Abstract: System and methods for generating a shareability score in accordance with some example embodiments are disclosed. A social networking system receives a request to generate a shareability score for a list of content items for an organization. The social networking system identifies a plurality of members associated with the organization and analyzes past share data for the plurality of members to generate an organization sharing profile. The social networking system retrieves early sharing information for each content item in the list of content items. The social networking system generates a shareability score for each particular content item and ranks the list of content items based on the generated shareability scores. The social networking system then transmits the ranked list of content items to a client device, receives a selection of one or more content items, and broadcasts the one or more selected items to a plurality of client devices.

Abstract: Techniques for identifying and presenting member profiles similar to a source member profile are described. With some embodiments, a general recommendation engine is used to extract features from member profiles, and then store the extracted features, including any computed, derived or retrieved profile features, in an enhanced member profile. In real-time, the general recommendation engine processes client requests to identify member profiles similar to a source member profile by comparing select profile features stored in the enhanced member profile with corresponding profile features of the source member profile, where the comparison results in several similarity sub-scores that are then combined in accordance with directives set forth in a configuration file. Finally, the member profiles with the highest similarity scores corresponding with the user-selected member profile are selected, and in some instances, presented to a user.

Abstract: 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.

Abstract: 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.

Abstract: In an example embodiment, one or more ideal candidate member profiles in a social networking service are obtained. Then a search is performed on member profiles in the social networking service using a search query, returning one or more result member profiles. One or more query-based features are produced from the one or more result member profiles using the search query. One or more ideal candidate-based features are produced from the one or more result member profiles using the one or more ideal candidate member profiles. The one or more query-based features and the one or more ideal candidate-based features are input to a combined ranking model trained by a machine learning algorithm to output a ranking score for each of the one or more result member profiles. The one or more result member profiles are then ranked based on the ranking scores.

Abstract: In an example embodiment, as time goes on and as refinements are received to an online search, weights assigned to each of the one or more query-based features are dynamically trained to increase as more refinements are received and weights assigned to each of the one or more ideal candidate-based features to decrease as more refinements are received.

Abstract: 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.

Abstract: Techniques for identifying and presenting member profiles similar to a source member profile are described. With some embodiments, a general recommendation engine is used to extract features from member profiles, and then store the extracted features, including any computed, derived or retrieved profile features, in an enhanced member profile. In real-time, the general recommendation engine processes client requests to identify member profiles similar to a source member profile by comparing select profile features stored in the enhanced member profile with corresponding profile features of the source member profile, where the comparison results in several similarity sub-scores that are then combined in accordance with directives set forth in a configuration file. Finally, the member profiles with the highest similarity scores corresponding with the user-selected member profile are selected, and in some instances, presented to a user.

Abstract: Techniques for searching for future candidates are disclosed herein. In some example embodiments, a future candidate system determines one or more target candidate attributes based on user input, and identifies one or more precedent candidate attributes based on a sequential relationship between the one or more precedent candidate attributes and the one or more target candidate attributes, with the one or more precedent candidate attributes being different from and preceding the one or more target candidate attributes. In some example embodiments, the future candidate system identifies one or more candidates from among a plurality of candidates based on a determination that the one or more candidates comprise the one or more precedent candidate attributes, and causes the identified one or more candidates to be displayed on a client device.

Abstract: The subject disclosure is directed towards using primary data deduplication concepts for more efficient access of data via content addressable caches. Chunks of data, such as deduplicated data chunks, are maintained in a fast access client-side cache, such as containing chunks based upon access patterns. The chunked content is content addressable via a hash or other unique identifier of that content in the system. When a chunk is needed, the client-side cache (or caches) is checked for the chunk before going to a file server for the chunk. The file server may likewise maintain content addressable (chunk) caches. Also described are cache maintenance, management and organization, including pre-populating caches with chunks, as well as using RAM and/or solid-state storage device caches.

Abstract: A first user of a presentation machine may be a recruiter that initiates an action in reference to a first user profile. The first user profile may describe a first candidate for a job. The presentation machine may identify the first user profile and determine a similarity score that indicates a degree of similarity between the first user profile and a second user profile that describes a second candidate for the job. The presentation machine may also access a volatility score that indicates a likelihood that the second candidate is receptive to a proposal that the second candidate be employed by an employer. Based on the similarity score and on the volatility score, the presentation machine may determine a rank of the second user profile. Based on the determined rank, the presentation machine may present the second user profile to the first user.

