公开(公告)号：US20220245160A1

公开(公告)日：2022-08-04

申请号：US17162859

申请日：2021-01-29

Applicant: salesforce.com, inc.

Inventor： Mohamed Abdelrahman Zahran Mohamed ,  Ashish Bharadwaj Srinivasa ,  Mario Sergio Rodriguez ,  Christian Posse

IPC: G06F16/2457 ,  G06F16/9538 ,  G06F16/93

Abstract: In accordance with embodiments, there are provided mechanisms and methods for facilitating relevance prediction-based ranking and presentation of documents for intelligent searching in cloud computing environments in database systems according to one embodiment. In one embodiment and by way of example, a method includes receiving a query, predicting relevance of documents associated with the query based on content of the query and historical user expectations, where the relevance is predicted based on comparison of a first relevance prediction with a second relevance prediction. The method may further include ranking the documents based on the predicted relevance, where the documents are sorted based on the ranking, and communicating, in response to the query, the ranked and sorted documents to a computing device over a communication network.

公开(公告)号：US20220229843A1

公开(公告)日：2022-07-21

申请号：US17154378

申请日：2021-01-21

Applicant: salesforce.com, inc.

Inventor： Ashish Bharadwaj Srinivasa ,  Jacob Alexander Mannix ,  Mario Sergio Rodriguez

IPC: G06F16/2457 ,  G06F16/28

Abstract: Methods, computer readable media, and devices for modeling heterogeneous feature sets for use in personalized search are provided. The method may include generating a similarity factor for each of a plurality of personalization features. For each of the plurality of personalization features, a personalization feature weight is calculated. Each personalization feature weight is converted into a probability distribution and each similarity factor is scaled based on a corresponding probability distribution. Based on each scaled similarity factor, a most recently used affinity value is generated for each corresponding personalization feature. The most recently used affinity values are used to generate a ranking function for use as part of personalized search.