FACILITATING SMART ADVERTISING ON CURATED CONTENT-BASED NETWORKING WEBSITES IN AN ON-DEMAND SERVICES ENVIRONMENT

• US 20150186931A1
• Filed: 10/09/2014
• Published: 07/02/2015
• Est. Priority Date: 12/31/2013
• Status: Active Grant

• Current Assignee: Salesforce.com, Inc.
• Original Assignee: Salesforce.com, Inc.
• Inventors: FLAKE, GARY W.
• Granted Patent: US 10,902,459 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06Q 30/0247 Calculate past, present or future revenues
• CPC Class Codes: G06Q 30/0251 Targeted advertisements
• CPC Class Codes: G06Q 30/0277 Online advertisement

Abstract

In accordance with embodiments, there are provided mechanisms and methods for facilitating smart advertising on curated content-based networking websites in an on-demand services environment in a multi-tenant environment according to one embodiment. In one embodiment and by way of example, a method includes receiving a request for advertisement on a website, where the request may be received from an advertising organization. The method may further include estimating one or more factors relating to the advertisement, computing, based on the estimation, expected revenue relating to the advertisement, and placing the advertisement on the website based on the expected revenue.

Claims

1. A method comprising:

receiving a request for advertisement on a website, wherein the request is received from an advertising organization;

estimating one or more factors relating to the advertisement;

computing, based on the estimation, expected revenue relating to the advertisement; and

placing the advertisement on the website based on the expected revenue.

2. The method of claim 1, wherein the one or more factors include one or more of a probability of relevance, a rate of action, and a cost of action, wherein the website comprises a curated content-based networking website.

3. The method of claim 2, wherein the probability of relevance comprises an estimation of relevance of the advertisement with contents of the website, wherein the contents include one or more of primary contents obtained from primary sources, and secondary contents obtained from secondary sources, wherein the secondary contents co-occur with the primary contents.

4. The method of claim 3, wherein the probability of relevance is estimated via one or more techniques including one or more of the Jaccard Index, the term frequency-inverse document frequency (“

tf-idf”

), a web search relevance.

5. The method of claim 2, wherein the rate of action comprises a frequency at which an action is associated with the advertisement once the advertisement is placed on the website, wherein the action includes one or more users actions including one or more of clicking on the advertisement, pinning or repining the advertisement, liking the advertisement, sending or sharing the advertisement, opening a webpage associated with the advertisement, and browsing or shopping on the webpage associated with the advertisement.

6. The method of claim 2, wherein the cost of action comprises a price tag for each action associated with the advertisement, wherein the price tag includes a price derived from supply and demand.

7. The method of claim 1, further comprising evaluating the expected revenue associated with the advertisement, wherein evaluating includes rejection the advertisement or accepting the advertisement for placement on the website, wherein accepting includes assigning the advertisement to a queue prior to selecting the advertisement for placement on the website, wherein the queue includes a plurality of advertisements.

8. The method of claim 7, wherein the selection of the advertisement from the queue over the plurality of advertisements is performed based on one or more selection techniques, deterministic or probabilistic, allowing for revenues to be optimized while simultaneously facilitating selections for improving a confidence level relating to statistical estimates.

9. A system comprising:

a processor and a memory to execute instructions at the system; and

a mechanism to;

receive a request for advertisement on a website, wherein the request is received from an advertising organization;

estimate one or more factors relating to the advertisement;

compute, based on the estimation, expected revenue relating to the advertisement; and

place the advertisement on the website based on the expected revenue.

10. The system of claim 9, wherein the one or more factors include one or more of a probability of relevance, a rate of action, and a cost of action, wherein the website comprises a curated content-based networking website.

11. The system of claim 10, wherein the probability of relevance comprises an estimation of relevance of the advertisement with contents of the website, wherein the contents include one or more of primary contents obtained from primary sources, and secondary contents obtained from secondary sources, wherein the secondary contents co-occur with the primary contents.

12. The system of claim 11, wherein the probability of relevance is estimated via one or more techniques including one or more of the Jaccard Index, the term frequency-inverse document frequency (“

tf-idf”

), a web search relevance.

13. The system of claim 10, wherein the rate of action comprises a frequency at which an action is associated with the advertisement once the advertisement is placed on the website, wherein the action includes one or more users actions including one or more of clicking on the advertisement, pinning or repining the advertisement, liking the advertisement, sending or sharing the advertisement, opening a webpage associated with the advertisement, and browsing or shopping on the webpage associated with the advertisement.

14. The system of claim 10, wherein the cost of action comprises a price tag for each action associated with the advertisement, wherein the price tag includes a price derived from supply and demand.

15. The system of claim 9, wherein the mechanism is further to evaluate the expected revenue associated with the advertisement, wherein evaluating includes rejection the advertisement or accepting the advertisement for placement on the website, wherein accepting includes assigning the advertisement to a queue prior to selecting the advertisement for placement on the website, wherein the queue includes a plurality of advertisements.

16. The system of claim 15, wherein the selection of the advertisement from the queue over the plurality of advertisements is performed based on one or more selection techniques, deterministic or probabilistic, to allow for revenues to be optimized while simultaneously facilitating selections to improve a confidence level relating to statistical estimates.

17. A machine-readable medium comprising a plurality of instructions which, when executed by a processing device, cause the processing device to perform one or more operations comprising:

receiving a request for advertisement on a website, wherein the request is received from an advertising organization;

estimating one or more factors relating to the advertisement;

computing, based on the estimation, expected revenue relating to the advertisement; and

placing the advertisement on the website based on the expected revenue.

18. The machine-readable medium of claim 17, wherein the one or more factors include one or more of a probability of relevance, a rate of action, and a cost of action, wherein the website comprises a curated content-based networking website.

19. The machine-readable medium of claim 18, wherein the probability of relevance comprises an estimation of relevance of the advertisement with contents of the website, wherein the contents include one or more of primary contents obtained from primary sources, and secondary contents obtained from secondary sources, wherein the secondary contents co-occur with the primary contents.

20. The machine-readable medium of claim 19, wherein the probability of relevance is estimated via one or more techniques including one or more of the Jaccard Index, the term frequency-inverse document frequency (“

tf-idf”

), a web search relevance.

21. The machine-readable medium of claim 18, wherein the rate of action comprises a frequency at which an action is associated with the advertisement once the advertisement is placed on the website, wherein the action includes one or more users actions including one or more of clicking on the advertisement, pinning or repining the advertisement, liking the advertisement, sending or sharing the advertisement, opening a webpage associated with the advertisement, and browsing or shopping on the webpage associated with the advertisement.

22. The machine-readable medium of claim 18, wherein the cost of action comprises a price tag for each action associated with the advertisement, wherein the price tag includes a price derived from supply and demand.

23. The machine-readable medium of claim 17, wherein the one or more operations further comprise evaluating the expected revenue associated with the advertisement, wherein evaluating includes rejection the advertisement or accepting the advertisement for placement on the website, wherein accepting includes assigning the advertisement to a queue prior to selecting the advertisement for placement on the website, wherein the queue includes a plurality of advertisements.

24. The machine-readable medium of claim 23, wherein the selection of the advertisement from the queue over the plurality of advertisements is performed based on one or more selection techniques, deterministic or probabilistic, allowing for revenues to be optimized while simultaneously facilitating selections for improving a confidence level relating to statistical estimates.