Systems And Methods For Serving Product Recommendations
Example systems and methods for serving product recommendations for key performance indicator (KPI) optimization are described. In one implementation, a method selects at least a first item from a set of items such that a first performance indicator among a plurality of performance indicators is improved as a result of a user purchasing the first item in response to viewing at least the first item on a webpage of a website. The method also displays a graphic or textual representation of at least the first item on the webpage as a recommendation to the user.
1 . A method comprising:
selecting, by one or more processors, at least a first item from a set of items such that a first performance indicator among a plurality of performance indicators is improved as a result of a user purchasing the first item in response to viewing at least the first item on a webpage of a website; and
displaying a graphic or textual representation of at least the first item on the webpage as a recommendation to the user.
2 . The method of claim 1 , wherein the plurality of performance indicators comprise revenue, profit margin, inventory and one or more user-specific indicators.
3 . The method of claim 1 , where the selecting comprises:
computing a probability distribution related to a likelihood of the user purchasing the first item from among a subset of items of the set of items when the subset of items are displayed to the user on the webpage.
4 . The method of claim 3 , wherein the probability distribution is proportion to a product of a click-through rate and a buy-through rate, wherein the click-through rate is related to a likelihood of the user clicking on the graphic or textual representation of the first item on the webpage when the user is viewing the webpage, and wherein the buy-through rate is related to a likelihood of the user purchasing the first item when the user is viewing the webpage.
5 . The method of claim 4 , wherein the computing the probability distribution comprises approximating the click-through rate using data on co-viewing of one or more other items from the set of items that are displayed on the webpage with the first item.
6 . The method of claim 3 , wherein the computing the probability distribution comprises:
computing a prior probability distribution related to a first parameter and a second parameter, the first parameter associated with a number of users who viewed the first item on the webpage, the second parameter associated with a number of users who viewed the first item who also viewed another item from the set of items on the webpage; and
applying a soft-threshold function to the first and the second parameters to limit the prior probability distribution to be equivalent to an action of a plurality of pseudo-visitors to the website.
7 . The method of claim 6 , wherein the computing the probability distribution further comprises:
scaling the prior probability distribution to provide a rescaled prior probability distribution; and
combining a probability of a likelihood of the user clicking on a graphic or textual representation of the first item on the webpage when the user is viewing the webpage and a Beta function of parameters of the scaled, soft-thresholded prior probability distribution to provide a posterior probability distribution.
8 . The method of claim 3 , wherein the computing comprises computing based at least in part on information about the user and information about one or more other users.
9 . The method of claim 8 , wherein the information about the user comprises some or all of information related to at least one previous transaction or action taken by the user on the website, a location of the user, demographic information of the user, and a social network of the user.
10 . The method of claim 8 , wherein the information about the one or more other users comprises some or all of information related to one or more other items from the set of items viewed by the one or more other users on the website, one or more other webpages of the website viewed by the one or more other users, at least one previous transaction by each of the one or more other users on the website, a location of each of the one or more other users, demographic information of the one or more other users, a social network of each of the one or more other users, and time of a year at a time of the computing.
11 . The method of claim 1 , further comprising:
receiving, by the one or more processors prior to the selecting, a user input that selects the first performance indicator from the plurality of performance indicators.
12 . The method of claim 1 , further comprising:
computing, by the one or more processors, a value of an expected performance indicator for a recommendation associated with each item of the set of items;
selecting a second item of the set of items having a highest value of the expected performance indicator; and
displaying a graphic or textual representation of at least the second item on the webpage as a recommendation to the user.
13 . A method comprising:
selecting, by one or more processors, a first subset of items from a set of items for display to a user on a first webpage of a website, the selecting based at least in part on information about one or more other users;
displaying a graphic or textual representation of each item in the first subset on the first webpage as first recommendations to the user;
selecting, by one or more processors, a second subset of items from a set of items for display to the user on a second webpage of the website such that a first performance indicator among a plurality of performance indicators is improved as a result of the user purchasing an item from the second subset of items in response to viewing the second subset of items on the second webpage, the selecting based at least in part on information about the user; and
displaying a graphic or textual representation of each item in the second subset on the second webpage as second recommendations to the user.
14 . The method of claim 13 , wherein the plurality of performance indicators comprise revenue, profit margin, inventory and one or more user-specific indicators.
15 . The method of claim 13 , wherein the information about the user comprises some or all of information related to at least one previous transaction or action taken by the user on the website, a location of the user, demographic information of the user, and a social network of the user.
16 . The method of claim 13 , wherein the information about the one or more other users comprises some or all of information related to one or more other items from the set of items viewed by the one or more other users on the website, one or more other webpages of the website viewed by the one or more other users, at least one previous transaction or action taken by each of the one or more other users on the website, a location of each of the one or more other users, demographic information of the one or more other users, a social network of each of the one or more other users, and time of a year at a time of the computing.
17 . The method of claim 13 , where the selecting the first subset of items from the set of items comprises:
computing a probability distribution related to a likelihood of the user purchasing a first item from among the first subset of items when the first subset of items are displayed to the user on the first webpage, wherein the computing the probability distribution comprises:
computing a prior probability distribution related to a first parameter and a second parameter, the first parameter associated with a number of users who viewed the first item on the first webpage, the second parameter associated with a number of users who viewed the first item who also viewed another item from the set of items on the first webpage;
applying a soft-threshold function to the first and the second parameters to limit the prior probability distribution to be equivalent to an action of a plurality of pseudo-visitors to the website;
scaling the prior probability distribution to provide a rescaled prior probability distribution; and
combining a probability of a likelihood of the user clicking on a graphic or textual representation of the first item on the first webpage when the user is viewing the first webpage and a Beta function of parameters of the scaled, soft-thresholded prior probability distribution to provide a posterior probability distribution.
18 . The method of claim 13 , further comprising:
receiving, by the one or more processors prior to the selecting, a user input that selects the first performance indicator from the plurality of performance indicators.
19 . The method of claim 13 , further comprising:
computing, by the one or more processors, a value of an expected performance indicator for a recommendation associated with each item of the set of items;
selecting a second item of the set of items having a highest value of the expected performance indicator; and
displaying a graphic or textual representation of at least the second item on the webpage as a recommendation to the user.
20 . An apparatus comprising:
a memory configured to store data and one or more sets of instructions; and
one or more processors coupled to the memory, the one or more processors configured to execute the one or more sets of instructions and perform operations comprising:
selecting, by one or more processors, at least a first item from a set of items such that a first performance indicator among a plurality of performance indicators is improved as a result of a user purchasing the first item in response to viewing at least the first item on a webpage of a website; and
displaying a graphic or textual representation of at least the first item on the webpage as a recommendation to the user.