Investing an additional 3 billion yuan, Dianping aims to uphold "authenticity" amidst AI hallucinations.
On November 5th, Dianping officially announced a major plan: it will invest at least 3 billion yuan in the next five years to upgrade the "local life information infrastructure".
According to official disclosures, this funding will be mainly directed towards two key areas: first, implementing information mining and verification capabilities in more regions globally; second, building a more efficient information calibration mechanism in combination with Meituan's self - developed B - end large - scale model.
In this consumption era where content recommendation, live - streaming e - commerce, and traffic algorithms are popular, why is Dianping investing such a huge amount of resources in the seemingly basic and dull field of "information infrastructure"?
The answer is just two words - authenticity.
"Accurate and timely information in the physical world is the foundation for users' decision - making," emphasized a relevant person in charge of Dianping.
If we restore the local life scenario, the starting point for consumers' online decision - making is always basic information such as "whether the business is open", "whether the address is accurate", and "if there is a parking lot". Once the information is false or outdated, even with numerous positive reviews and eye - catching lists, users may still have a bad experience, leading to a collapse of trust.
Especially in the current context where large - scale AI models are becoming increasingly popular, users' demand for obtaining local life decision - making references through AI is growing. The accuracy of AI output results completely depends on the "authenticity" of the underlying information database. Otherwise, users will live in an "AI illusion".
Dianping's investment plan reveals a long - underestimated fact: One of the core values of local life platforms is to serve as a "digital yellow pages of the physical world" and act as the infrastructure connecting digital needs with offline entities.
Today, Dianping has quietly evolved into a "local life cyber yellow pages" that connects digital needs with the physical world. Its core competitiveness lies in its ability to accurately capture, efficiently verify, and dynamically present information on millions of offline commercial locations.
Now, the additional 3 billion yuan investment in "information infrastructure" is not just a simple upgrade but a long - term bet on the word "authenticity". In the future, the platform that can more accurately and timely reflect the physical world will have a deeper and wider moat.
01
Behind the Accurate and Timely "Basic Information" Lies a Complex System Project
On a Saturday evening, Chen Yue, who lives in Xihu District, Hangzhou, had just finished cleaning when she received a message from her friend: "It's been a long time since we last had hot pot. How about having one tonight? You choose the place." Immediately, Chen Yue skillfully opened Dianping and typed "Chongqing hot pot" in the search box.
Among the search results, the first things she cared about were basic information such as the closing time of each business, the distance from her location, the waiting time during the Saturday evening peak, and the availability of parking nearby.
After confirming the basic information, she selected a hot pot restaurant, browsed the real user reviews, checked the list information, and then completed a series of actions such as taking a queue number online, choosing a dish package, and setting the navigation.
Source: Dianping
Chen Yue's actions are a real microcosm of countless users using Dianping. After searching for a business, the primary concern is the practical question of "whether it's possible to go". Understanding basic information such as the business status, distance, opening hours, and supporting facilities is an absolute prerequisite. After all, no one wants to arrive at a closed business, which would only ruin the dining mood.
Then, users browse auxiliary information such as business ratings, consumer reviews, and real - shot pictures to make consumption decisions and judge whether a restaurant is worth visiting.
Finally, users refer to lists, recommendations, and content guides as a basis for trust to decide which restaurant is more worthy of a visit.
For many users, the above decision - making process has almost become a natural muscle memory. However, few people realize that behind each "simple piece of information" that can be retrieved lies a complex operation of the platform.
In fact, for most users, Dianping is first and foremost a real - time and dynamically updated "digital yellow pages of the physical world". It can reflect the latest operating status of businesses in real - time and more accurately recommend nearby options based on the user's location.
Especially in the current situation where large - scale AI models are gradually infiltrating into life decision - making, users' demand for obtaining local life decision - making references through AI is increasing. For example, many users are accustomed to asking AI to recommend restaurants. Whether the AI's recommendations are reliable completely depends on the "authenticity" of the underlying information database.
If the basic information is incorrect, even the most intelligent AI recommendations will deviate from reality. Therefore, Dianping also undertakes a more important role, which is to provide an "information base" for AI and serve as the information infrastructure for AI large - scale models to output decision - making information to users.