Abstract: The present invention relates to textile fabrics and methods of manufacturing textile fabrics. Particularly, the invention comprises a method of producing a fabric, comprising the steps of (i) blending chemo mechanically felting fibers with non-felting fibers into a blended feed material, (ii) spinning the blended feed material into a blended yarn, (iii) producing a fabric comprising the blended yarn, (iv) subjecting the fabric to a first fabric treatment comprising a mechanical felting treatment; and (v) subjecting the fabric to a second fabric treatment comprising a chemical treatment of the fabric with an alkali, wherein the ratio of weight of the alkali to dry fabric weight is between 0.02 and 0.05, thereby obtaining increased air space in the resultant fabric.

Abstract: Techniques for identifying and presenting member profiles similar to a source member profile are described. With some embodiments, a general recommendation engine is used to extract features from member profiles, and then store the extracted features, including any computed, derived or retrieved profile features, in an enhanced member profile. In real-time, the general recommendation engine processes client requests to identify member profiles similar to a source member profile by comparing select profile features stored in the enhanced member profile with corresponding profile features of the source member profile, where the comparison results in several similarity sub-scores that are then combined in accordance with directives set forth in a configuration file. Finally, the member profiles with the highest similarity scores corresponding with the user-selected member profile are selected, and in some instances, presented to a user.

Abstract: An article of dynamic jewellery includes a protective housing having an interior and a viewing window through which the interior is visible. A pendulum is suspended within the interior of the protective housing. The lower end of the pendulum supports an ornament which gives the pendulum sufficient weight that the pendulum swings freely in response to movements of a wearer's body.

Abstract: System and techniques for autonomously adaptive performance monitoring are described herein. A text input field may be presented on a graphical user interface. A flow-selector may be presented in contact with the text input field in response to receiving user input at the text input field. Here, the flow-selector includes a set of flow choices selected based on the user input. A user selection of a flow choice may be received. Next-steps flow elements may be presented in response to the user selection. User query choices may be collected from the next-steps flow elements to populate a query template corresponding to the flow choice. The query template is executed to produce search results.

Abstract: 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.

Abstract: The invention provides a novel pile fabric and a method for manufacturing the novel pile fabric. The pile fabric comprises: (i) a woven ground fabric comprising a plurality of warp yarns and a plurality of weft yarns intersecting the plurality of warp yarns, (ii) one or more pile yarns woven to form a plurality of pile loops extending from the ground fabric, wherein (a) at least one pile yarn from among the one or more pile yarns is a multi-ply pile yarn comprising a first pile component yarn plied with a second pile component yarn, and (b) a turns-per-loop ratio of the multi-ply pile yarn is less than 1.9 turns-per-loop.

Abstract: A system and method for exporting a training model for performing a prediction including sending for presentation, to a user, a directed acyclic graph representing a model trained using a transformed source dataset and one or more transformations used to obtain the transformed source dataset; determining the model as an endpoint based on a user selection; determining a transformation as a start point; determining whether one or more intervening transformations exist on a path going from the start point and leading to the endpoint in the directed acyclic graph; and exporting the model and relevant transformations, the relevant transformations including the transformation at the start point and any intervening transformations on the path going from the start point and leading to the model at endpoint in the directed acyclic graph to a production environment.

Abstract: A system and method for facilitating interoperability of data transformations developed in different programming platforms under a unified platform including receiving a first transformation utilizing a first programming platform; receiving information about the first transformation; wrapping the first transformation; including the wrapped, first transformation in a transformation pipeline, the transformation pipeline including a second transformation that is wrapped, the second transformation utilizing a second programming platform different from the first programming platform; and executing the transformation pipeline including the wrapped, first transformation and the wrapped, second transformation in batch mode or real-time streaming mode.

Abstract: A first user of a presentation machine may be a recruiter that initiates an action in reference to a first user profile. The first user profile may describe a first candidate for a job. The presentation machine may identify the first user profile and determine a similarity score that indicates a degree of similarity between the first user profile and a second user profile that describes a second candidate for the job. The presentation machine may also access a volatility score that indicates a likelihood that the second candidate is receptive to a proposal that the second candidate be employed by an employer. Based on the similarity score and on the volatility score, the presentation machine may determine a rank of the second user profile. Based on the determined rank, the presentation machine may present the second user profile to the first user.