18112934. SIMILARITY-BASED LISTING RECOMMENDATIONS IN A DATA EXCHANGE simplified abstract (Snowflake Inc.)

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SIMILARITY-BASED LISTING RECOMMENDATIONS IN A DATA EXCHANGE

Organization Name

Snowflake Inc.

Inventor(s)

Orestis Kostakis of Redmond WA (US)

Prasanna V. Krishnan of Palo Alto CA (US)

Subramanian Muralidhar of Mercer Island WA (US)

Shakhina Pulatova of San Francisco CA (US)

Megan Marie Schoendorf of San Francisco CA (US)

SIMILARITY-BASED LISTING RECOMMENDATIONS IN A DATA EXCHANGE - A simplified explanation of the abstract

This abstract first appeared for US patent application 18112934 titled 'SIMILARITY-BASED LISTING RECOMMENDATIONS IN A DATA EXCHANGE

Simplified Explanation

The abstract describes a method for determining affinity metrics for a set of listings in a data exchange. Affinity metrics are characteristics that help identify listings with similar characteristics. An affinity score is calculated for each pair of listings using these metrics and stored in an affinity store. The affinity score is then used to present listings that have a high affinity with a particular listing.

  • Affinity metrics are determined for a set of listings in a data exchange.
  • Affinity metrics help identify listings with similar characteristics.
  • An affinity score is calculated for each pair of listings using the affinity metrics.
  • The affinity score is stored in an affinity store.
  • Listings with high affinity scores are presented based on their similarity to a particular listing.

Potential Applications

  • This technology can be applied in online marketplaces to recommend similar listings to users based on their preferences.
  • It can be used in social networking platforms to suggest connections or friends with similar interests.
  • The method can be utilized in content recommendation systems to provide personalized suggestions based on user preferences.

Problems Solved

  • The technology solves the problem of efficiently identifying and presenting listings with similar characteristics in a data exchange.
  • It addresses the challenge of providing personalized recommendations to users based on their preferences.
  • The method solves the issue of manually searching for similar listings by automating the process using affinity metrics and scores.

Benefits

  • Users can easily discover and explore listings that match their preferences without extensive searching.
  • The technology improves user experience by providing personalized recommendations.
  • It saves time and effort for users by automating the process of finding similar listings.
  • The method enhances the efficiency of data exchanges by facilitating the identification and presentation of relevant listings.


Original Abstract Submitted

A set of affinity metrics may be determined for a set of listings, each listing of the set of listings comprising data to be shared through a data exchange, wherein the set of affinity metrics includes a set of characteristics allowing identification of a listing having one or more characteristics in the set of characteristics. For each pair of listings of the set of listings, an affinity score can be calculated, using the set of affinity metrics, and stored as part of the record in an affinity store. One or more listings of the set of listings using the affinity score between the first listing of the set of listings and the one or more listings of the set of listings can be presented.

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