Similarity-based listing recommendations in a data exchange
Affinity-based listing recommendations are created and used in a public data exchange. Listings can be evaluated against one another for affinity or similarity such that users working with a particular dataset can be presented with other datasets that share an affinity. Affinity can be determined fr...
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| creator | Muralidhar, Subramanian Schoendorf, Megan Marie Kostakis, Orestis Krishnan, Prasanna V Pulatova, Shakhina |
| description | Affinity-based listing recommendations are created and used in a public data exchange. Listings can be evaluated against one another for affinity or similarity such that users working with a particular dataset can be presented with other datasets that share an affinity. Affinity can be determined from both the dataset metadata as well as information from the dataset content. Calculation of affinity scores can be pre-computed and stored, in advance of use, or determined on-the-fly. Presentation of most-similar listings can be deterministic, can contain randomization, can employ time-decay, can be weighted, and can make use of a tiered-sum approach. |
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| subjects | CALCULATING COMPUTING COUNTING ELECTRIC DIGITAL DATA PROCESSING PHYSICS |
| title | Similarity-based listing recommendations in a data exchange |
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