In a never-ending quest to delight users with fresh, relevant features, Bing has been improving their innovation rate and code velocities.  Four years ago, Bing engineers deployed new features once a month. To accelerate feature deployment and innovation, we have invested much effort in overcoming software engineering challenges. The monthly deployment cadence has been gone for some time, taking with it both the old culture and most of the...
Read More

This month, we released a new Bing image search experience designed to help customers be inspired, learn more, and do more with image search. We discover billions of images on the Internet, but understanding the searches and providing helpful information for each image is an enormous challenge. In this blog post, my colleagues Arun Sacheti and Eason Wang provide a deeper look at how we built the image graph that allows us to automatically derive...
Read More

In the past, we’ve provided an in-depth view of how Bing.com improves relevance for users in web and image search, along with ways we’ve improved experimentation. Leveraging technology similar to search, the team has embarked on the relatively new area we call “Bing Predicts.” This feature analyzes web activity, social sentiment, and other signals to predict the outcomes of events. My colleague, Walter Sun, the technical lead for the project...
Read More

In today’s post we take a look at how the Bing ranking algorithm considers content quality for ranking web documents. There are often many web documents on the web that are relevant for any given user query and an effective ranking algorithm should consider not only relevance but also the authority, utility and presentation of the content in order to provide the best search results. My colleague Michael Basilyan will provide some insights on how...
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...
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...
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...
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...
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...
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...
Read More