This time, in addition to implementing information verification capabilities in global regions, the additional 3 billion yuan invested by Dianping is also mainly directed towards the research and development of the B - end large - scale model. By building an information calibration mechanism, AI can more accurately capture and analyze business information, enabling global users to obtain reliable local life information whether through traditional searches or AI recommendations.
Therefore, what seem to be "simple pieces of information" to consumers are actually the result of an extremely complex systematic project.
For example, the opening hours and contact numbers of restaurants may seem static, but in the real business environment, they are actually changing dynamically at any time. A popular restaurant may stop taking queue numbers due to a large number of waiting customers. A small restaurant may temporarily close due to equipment damage. The parking lot of a mall may also temporarily close on weekends due to saturated traffic.
The distortion of each piece of information may directly lead to a "collapse" of the user experience. To ensure the "authentic" foundation of local life, Dianping has invested an unimaginable amount of technology, manpower, and funds behind the scenes, conducting daily mining, verification, and updates, and finally building a solid information infrastructure.
This painstaking effort on "authenticity" over the past twenty years is the key for Dianping to make users "feel confident when searching".
02
The Painstaking Effort in Information Infrastructure: From "Existence" to "Accuracy"
Imagine that on a weekend morning, you have planned to visit a long - awaited restaurant. Before setting off, you specifically checked the business information, and the platform showed "open". However, after driving for half an hour, you found a notice on the door saying "closed today".
This fruitless trip will make you have doubts the next time you use the platform to check information. Will the information provided this time be accurate?
Consumers may not remember the platform providing accurate information a hundred times, but a single information error with a bad experience is enough to reduce consumers' trust in the platform.
The accuracy, timeliness, and completeness of business information, seemingly basic functions provided by the platform, are actually the "lifeline" for user retention.
To safeguard this "lifeline", the first challenge to overcome is the difficulty of information collection from scratch, and building a basic data platform covering tens of millions of businesses.
To this end, Dianping has built an information collection system like capillaries, using multiple channels simultaneously to ensure the breadth of information.
First, Dianping relies on information submission channels such as businesses, users, delivery riders, and salespeople to obtain basic data such as business qualifications, addresses, and opening hours from merchants.
At the same time, the delivery point information of delivery riders helps the platform to make the coordinates more accurate. For example, some small shops are hidden in alleys and are difficult to find. The precise positioning of riders when picking up orders can correct navigation errors, making it easier for users to find the store location.
Second, business data generated within the ecosystem, such as consumers' in - store coupon verification and the completion of takeaway orders, are also valuable behavioral clues for judging the business status of merchants and verifying the authenticity of information. For example, if a restaurant has no records of consumers verifying coupons in - store for a week, the system can mark it as "suspected to be closed", and finally, field sales personnel will conduct on - site verification.
In addition, Dianping also uses a self - developed B - end large - scale model to mine key information such as parking lot information, reservation information, private room information, environmental style, and smoking policies from a large amount of UGC content such as user reviews and notes, supplementing the above - mentioned information collection channels.
However, the mere existence of information is far from enough. Facing a vast, complex, and even conflicting information source, the greater challenge is how to ensure the "accuracy" of the final presented results.
Dianping's approach is to establish a strict "AI + human" dual verification mechanism. The platform has invested heavily in self - developing an AI large - scale model, which includes non - real picture recognition models, similar dish recognition models, and picture - text consistency recognition models. These models conduct initial screening, deduplication, and preliminary authenticity identification of the influx of massive information, undertaking the first - round cleaning of large - scale data.
For complex information that AI cannot determine, it will be transferred to the manual review team for manual review and sampling inspection. Taking the "business operating status" as an example, if the system identifies that users report the business as closed while the business is marked as open, the reviewer will first call the merchant. If the call cannot be connected, the regional field sales personnel will be coordinated for on - site confirmation.
Behind this is the platform's data mining, cross - verification, and intelligent decision - making capabilities accumulated over more than a decade, as well as a well - trained operating team that understands the complexity of offline business.
