Engagement-oriented recommendation principle
A method selects a predictor item that has a relevance to a user. The method receives a set of affinity items having affinity scores that relate the predictor item to the affinity items. The method filters the list of affinity items based on the affinity scores, and selects a first set of affinity items from the filtered items. For each selected affinity item, the method calculates a difference score from the predictor item, and selects a first affinity item based on the difference score for the first affinity item. Preferably, content is presented to the user based on the selected first affinity item. Additional embodiments include a system and/or computer readable medium having instructions for execution of the foregoing.
1. A computer implemented method of targeting, the method comprising:
receiving, at a computer, online activity of at least one user;
selecting, using a computer, a predictor item having a relevance to the online activity of the user;
receiving, at a computer, a plurality of affinity items having affinity scores that indicate relevance between the predictor item and the affinity items, wherein the affinity items and the predictor items are categorized in a classification system comprising a plurality of nodes arranged in a hierarchical tree structure;
identifying, using a computer, affinity items from among the plurality of affinity items having affinity scores that are greater than a threshold;
calculating, using computer, a difference score measuring a difference between the identified affinity items and the predictor item, wherein the difference score corresponds to the number of nodes separating said predictor item and the identified affinity items within said hierarchical tree structure;
selecting, using a computer, a first affinity item from among the identified affinity items based on a difference score for the first affinity item; and
using, in a computer, the first affinity item to target advertising to the user.
2. The computer implemented method of claim 1 , further comprising: presenting to the user, using a computer, content based on the selected first affinity item.
3. The computer implemented method of claim 1 , further comprising:
for the first affinity item, determining, using a computer, a first total score comprising the affinity score and the difference score for the first affinity item;
for a second affinity item, determining, using a computer, a second total score comprising the affinity score and the difference score for the second affinity item; and
selecting, using a computer, the first affinity item further comprising: comparing the first and second total scores for the first and second affinity items.
4. The computer implemented method of claim 3 , the step of determining a total score further comprising: applying, using computer, a weight factor to the difference score thereby scaling the difference score to the affinity score, and further thereby adjusting the significance of the difference score in the total score.
5. A computer readable medium storing a program having sets of instructions for targeting, the sets of instructions comprising instructions for:
receiving, at a computer, online activity of at least one user;
selecting, using a computer, a predictor item having a relevance to the online activity of the user;
receiving, at a computer, a plurality of affinity items having affinity scores that indicate relevance between the predictor item and the affinity items, wherein the affinity items and the predictor items are categorized in a classification system comprising a plurality of nodes arranged in a hierarchical tree structure;
identifying, using a computer, affinity items from among the plurality of affinity items having affinity scores that are greater than a threshold;
calculating, using computer, a difference score measuring a difference between the identified affinity items and the predictor item, wherein the difference score corresponds to the number of nodes separating said predictor item and the identified affinity items within said hierarchical tree structure;
selecting, using a computer a first affinity item from among the identified affinity items based on a difference score for the first affinity item; and
using, in a computer, the first affinity item to target advertising to the user.
6. The computer readable medium of claim 5 , further comprising instructions for: presenting to the user, content based on the selected first affinity item.
7. The computer readable medium of claim 5 , further comprising instructions for:
for the first affinity item, determining a first total score comprising the affinity score and the difference score for the first affinity item;
for a second affinity item, determining a second total score comprising the affinity score and the difference score for the second affinity item; and
the instructions for selecting the first affinity item further comprising instructions for: comparing the first and second total scores for the first affinity item and the second affinity item.
8. The computer readable medium of claim 7 , determining a total score further comprising: applying a weight factor to the difference score thereby scaling the difference score to the affinity score, and further thereby adjusting the significance of the difference score in the total score.
9. A system for targeting, the system configured for:
receiving, at a computer, online activity of at least one user;
selecting, using a computer, a predictor item having a relevance to the online activity of the user;
receiving, at a computer, a plurality of affinity items having affinity scores that indicate relevance between the predictor item and the affinity items, wherein the affinity items and the predictor items are categorized in a classification system comprising a plurality of nodes arranged in a hierarchical tree structure;
identifying, using a computer, affinity items from among the plurality of affinity items having affinity scores that are greater than a threshold;
calculating, using computer, a difference score measuring a difference between the identified affinity items and the predictor item, wherein the difference score corresponds to the number of nodes separating said predictor item and the identified affinity items within said hierarchical tree structure;
selecting, using computer, a first affinity item from among the identified affinity items based on a difference score for the first affinity item; and
using, in a computer, the first affinity item to target advertising to the user.
10. The system of claim 9 , further configured for: presenting to the user, content based on the selected first affinity item.
11. The system of claim 9 , further configured for:
for the first affinity item, determining a first total score comprising the affinity score and the difference score for the first affinity item;
for a second affinity item, determining a second total score comprising the affinity score and the difference score for the second affinity item; and
the system comprising a comparator for comparing the first and second total scores for the first affinity item and the second affinity item.
12. The system of claim 11 , determining a total score further comprising: a weight factor for scaling the difference score to the affinity score, and further for adjusting the significance of the difference score in the total score.