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Scalable trend detection in a personalized search context

  • US 10,210,214 B2
  • Filed: 08/27/2014
  • Issued: 02/19/2019
  • Est. Priority Date: 08/27/2014
  • Status: Expired due to Fees
First Claim
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1. A computer-implemented method comprising:

  • obtaining search results matching a search query, wherein the search query is a query defined by a user, wherein the search results comprise occurrences of events, wherein each occurrence is associated with a timestamp;

    in response to obtaining the search results, calculating, by a processor, trend scores for the events based on the occurrences comprised by the search results, wherein said calculating comprises recursively calculating the trend scores for the events using a formula recursive in time, whereby a trend score of an event at a specific time is based on the trend score for the event at a previous time, and on a weighted average of an arrival rate of the event at the specific time; and

    providing an output to the user based on the trend scores,wherein computation of the trend score has complexity of O(n) wherein n is a number of the occurrences comprised in the search results, andwherein said recursive calculating is based on the following recursive formula;


    trendScore(t, i)=β

    ·

    trendScore(t,i−

    1)+eventScore−

    serviceRate(t,i),wherein t is an event, wherein i is a time, wherein trendScore(t,i) is a trend score of the event at time i based on the occurrences at and before time i, wherein eventScore is a score of the event at time i based on occurrences of the event at time i, wherein serviceRate is the weighted average of the arrival rate of the event t at time i, and wherein β

    is a constant.

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