Microsoft Technology Licensing, LLC

Wen Pu

Kun Liu

Liqin Xu

Rohan Ramanath

Qing Duan

Keqing Liang

Giorgio Paolo Martini

Liang Zhang

Aman Grover

Shan Zhou

Benjamin Hoan Le

Nikhil Devanur Rangarajan

Jianqiang Shen

Swetha Nagabhushan Karthik

Linda Fayad

Zhifeng Deng

Austin Qingfeng Lu

Peter Poon

Sponsored and organic pieces of content are displayed in accordance with a blending model that is used to first identify a pattern of slots to which to assign either sponsored or organic pieces of content. This blending model is applied to a combination of both sponsored and non-sponsored pieces of content being considered for display to a user. This pattern only determines the slot assignments. The actual ranking of the pieces of content, and more particularly the actual ranking of the organic pieces of content, is determined by an ordering other than the ranking determined by the blending model, such as by using the original ordering of the second list. The pieces of content are then displayed in the order of this actual ranking, but in the slots indicated as having been assigned to be either sponsored or organic in the pattern determined by the blending model.

Dynamic slotting using blending model

Techniques for the dynamic slotting of content items within electronic content are provided. In response to receiving a request, a first set of content items is identified and a first score is generated for each based on a first objective. Based on the first scores, a first ranking of the first set of content items is generated. A subset of the first set is selected based on the first ranking. A second set of content items that includes the subset is identified and a second score is generated for each item in the second set based on a second objective that is different than the first objective. Based on the second scores, a second ranking of the second set is generated. A subset of the second set is selected based on the second ranking. The second subset is presented on a computing device that is associated with the request.

Dynamic slotting of content items within electronic content

Technologies for determining performance gains for content item delivery based on modifications to content item selection parameters are provided. The disclosed techniques include implementing a multi-objective optimization model for content item selection using a value for a parameter. The model generates a first plurality of scores and a first ranking for content items. Subset of content items is selected for delivery based on the first ranking. New values for the parameter are identified and for each new value, the content item selection event is replayed. A second plurality of scores and a second ranking is generated for the content items, where the second ranking is different from the first ranking. A third plurality of scores and a third ranking is generated where the third ranking matches the second ranking. A set of gains is calculated for each new value, where each gain corresponds to a different objective of the model.

Estimating performance gains for content item delivery using performance replay

Techniques for leveraging existing statistical prediction models are provided. A first statistical prediction model is generated for a content item. An instruction is received to create a clone from the content item. In response to receiving the instruction, the clone is created based on attributes of the content item. A second statistical prediction model that is different than the first statistical prediction model is generated for the clone. In response to receiving a request for content, the clone is identified as relevant to the first request. A similarity between (1) first content of the content item and (2) second content of the clone is determined. If the similarity exceeds a similarity threshold, then the first statistical prediction model is used to generate a prediction of an entity user selection rate associated with the clone. Otherwise, the second statistical prediction model is used to generate the prediction.

Realtime response to network-transferred content requests using statistical prediction models

Instead of a fixed fee for a particular job application, discussed in some examples are methods, systems, and machine readable mediums which provide for a job posting service that utilizes a pay-per-click model. That is, job posters pay a fee for each time the member selects the job posting for further inquiry when that posting is shown to a member (called an impression). The fee that is paid is determined by the job poster. Selecting a job posting may comprise clicking on or otherwise entering an input signifying an intention to view the job.

Electronic job posting marketplace

Techniques for incremental workflow execution are provided. In one technique, a computing job in a workflow identifies an input path that indicates a first location from which the computing job is to read input data. The computing job identifies an output path that indicates a second location to which the computing job is to write output data. The computing job performs a comparison between the input path and the output path. Based on the comparison, the computing job determines whether to read the input data from the first location. If the input path does not correspond to the output path, then the computing job reads the input data from the first location, generates particular output data based on the input data, and writes the particular output data to the second location. The computing job ceases to execute if the input path corresponds to the output path.

Incremental workflow execution

Techniques related to cache management for multi-node databases are disclosed. In some embodiments, a system comprises one or more computing devices including a training component, data store, cache, filtering component, and listening component. The training component produces a plurality of models based on user interaction data. The plurality of models are stored in the data store, which responds to requests from the cache when the cache experiences cache misses. The cache stores a first subset of the plurality of models. The filtering component selects a second subset of the plurality of models based on one or more criteria. Furthermore, the filtering component sends the second subset of the plurality of models to a messaging service. The listening component retrieves the second subset of the plurality of models from the messaging service. Furthermore, the listening component causes the second subset of the plurality of models to be stored in the cache.

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Santa Clara, CA, United States of America

Wen Pu