Microsoft Technology Licensing, LLC

Snap Inc.

Satya Kanduri

Shakti Dhirendraji Sinha

Abhishek Gupta

Xianren Wu

Viet Thuc Ha

Ye Xu

Yan Yan

Vijay K Dialani

Nathan Kenneth Boyd

Dhruv Arya

Sivakumar Loganathan

Abhimanyu Lad

Maryam Daneshi

Senthil Karthikeyan Sundaram

Evan Shaw

Xiaochuan Ni

Shan Zhou

Rahim Daya

Zian Yu

Jordan Anthony Saints

Timothy Patrick Jordt

Gregory Alan Walloch

Zachary Tyler Piepmeyer

Aspects of the present disclosure involve a system comprising a computer-readable storage medium storing at least one program and a method for ranking videos. The program and method include storing, in a database, a plurality of videos; obtaining, for a first of the plurality of videos, first data representing a first interaction performed by a creator of the first video in association with the first video; and obtaining, for the first video, second data representing a second interaction performed by a consumer of the first video in association with the first video. A score is computed for the first video based on the first and second data and the first video is ranked relative to a remaining subset of the plurality of videos by comparing the computed score for the first video with respective scores of videos in the remaining subset of the plurality of videos.

Automated ranking of video clips

In an example embodiment, one or more specified ideal candidates are used to perform a search in a database. One or more attributes are extracted from one or more ideal candidate member profiles. A search query is then generated based on the extracted one or more attributes. Then, a search is performed on member profiles in the social networking service using the generated search query, returning one or more result member profiles.

Query building for search by ideal candidates

In an example embodiment, one or more query terms are obtained. Then, for each of the one or more query terms, a standardized entity taxonomy is searched to locate a standardized entity that most closely matches the query term, with the standardized entity taxonomy comprising an entity identification for each of a plurality of different standardized entities. A confidence score is then calculated for the query term-standardized entity pair for the standardized entity that most closely matches the query term, and the query term is tagged with the entity identification corresponding to the standardized entity that most closely matches the query term and the calculated confidence score.

Entity based search retrieval and ranking

A system and method for generating modifiers for updated search queries are provided. In example embodiments, metadata is accessed, the metadata corresponds to search results of an input query and comprising a plurality of candidate modifiers. A score is calculated for each candidate based on a relevance value that indicates the correlation between a candidate modifier and the input query. A list of top number of candidate modifiers is generated based on the score of the candidate modifier transgressing a first threshold. A uniqueness score is calculated for combination pairs of candidate modifiers within the list, the uniqueness score being used to eliminate candidate modifiers. The list of top number of candidate modifiers is presented, at a user interface, according to a ranked order based on the score.

Generating modifiers for updating search queries

In an example embodiment, one or more sample ideal candidate member profiles in a social networking service are obtained, as well as one or more sample search result member profiles in the social networking service. Then, for each unique pair of sample ideal candidate member profile and sample search result member profile, a label is generated using a score generated from log information of the social networking service, the log information including records of communications between a searcher and members of the social networking service, the score being higher if the searcher communicated with both the member corresponding sample ideal candidate member profile and the member corresponding to the sample search result member profile in a same search session. The generated labels are fed into a machine learning algorithm to train a combined ranking model used to output ranking scores for search result member profiles.

Generation of training data for ideal candidate search ranking model

System and techniques for facet expansion are described herein. A user interface element may be presented on facet selection portion of a search result display including search results. Here, the user interface element is arranged to accept user input of a facet. Partial user input for a facet may be received. A peer entity to an entity corresponding to the facet may be obtained. A peer facet may be presented in a suggestion element in the facet selection portion in response to receiving the partial user input.

System for facet expansion

In an example embodiment, a query for search results is received, the query including at least one value for one facet, a facet defining a categorical dimension for the search results. It is then determined that the facet in the query is exclusive. In response to the determination that the facet is exclusive: for each potential facet different from the facet in the query: for each potential value in the potential facet: conditional entropy gain of the value in the query and the potential value is determined. The potential value in the potential facet that has the highest conditional entropy gain is determined, as is the potential facet with the minimum maximum conditional entropy gain. Then the potential facet with the minimum maximum is input into a machine learning model, causing the machine learning model to output one or more suggested facets to add to the query.

Smart suggestions for query refinements

Growing community of inventors

Mountain View, CA, United States of America

Satya Kanduri