REALTIME POPULARITY PREDICTION FOR EVENTS AND QUERIES

• US 20100114954A1
• Filed: 10/28/2008
• Published: 05/06/2010
• Est. Priority Date: 10/28/2008
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: LAM, ANDY, BUCKLEY, JAMIE PAUL, SPENCER, WILLIAM ALEXANDER JR., SAREEN, BHRIGHU
• Application Number:
  US12/259,665
• Publication Number:
  US 20100114954A1
• Time in Patent Office: Days
• US Class Current: 707/776
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques

Abstract

A system, media, and method for realtime popularity prediction for event and queries are provided. The popularity prediction is made by a prediction engine that is coupled to a search engine, a crawler, and a sentiment component. The prediction engine determines a change in popularity for an event or a query based on content provided by the crawler, sentiments identified by the sentiment component, and queries received in realtime by the search engine. The prediction engine may also use the content, sentiments, and queries to predict an outcome for a popularity based event.

Claims

1. A computer-implemented method to forecast the outcome of an event, the computer-implemented method comprising:

accessing a log having queries received by a search engine, search navigation data for users that access search results returned by the search engine, and browsing data received from client devices used by the users;

traversing the log to identify entries that correspond to an event of interest to a user;

assigning a popularity measure to the event based on a count of the identified entries that correspond to the event;

analyzing the identified entries to determine a sentiment associated with the users that access content associated with the event; and

selecting an outcome of the event based on the sentiment of the users that access content associated with the event and a rate of change associated with the popularity measure assigned to the event using the log.

2. The computer-implemented method of claim 1, wherein the event is one of:

a popularity contest, media release, initial public offering, ticket sale, or price of an item.

3. The computer-implemented method of claim 1, wherein the entries include terms of the query, dwell time for content associated with the event, and click through data associated with the content.

4. The computer-implemented method of claim 1, wherein the popularity measure corresponding to the event increases based on updates processed by a web crawler that stores the updates in the log.

5. The computer-implemented method of claim 4, wherein an increase in a rate of publication of content related to the event observed by the web crawler, generates increases in the assigned popularity measure corresponding to the event.

6. The computer-implemented method of claim 4, wherein the popularity measure associated with the event is imputed to queries related to the event.

7. The computer-implemented method of claim 4, wherein a future popularity of the queries is predicted based on changes in the popularity of an event related to the queries.

8. The computer-implemented method of claim 1, wherein the log is updated to include queries received in realtime.

9. The computer-implemented method of claim 8, wherein a seasonal period associated with the queries that are received in realtime impact the popularity measure of the event.

10. The computer-implemented method of claim 8, further comprising monitoring the queries received in realtime to identify significant changes in sentiment or popularity measures for entries in the log.

15. The media of claim 10, wherein each industry has a white list, a gray list, and a red list.

11. One or more computer-readable media storing instructions for performing a method to determine the sentiment for a query, the method comprising:

parsing each query in a log to identify terms that are included in a white list, gray list, and red list;

assigning a positive, negative, or neutral sentiment to the query based on the distribution of the terms in the white list, gray list, and red list; and

generating a popularity measure for each query based on counts included in the query log and the sentiments assigned to the queries.

12. The media of claim 11, wherein the white list consists of terms that assigned a positive sentiment

13. The media of claim 11, wherein the gray list consists of terms that are assigned a neutral sentiment.

14. The media of claim 11, wherein the red list consists of terms that are assigned a negative treatment.

16. A computer prediction system to forecast future popularity for queries, the prediction system comprising:

one or more search engines configured to receive queries from a user and to provide results to the user;

one or more logs coupled to the one or more search engines and configured to store purchase transaction data, browsing data, and queries issued by users, who submit queries to the one or more search engines; and

one or more prediction engines configured to forecast a future popularity of queries that the user is likely to issue in a certain time period based on queries, purchases, and aggregated behaviors for a group of users that issue the queries.

17. The computing system of claim 16, further comprising one or more monitor components configured to monitor queries issued in realtime to the search engine.

18. The computing system of claim 17, further comprising one or more crawler components configured to locate new website content or updated website content related to queries stored in the one or more query logs and to notify the monitor component of a large number of new website content or updated website content regarding a particular subject from a number of different websites.

19. The computing system of claim 16, further comprising one or more sentiment components configured to identify a sentiment associated with queries issued by the user.

20. The computing system of claim 19, wherein the one or more sentiment components select a vector to forecast a future popularity of the queries and provide the vector to the prediction engine, which utilizes the vector to predict a change in the popularity measure associated with the queries.