Bing's New 'Deep Search' Feature Delivers More Complete Answers to Complex Search Queries

Bing has announced the launch of a new feature called “Deep Search” that aims to provide more relevant and detailed answers to complex search queries. The feature uses advanced natural language processing and deep learning techniques to understand the intent and context of the user's query and then generate a comprehensive response from multiple sources of information.

According to Bing, 'Deep Search' can handle queries involving multiple entities, attributes, comparisons, or conditions, such as “best gaming laptops under $1000” or “top 10 movies of 2023 as ranked by IMDb.” The feature can also answer questions that require reasoning or inference, such as “why is the sky blue” or “how to prevent global warming.”

The feature is designed to help users save time and effort by providing them with a single answer that covers all aspects of their query, rather than having to navigate through multiple web pages or links. The feature also allows users to refine or expand their query by using interactive filters or suggestions that appear below the answer.

Bing says 'Deep Search' is the result of years of research and development, and leverages the latest innovations in artificial intelligence and big data. The feature is currently available in English for select topics and domains such as products, movies, health, and education. Bing plans to expand the coverage and scope of the feature in the future, as well as support more languages ​​and regions.

Microsoft Bing is getting a new “Deep Search” feature powered by OpenAI's GPT-4. The feature is designed to provide users with more relevant and comprehensive answers to complex search queries. Microsoft notes that Deep Search does not replace Bing's existing web search, but rather is an enhancement that allows for deeper exploration of the web.

In a blog post, Microsoft explains that the new feature builds on Bing's current web index and ranking system and enhances it with GPT-4, which takes the search query and turns it into a more complete description of what which should include the results.

For example, let's say a user searches for loyalty programs in different countries and enters the query: "How do points systems work in Japan?" Deep Search would take the query and expand it to the following:

Provide an explanation of how the various loyalty card programs in Japan work, including the benefits, requirements, and limitations of each. Include examples of popular loyalty cards from different categories, such as convenience stores, supermarkets, and restaurants. Show a comparison of the advantages and disadvantages of using loyalty cards versus other payment methods in Japan, including current rewards and benefits. Highlight the most popular services and participating merchants.

With this expanded description, you will be able to explain your intent better than just a few words.

In cases where your search query is more ambiguous, Deep Search will find all possible intents and create a complete description for each of them. Deep Search then shows you these intents, allowing you to select the right one.

Below is an example of how deep search works in practice:

How do points systems work in Japan?

Deep Search Intent 1: Provide an overview of how points systems work in Japan, including the history, culture, and legal aspects of loyalty programs. Explain how points are earned, redeemed, and exchanged in different scenarios. Compare Japan's points systems with those of other countries, such as the US, UK and China.

Deep Dive Intent 2: Provide guidance on how to use points systems in Japan, including tips on how to register, manage, and maximize your points. Recommend the best loyalty cards for different purposes such as travel, shopping, dining, and entertainment. Provide links to helpful resources and websites to find deals and discounts with points.

Deep Dive Intent 3: Provide a review of how points systems work in Japan, including the pros and cons of loyalty programs. Please share your personal experience and opinion on the use of points in Japan. Discuss the challenges and opportunities of points systems in Japan, such as fraud prevention, customer satisfaction, and innovation.

You can choose one of these intents or enter a different query if none of them match your needs.

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Bing has introduced a new feature called Deep Search that allows users to get more detailed and relevant results for complex queries. Deep search works by creating an expanded description of the user's query based on their intent and context. For example, if a user searches for “how to use loyalty points in Japan,” Deep Search will generate a description that includes information such as the types of loyalty programs available in Japan, the best ones for travelers, how to redeem them, and how to manage them. with phone apps.

Deep Search then uses this description to find web pages that may not contain the exact keywords of the query, but are still relevant and informative. Deep Search also rewrites the query in different ways to cover different aspects of the topic. For example, Deep Search can also search for “loyalty card programs in Japan,” “best loyalty cards for travelers in Japan,” “comparison of loyalty programs by category in Japan,” “redeem loyalty cards in Japan.” » and «manage loyalty points with phone apps».

Deep Search then ranks web pages based on how well they match the extended description. Deep search considers factors such as topic relevance, level of detail, reliability, freshness, and popularity of the page. In this way, Deep Search can provide users with more complete and specific answers than normal Bing search.

Deep search is an optional feature that may take up to 30 seconds to complete. It is not designed for all queries or users, but for those who want to explore a topic in depth and learn more. Users who prefer faster and simpler results can still use regular Bing search without Deep Search.