Microsoft Technology Licensing, LLC

Taifeng Wang

Tie-Yan Liu

Bin Gao

Zhi Chen

Minghao Liu

Xiaodong Fan

Techniques for determining user information needs and selecting data based on user information needs are described herein. The present disclosure describes extracting topics of interests to users from multiple sources including search log data and social network website, and assigns a budget to each topic to stipulate the quota of data to be selected for each topic. The present disclosure also describes calculating similarities between gathered data and the topics, and selecting top related data with each topic subject to limit of the budget. A search engine may use the techniques described here to select data for its index.

User information needs based data selection

Some implementations provide techniques for selecting web pages for inclusion in an index. For example, some implementations apply regularization to select a subset of the crawled web pages for indexing based on link relationships between the crawled web pages, features extracted from the crawled web pages, and user behavior information determined for at least some of the crawled web pages. Further, in some implementations, the user behavior information may be used to sort a training set of crawled web pages into a plurality of labeled groups. The labeled groups may be represented in a directed graph that indicates relative priorities for being selected for indexing.

Page selection for indexing

Efficient search query clustering using tripartite graphs may enable a search engine developer to model information needs of users while expending less computing resources. The efficient clustering of search queries may involve multiple computing devices receiving a subgraph of a multi-partite graph that encompasses search queries, as well as receiving a global center vector table that includes cluster center entries for query clusters. At each computing device, the received global center vector table may be filtered to eliminate one or more cluster center entries that are irrelevant to the search queries. Subsequently, the search queries may be clustered into the query clusters by at least using the filtered global center vector table at each of the computing devices. In some instances, one or more comparisons between search queries and the cluster center entries in the global center vector table during the clustering may be eliminated.

Efficient query clustering using multi-partite graphs

Embodiments for caching and accessing Directed Acyclic Graph (DAG) data to and from a computing device of a DAG distributed execution engine during the processing of an iterative algorithm. In accordance with one embodiment, a method includes processing a first subgraph of the plurality of subgraphs from the distributed storage system in the computing device. The first subgraph being processed with associated input values in the computing device to generate first output values in an iteration. The method further includes storing a second subgraph in a cache of the device. The second subgraph being a duplicate of the first subgraph. Moreover, the method also includes processing the second subgraph with the first output values to generate second output values if the device is to process the first subgraph in each of one or more subsequent iterations.

Data caching for distributed execution computing

Systems, methods, and devices for sorting and processing various types of graph data are described herein. Partitioning graph data into master data and associated slave data allows for sorting of the graph data by sorting the master data. In another embodiment, promoting a data bucket having a first data bucket size to a data bucket having a second data bucket size greater than the first data bucket size upon reaching a memory limit allows for the reduction of temporary files output by the data bucket.

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Beijing, China

Taifeng Wang