Ebay, Incorporated

International Business Machines Corporation

Microsoft Technology Licensing, LLC

Michael Ching

David Goldberg

Shubha Umesh Nabar

Andrew Stephen Tomkins

Rahul Nair

Simon P King

Brian Scott Johnson

Sandy Wong

Julie Lavee Netzloff

Vamsi Salaka

Daniel Norin Meredith

Davide Libenzi

Srinivasan Balasubramanian

Stephen Dill

Jun Yin

Xavier Legros

David L Blackman

Kevin L Haas

Piyoosh Jalan

Satish Chandra Penmetsa

Ivan Eduardo Gonzalez

Adam Marcus

Linda Anh Linh Nguyen

Miroslav Melichar

Wei Mu

Srinivasan Badrinarayanan

Xiaoyuan Wu

Yu Liu

Peter Bailey

Scott Joseph Counts

Nikhil Dandekar

Chad Carson

Lei Duan

Joseph Anthony Beynon

Alpha Kamchiu Luk

Charles C Carson, Jr

Rajesh Shenoy

Ho John Lee

Yuanjie Liu

Satish Kumar Musukula

Hyun-Ju Seo

Paul Alexander Dow

Shervin Moghaddam

Vinayak Agarwal

Shuang Guo

Yajie Siamwalla

Ben Chisell

Johannes Braun

Tom Pugh

Tom Wagstaff

Gajanan Adalinge

Aditya Pal

Song Feng

A system and method of identifying suspicious item-related features are disclosed. In some embodiments, a new item listing is received. The item listing may correspond to a request to list an item for sale on an e-commerce website. Item-related data for the item listing may be extracted. The item-related data can be used by a model to classify the new item listing. The model may be trained on data comprising identifications of which item listings in the sample of item listings are suspicious and identifications of which item listings in the sample of item listings are not suspicious may be received.

Suspicion classifier for website activity

Disclosed is a system comprising a computer-readable storage medium storing at least one program, and a computer-implemented method for generating search results. A data access module accesses search results data indicative of a plurality items and that is indicative of ranking values of the plurality of items. A controller module generates page data based on the search results data. The controller module selects a first item from the plurality of items. A diversification module accesses a first rule of the page data. The diversification module determines a deviance value and a reordering-cost value of a second item from the plurality of items. The controller module can select, based at least on a first combination of the deviance and the reordering-cost values of the second item, the second item for placement ahead of the first item on the page data.

Diversifying search results

A system and method of identifying suspicious item-related features are disclosed. In some embodiments, a sample of item listings is received. Each item listing in the sample of item listings may correspond to an item offered for sale on an e-commerce website. Item-related data for each item listing in the sample of item listings may be extracted. Training data comprising identifications of which item listings in the sample of item listings are suspicious and identifications of which item listings in the sample of item listings are not suspicious may be received. A model for classifying a candidate item listing on the e-commerce website as being suspicious may be determined using the training data.

Disclosed are a system comprising a computer-readable storage medium storing at least one program, and a computer-implemented method for generating search results. A data access module accesses search results data indicative of a plurality items and that is indicative of ranking values of the plurality of items. A controller module generates page data based on the search results data. The controller module selects a first item from the plurality of items. A diversification module accesses a first rule of the page data. The diversification module determines a deviance value and a reordering-cost value of a second item from the plurality of items. The controller module can select, based at least on a first combination of the deviance and the reordering-cost values of the second item, the second item for placement ahead of the first item on the page data.

Techniques for determining search results based on session based refinements are presented herein. A method is disclosed that includes receiving a query in a user session, the query comprising one or more search parameters, detecting, in the user session and after receiving the query, a user event associated with a property of an item, updating a record in a table that associates the query with the property, the table comprising a plurality of records that associate the query with respective item properties, the record comprising the query, the property, and a score, and ranking search results for a subsequent query based on the associated properties indicated in the plurality of records, the subsequent query including the one or more search parameters.

Determining search results using session based refinements

Search results may include both objective results and person results. In one example, a search query is evaluated to determine whether it is the type of query that a user might want to ask to a friend. If the query is of such a type, then the search engine may examine a social graph to determine which friends of the user who entered the query may have information that is relevant to answering the query. If such friends exist, then the friends may be displayed along with objective search results, along with an explanation of each friend's relevance to the query. Clicking on a person in the results may cause a conversation to be initiated with that person, thereby allowing the user who entered the query to ask his or her friend about the subject of the query.

Providing objective and people results for search

Systems and methods to predict bidding behavior are described. The system identifies a listing that includes listing information that describes an item that is being auctioned on a network-based marketplace. The system further identifies bid classification information based on the number of bids received for the item. Finally, the system predicts whether no more bids are expected to be received for the item based on the classification information.

Systems and methods to predict bidding behaviour

The author ranking technique described herein is a technique to rank authors in social media systems along various dimensions, using a variety of statistical methods for utilizing those dimensions. More particularly, the technique ranks authors in social media systems through a combination of statistical techniques that leverage usage metrics, and social and topical graph characteristics. In various exemplary embodiments, the technique can rank author authority by the following: 1) temporal analysis of link sharing in which authority is computed based on a user's propensity to provide early links to web pages that subsequently become popular; 2) topical authority based on the author's links and content updates in specific topic areas; and 3) popularity and influence based on nodal properties of authors.

Ranking authors in social media systems

Systems, methods, and computer-readable storage media for presenting supplemental search results having indications that identified documents have been endorsed by the user and/or social network connections of the user are provided. A search query is received and it is determined that the user's social networking data is available. Documents that have been endorsed by the user and/or social network connections of the user are searched to identify those that satisfy the input search query. The satisfying documents are assigned a relevance score to determine whether each satisfying document is relevant enough to be shown and to determine placement of the document on the SERP relative to standard search results. Upon rendering of the SERP, a supplemental search result identifying documents that were endorsed by the user and/or social network connections of the user is presented, as is an indication about which social network connection(s) of the user endorsed the document.

Supplemental search results having social connection endorsement data on a SERP

A system and method of crawling at least one website comprising at least one URL includes maintaining a lookup structure comprising all of the URLs known to be on a website; calculating a hub score for each webpage of the website to be recrawled, wherein the hub score measures how likely the to be recrawled webpage includes links to fresh content published on the website; sorting all the to be recrawled pages by their hub scores; and crawling the to be recrawled pages in order from highest hub scores to lowest hub scores. The calculating comprises computing a first value equaling a percentage of a number of new relative URLs on the to be recrawled page; computing a second value equaling a percentage of a previous hub score of the to be recrawled page; and computing the hub score as a sum of the first and the second values.

Microhubs and its applications

A system and method for prioritizing a fetch order of web pages. The method comprises extracting by a web crawler a set of candidate web pages to be crawled. Each web page in the set of candidate web pages is associated with a website in a computer network. A determination is made to determine if a first website score for the website is in a website score database. The first website score is associated with web pages in the set of candidate web pages if the first website score exists in the website score database. The set of candidate web pages is prioritized with respect to an associated website score for each web page in the candidate set of web pages. Content is retrieved from the set of candidate web. Hyperlinks are extracted from the content. The hyperlinks are stored in a memory unit.

System and method for prioritizing websites during a webcrawling process

Growing community of inventors

San Jose, CA, United States of America

Michael Ching