The accuracy of static information is just the starting point. The real - time maintenance of dynamic data is the key to combating "outdated information". The operating status of offline businesses is constantly changing. For example, dynamic information such as temporary store closures, renovation closures, private room availability, and parking lot maintenance, if not updated in time, users may retrieve incorrect information.
This requires local life platforms to mobilize all data resources within the ecosystem for a long time, establish a perfect offline information submission and response system, and use technological means and a mature review process to achieve the ultimate pursuit of information accuracy, coverage, and timeliness.
From information collection, verification to dynamic maintenance, Dianping's information infrastructure is a large - scale and highly complex system project. It is not a static information database that can be built at once but a "dynamic project" that requires continuous investment.
It is reported that since its establishment, Dianping has invested a total of tens of billions of yuan. It has deeply explored the online transformation of physical world information in China, providing an industry model for the "information infrastructure" in the local life field and will also accelerate its expansion overseas in this field in the future.
Ultimately, a solid information infrastructure is not built overnight but requires long - term and continuous investment in funds, manpower, and technology. The "information infrastructure" built by Dianping for the sake of "authenticity" has been gradually formed through such daily "painstaking efforts".
03
The Invisible Barrier: Why "Information Infrastructure" Is an "Extremely Difficult to Replicate" Moat
In the local life market, traffic, subsidies, and lists have become "conventional weapons" for many platforms to compete for users. However, in fact, a real and accurate information infrastructure is one of the core barriers for a platform to stand firm in the long run.
In terms of data precipitation, through twenty years of operation, Dianping has accumulated a vast business information database covering thousands of cities globally, tens of millions of merchants, and including billions of pictures and reviews.
This information database not only records the static information of merchants but also includes dynamic information with a time dimension, such as changes in opening hours, menu updates, and store relocations. This large - scale and in - depth accumulation of real information, built through long - term investment, is the platform's long - established moat.
It should be noted that the real, accurate, and timely "information infrastructure" is the foundation for local life platforms to form a flywheel effect.
The vast amount of new data generated by the platform every day and the high - frequency interaction with users also provide continuous training materials for AI large - scale models. The more data there is and the more frequent the interaction between users and merchants, the stronger the recognition and mining capabilities of the large - scale model will be, thus forming a flywheel effect of "data - model - accuracy".
Taking Dianping's self - developed non - real picture recognition model as an example, only through the comparative learning of hundreds of millions of real and fake merchant pictures can it accurately distinguish which are real on - site photos of merchants and which are over - retouched fake pictures.
What is even more difficult to replicate is the user mindset and trust cultivated by long - term and stable information accuracy. When users consistently find accurate business opening hours, addresses, and other information on Dianping, they will form a corresponding cognitive dependence.
This user habit based on trust stems from the stable fulfillment experience brought by the platform's long - term and continuous provision of accurate information. It is a profound brand asset that needs to be built over time. Behind this is an extremely large and continuous investment in manpower, time, and technology by the platform, along with a long and cumbersome on - the - ground promotion and data verification process. Moreover, this investment hardly yields direct economic returns in the short term.
It is reported that Dianping spends millions of yuan each year on recognition, review, and operation costs for AI intelligent review technology alone, and the monthly cost of machine review is also substantial.
This is a typical heavy - asset and heavy - model approach, testing the strategic patience of long - termism.
A solid information infrastructure is the cornerstone of all superstructures. All value - added services such as lists and rich review content are built on a solid information foundation.
Dianping's content products such as lists and reviews are also built on the basis of ensuring the authenticity of information as much as possible. This logic of first ensuring the authenticity of basic information and then adding value to local life decision - making content provides a reliable foundation for upper - layer products.
In the future, Dianping will continue to widen the moat of its information infrastructure. One of the key focuses of the additional 3 billion yuan investment is to promote the research and development of the B - end large - scale model and accelerate the global layout.
Only by having the ability to accurately digitize the physical world can the platform provide more intelligent business analysis tools for merchants and successfully replicate this mature local life information infrastructure model to overseas markets, realizing the vision of connecting digital needs with the physical world globally.
This daily, seemingly clumsy effort to ensure the "authenticity" of each piece of information is the core and lasting competitiveness of Dianping.