Method for Determining an Enhanced Value to Keywords Having Sparse Data
A method for associating sparse keywords with non-sparse keywords. The method comprises determining from metrics of a plurality of keywords a list of sparse keywords and non-sparse keywords; generating a similarity score for each sparse keyword with respect of each non-sparse keyword; associating a sparse keyword with a non-sparse keyword; and storing the association between the non-sparse keyword and the sparse keyword in a database.
1 . A method for associating sparse keywords with non-sparse keywords, comprising:
determining from metrics of a plurality of keywords a list of sparse keywords and non-sparse keywords;
generating a similarity score for each sparse keyword with respect of each non-sparse keyword;
associating a sparse keyword with a non-sparse keyword; and
storing the association between the non-sparse keyword and the sparse keyword in a database.
2 . The method of claim 1 , wherein the association of the sparse keyword with the non-sparse keyword is performed if similarity between the sparse keyword and the non-sparse keyword is above a predetermined threshold.
3 . The method of claim 1 , wherein the association of the sparse keyword with the non-sparse keyword includes weighting data of at least one of non-sparse keywords and sparse keywords using a general monotonic function of the similarity score.
4 . The method of claim 1 , wherein the method is embodied as a series of instructions on a non-transitory and tangible medium readable by the computing device.
5 . The method of claim 1 , wherein the determination of the sparse keywords and non-sparse keywords is performed using a fitting predictive model.
6 . The method of claim 5 , wherein the fitting predictive model is at least one of: a non-linear regression and a generalized linear model.
7 . The method of claim 1 , wherein the similarity score is computed as a ratio between a residual sum of squares of a model for a non-sparse keyword metrics applied to the data of the sparse keyword metrics and a residual sum of squares of the model of the non-sparse keyword metrics.
8 . The method of claim 1 , further comprising:
receiving a query containing a keyword;
checking the database for at least a match with a keyword in the database; and
providing, responsive of the query, one or more associated keywords with the query keyword, wherein each of the associated keyword is a sparse keyword.
9 . A method for associating sparse keywords with non-sparse keywords, comprising:
determining from metrics of a plurality of keywords a list of sparse keywords and non-sparse keywords;
creating a plurality clusters from the plurality of keywords;
generating a similarity score for each sparse keyword with respect of each of the a plurality clusters;
associating a sparse keyword with a non-sparse keyword in each cluster of the plurality of clusters; and
storing the association between the non-sparse keyword and the sparse keyword in a database.
10 . The method of claim 9 , wherein the association of the sparse keyword with the non-sparse keyword is performed if similarity between the sparse keyword and at least one cluster is above a predetermined threshold.
11 . The method of claim 9 , wherein the association of the sparse keyword with the non-sparse keyword includes weighting the data of the plurality of clusters using a general monotonically increasing function of the similarity score.
12 . The method of claim 9 , wherein the method is embodied as a series of instructions on a non-transitory and tangible medium readable by the computing device.
13 . The method of claim 9 , wherein the determination of sparse keywords and non-sparse keywords is performed using a predictive model.
14 . The method of claim 13 , wherein the predictive model is at least one of: a linear regression and a generalized linear model.
15 . The method of claim 9 , further comprising:
receiving a query containing a keyword;
checking the database for at least a match with a keyword in the database; and
providing, responsive of the query, one or more associated keywords with the query keyword, each of the associated keyword is a sparse keyword.
16 . A system for associating sparse keywords with non-sparse keywords, comprising:
a processor connected to a memory by a computer link, the memory having code readable and executable by the processor;
an interface connected to the computer link enabling communication of the system to one or more peripheral devices by one or more communication links; and
a data storage connected to the processor for storing and retrieving information therein; wherein the processor fetches metrics of a plurality of keywords through at least one of the interface and the data storage; determines from the plurality of keywords a list of sparse keywords and non-sparse keywords; generates a similarity score for each sparse keyword with respect of each non-sparse keyword; associates a sparse keyword with a non-sparse keyword; and stores the association between the non-sparse keyword and the sparse keyword in a database.
17 . The system of claim 16 , wherein the association of the sparse keyword with the non-sparse keyword is performed if similarity between the sparse keyword and the non-sparse keyword is above a predetermined threshold.
18 . The system of claim 16 , wherein the association of the sparse keyword with the non-sparse keyword includes weighting the data of the non-sparse keywords and/or other sparse keywords using a monotonic function of the similarity score.
19 . The system of claim 16 , wherein the processor further creates clusters from the plurality of keywords.
20 . The system of claim 16 , wherein processor enables the determination of sparse keywords and non-sparse keywords using a predictive model.
21 . The system of claim 20 , wherein the predictive model is at least one of a linear regression a generalized linear method.
22 . The system of claim 16 , wherein the system is adapted to return a list of spare keywords associated with an input keyword included in a received a query.