Method and medium for determining whether a target item is related to a candidate affinity item

1. A method for recommending affinity products, the method comprising:

collecting data corresponding to monitored actions of a user of a web site, the monitored actions collected over a plurality of browsing sessions;

aggregating said collected data into aggregate groups, wherein said collected data comprises speedometer data which is aggregated and stored for values exceeding a threshold value in order to limit RAM usage;

identifying, with a server computer, at least one affinity product related to a target product, the identifying based on the monitored actions and a weighted formula, wherein weights are used to specify different probabilities to interrelate actions performed by said user across sessions while visiting said web site, wherein said weights combine a common affiliated product in a buy-to-buy, view-to-view, view-to-buy and abandon-to-buy actions;

receiving a selection of variables a, b, c and d assigned a percentage between 0 and 100% to select a recommended affinity product according to said weighted formula:

( a×BB )+( b×VV )+( c×VB )+( d×AB ),

wherein said BB corresponds to said buy-to-buy action, wherein said VV corresponds to said view-to-view action, wherein said VB corresponds to said view-to-buy action, and wherein said AB corresponds to said abandon-to-buy action;

displaying, with the server computer, a preview of the at least one affinity product and said stored speedometer data shown in a report module of a speedometer;

modifying at least one weight of the weighted formula; and

displaying, with the server computer, an updated preview showing any change in the at least one affinity product due to the modifying.

2. The method of claim 1 further wherein said at least one affinity product comprises a plurality of affinity products.

3. The method of claim 2 further comprising:

receiving a selection of at least one product to exclude from identification as said at least one affinity product.

4. The method of claim 2 wherein said weighted formula provides at least two correlations of a browsing or buying action of a first product by a first user with browsing, abandoning or buying actions of a group of users who also browsed or bought said first product.

5. The method of claim 2 wherein said monitored actions include keywords, and said at least one affinity product is a product on which action was taken by a group of users who used the same keyword.

6. The method of claim 2 further comprising:

receiving a selection indicating a number of affinity products to be identified.

7. The method of claim 2 further comprising:

displaying a conversion measure for the at least one affinity product in the displayed preview.

8. The method of claim 1 further comprising:

exporting a list of the at least one affinity product to a merchant's web site for use in displaying the at least one affinity product when the target product is displayed.

9. A method for recommending affinity products, the method comprising:

collecting data corresponding to monitored actions of a user of a web site, the monitored actions collected over a plurality of browsing sessions;

aggregating said collected data into aggregate groups, wherein said collected data comprises speedometer data which is aggregated and stored for values exceeding a threshold value in order to limit RAM usage;

providing a formula for identifying affinity products based on said monitored actions, wherein weights are used to specify different probabilities to interrelate actions performed by said user across sessions while visiting said web site, wherein said weights combine a common affiliated product in a buy-to-buy, view-to-view, view-to-buy and abandon-to-buy actions;

receiving a selection of variables a, b, c and d assigned a percentage between 0 and 100% to select a recommended affinity product according to said formula:

( a×BB )+( b×VV )+( c×VB )+( d×AB ),

wherein said BB corresponds to said buy-to-buy action, wherein said VV corresponds to said view-to-view action, wherein said VB corresponds to said view-to-buy action, and wherein said AB corresponds to said abandon-to-buy action;

tracking said monitored actions for an identified segment of users;

selecting, with a server computer, at least one affinity product using said formula and said monitored actions of said segment of users; and

displaying, with the server computer, a preview of the at least one affinity product and said stored speedometer data shown in a report module of a speedometer.

10. The method of claim 9 wherein said segment is selected from the group of segments comprising product market segment, time related segment, user characteristic segment, geographical segment and browsing action segment.

11. The method of claim 9 wherein said at least one selected affinity product is selected based on a relation to a target product.

12. The method of claim 9 further comprising:

excluding at least one affinity product from display if it is contained on a list of excluded affinity products.

13. The method of claim 9 wherein the collected data is collected from a plurality of web sites operated by a plurality of web site operators.

14. The method of claim 13 wherein said formula identifies said affinity products based on cross session and cross web site monitored actions.

15. The method of claim 9 , wherein said formula identifies said affinity products based in part on a conversion measure of affinity products.

16. The method of claim 11 , further comprising:

exporting said selected affinity product for display on a results page of a search engine; and

displaying said selected affinity product when a search performed on the search engine results in the target product.

17. A computer program product for recommending affinity products, the computer program product comprising a non-transitory computer readable storage medium having program code embodied therewith, the program code comprising the programming instructions for:

collecting data corresponding to monitored actions of a user of a web site, the monitored actions collected over a plurality of browsing sessions;

aggregating said collected data into aggregate groups, wherein said collected data comprises speedometer data which is aggregated and stored for values exceeding a threshold value in order to limit RAM usage;

identifying, with a server computer, at least one affinity product related to a target product, the identifying based on the monitored actions and a weighted formula, wherein weights are used to specify different probabilities to interrelate actions performed by said user across sessions while visiting said web site, wherein said weights combine a common affiliated product in a buy-to-buy, view-to-view, view-to-buy and abandon-to-buy actions;

receiving a selection of variables a, b, c and d assigned a percentage between 0 and 100% to select a recommended affinity product according to said weighted formula:

( a×BB )+( b×VV )+( c×VB )+( d×AB ),

wherein said BB corresponds to the buy-to-buy action, wherein said VV corresponds to the view-to-view action, wherein said VB corresponds to the view-to-buy action, and wherein said AB corresponds to the abandon-to-buy action;

displaying, with the server computer, a preview of the at least one affinity product and said stored speedometer data shown in a report module of a speedometer;

modifying at least one weight of the weighted formula; and

displaying, with the server computer, an updated preview showing any change in the at least one affinity product due to the modifying.

18. The computer program product as recited in claim 17 , wherein said at least one affinity product comprises a plurality of affinity products.

19. The computer program product as recited in claim 18 , wherein the program code further comprises the programming instructions for:

receiving a selection of at least one product to exclude from identification as said at least one affinity product.

20. The computer program product as recited in claim 18 , wherein said weighted formula provides at least two correlations of a browsing or buying action of a first product by a first user with browsing, abandoning or buying actions of a group of users who also browsed or bought said first product.