FIELD OF THE INVENTION

The present technology relates to the field of networked communications. More particularly, the present technology relates to techniques for improving the accuracy of content presented to users in a computerized networking system.

BACKGROUND

Today, people often utilize computing devices (or systems) for a wide variety of purposes. For example, users can use their computing devices to interact with other users, create content, share content, and view content. In some cases, users can utilize their computing devices to access a social network and post content to the social network. Content posted to the social network may include text content items and media content items, such as audio, images, and videos. The posted content may be published to the social network for consumption by others.

SUMMARY

Various embodiments of the present technology can include systems, methods, and non-transitory computer readable media configured to determine a set of candidate values for a field in a page. The set of candidate values can be evaluated for accuracy based at least in part on a machine learning model, wherein the machine learning model outputs a respective score for each candidate value that measures an accuracy of the candidate value for the field in the page. A best scoring candidate value can be determined from the set of candidate values. The field in the page can be associated with the best scoring candidate value.

In an embodiment, the field corresponds to at least one of: a page category field, a website field, a phone number field, an hours of operation field, and a physical address field.

In an embodiment, the systems, methods, and non-transitory computer readable media are further configured to cause the field in the page to be populated with the best scoring candidate value.

In an embodiment, the systems, methods, and non-transitory computer readable media are further configured to provide the best scoring candidate value as a recommendation for populating the field in the page.

In an embodiment, evaluating the set of candidate values for accuracy based at least in part on the machine learning model further causes the systems, methods, and non-transitory computer readable media to determine a feature vector representing a candidate value, provide the feature vector to the machine learning model as an input, and obtain a score for the candidate value from the machine learning model, wherein the score measures an accuracy of the candidate value for the field in the page.

In an embodiment, the feature vector includes a feature representing a set of weighted user endorsements for the candidate value, wherein a user endorsement is weighted based on a credibility score associated with the user, the credibility score measuring a credibility of the user.

In an embodiment, wherein the feature representing the set of weighted user endorsements for the candidate value is determined based at least in part on an activation function or a logit function.

In an embodiment, wherein the feature vector includes a feature representing a set of weighted data pipeline endorsements for the candidate value, wherein a data pipeline endorsement is weighted based on a consensus score associated with the data pipeline, the consensus score measuring a rate at which the data pipeline satisfies a user consensus.

In an embodiment, the feature representing the set of weighted data pipeline endorsements is determined based at least in part on an activation function or a logit function.

In an embodiment, the systems, methods, and non-transitory computer readable media are further configured to cause one or more users to be polled to confirm or improve an accuracy of the best scoring candidate value.

It should be appreciated that many other features, applications, embodiments, and/or variations of the disclosed technology will be apparent from the accompanying drawings and from the following detailed description. Additional and/or alternative implementations of the structures, systems, non-transitory computer readable media, and methods described herein can be employed without departing from the principles of the disclosed technology.

