closely together and have made significant contributions to the science of search. These contributions often start as basic research ideas published as scientific articles in international conferences and journals. These articles are reviewed by search experts from industry and academic institutions around the world, and only those that significantly advance the state of the art are accepted and published. Many of the concepts in these articles from Microsoft become specific techniques for enhancing the Bing search engine positively impacting millions of users. As an example, this week our researchers are participating in the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) 2012 ( www.cvpr2012.org ). CVPR is an international conference and an important event for the researcher and academic communities. Microsoft is presenting 41 scientific articles this week in CVPR that have already been reviewed by search experts covering a large number of topics such as ranking and image search. In particular, the article Salient Object Detection for Searched Web Images... Read More Researchers and engineers in Microsoft Research (MSR) and Bing R&D have been working closely together and have made significant contributions to the science of search. These contributions often start as basic research ideas published as scientific articles in international conferences and journals. These articles are reviewed by search experts from industry and academic institutions around the world, and only those that significantly advance the state of the art are accepted and published. Many of the concepts in these articles from Microsoft become specific techniques for enhancing the Bing search engine positively impacting millions of users. As an example, this week our researchers are participating in the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) 2012 ( www.cvpr2012.org ). CVPR is an international conference and an important event for the researcher and academic communities. Microsoft is presenting 41 scientific articles this week in CVPR that have already been reviewed by search experts covering a large number of topics such as ranking and image search. In particular, the article Salient Object Detection for Searched Web Images... Read More
  • March
    19

    Reducing Junk

    Everyone has experienced trying to follow a link that didn’t work. The link may have led to a page that is no longer available, or to a parked domain page. It can also be frustrating to find a search result page that has a missing or garbled description of where the link may take you. Considering the enormity of the web and the complexity of linking web documents , a key challenge for web search quality is the search engine’s ability to process, select and serve only good links with helpful descriptions. In this blog my colleague Richard Qian, Development Manager for Bing , provides an overview of our efforts to reduce junk links and junky or empty snippets. - Dr. Harry Shum, Corporate Vice President, Bing R&D To help clarify a few terms which we use in this blog, the following screenshot shows a typical search result with three common components: 1) title with a hyperlink. When we say links, we refer to this; 2) snippet is the description of the search result and 3) URL shows the address of the result page. a Removing Junk Links In search there’s perhaps nothing worse than clicking a search result only to get an error message in return. Or being... Read More Everyone has experienced trying to follow a link that didn’t work. The link may have led to a page that is no longer available, or to a parked domain page. It can also be frustrating to find a search result page that has a missing or garbled description of where the link may take you. Considering the enormity of the web and the complexity of linking web documents , a key challenge for web search quality is the search engine’s ability to process, select and serve only good links with helpful descriptions. In this blog my colleague Richard Qian, Development Manager for Bing , provides an overview of our efforts to reduce junk links and junky or empty snippets. - Dr. Harry Shum, Corporate Vice President, Bing R&D To help clarify a few terms which we use in this blog, the following screenshot shows a typical search result with three common components: 1) title with a hyperlink. When we say links, we refer to this; 2) snippet is the description of the search result and 3) URL shows the address of the result page. a Removing Junk Links In search there’s perhaps nothing worse than clicking a search result only to get an error message in return. Or being... Read More