PROFIT-BASED LAYOUT DETERMINATION FOR WEBPAGE IMPLEMENTATION
Systems and methods for automatic generation and efficient exploration of a large number of webpage layouts to discover a layout with superior empirical performance. A set of variants are displayed to visitors in accordance with a display probability distribution. Data related to visitors' interactions to the variants are collected and processed to evaluate their respective profit-related performances. The display probability distribution may be dynamically adjusted based on the profit-based evaluation. A profit brought by a webpage layout may be ascribed to a number of revenue sources. These difference revenues may be tracked and summed together to yield a profit assessment for a layout variant. Profit performance of a layout variant may be calculated using a Gaussian with NΓ −1 prior model, or a Gaussian-Dirac delta mixture mode.
1 . A computer implemented method of automatically determining a webpage layout, said method comprising:
accessing a set of test webpage layouts;
selecting for display said set of test webpage layouts to visitors to a website in accordance with a display probability distribution thereof, wherein each test webpage layout is assigned with a respective display probability value within said distribution;
evaluating said set of test webpage layouts based on profit associated with purchase activities based on commodities associated with said set of test webpage layouts;
adjusting said display probability distribution based on said evaluating;
repeating said selecting and said evaluating; and
selecting a resultant webpage layout from said test webpage layouts based on said evaluation.
2 . The computer implemented method of claim 1 , wherein said evaluating comprises determining expected profit rates of said set of test webpage layouts based on a determination that a profit rate is normally distributed, and wherein further said adjusting comprises adjusting said display probability distribution based on a distribution of said expected profit rates over said set of test webpage layouts.
3 . The computer implemented method of claim 2 , wherein said determining comprises updating hyper-parameters of a respective normal distribution based on observed profit values associated with a respective test webpage layout, and wherein an expected profit rate of said respective test webpage layout corresponds to a mean of said observed profit values.
4 . The computer implemented method of claim 3 , wherein said observed profit values are computed based on view-events resulting in purchases.
5 . The computer implemented method of claim 3 , wherein said expected profit rate of said respective test webpage layout is derived by a weighted integration of K components of profit rates in accordance with a multinomial distribution, wherein K represents a number of commodity categories presented through said respective test webpage layout, and wherein each of said K components correspond to a respective Gaussian distribution.
6 . The computer implemented method of claim 2 , wherein said determining comprises determining an expected profit rate of a respective test webpage layout, wherein said expected profit rate is determined based on a conversion rate and observed profit values related to said respective test webpage layout.
7 . The computer implemented method of claim 6 , wherein said expected profit rate is computed by a combination of a zero-profit component and a normally distributed component, wherein each component comprises a respective probability value related to said conversion rate, wherein said zero-profit component corresponds to view-events with no purchases, and wherein said normally distributed component corresponds to purchase events with purchases.
8 . The computer implemented method of claim 7 , wherein said expected profit rate of said respective test webpage layout is derived by a weighted integration of K components of profit rates in accordance with a multinomial distribution, wherein K represents a number of commodity categories presented through said respective test webpage layout, and wherein each of said K components correspond to a respective normal distribution with a respective set of hyper-parameters.
9 . The computer implemented method of claim 7 , wherein a respective set of hyper parameters corresponding to each of said K components are updated in a predefined order based on a total number of purchases, a total number of purchases per category, a sum of all profit per category, and a sum of all squared profit per category.
10 . The computer implemented method of claim 1 , wherein said set of test webpage layouts are displayed with contests, offers, e-readers on sale, and book-content for different lengths of time.
11 . A non-transitory computer-readable storage medium embodying instructions that, when executed by a processing device, cause the processing device to perform a method of determining a profit rate of a webpage layout, said method comprising:
selecting for displaying said webpage layout to visitors to a website with content comprising commodities in N view events, wherein N is an integer;
determining a conversion rate r of said webpage layout;
determining a respective profit m i resulted from each view-event, wherein i=1, 2, . . . , N;
deriving a profit rate distribution p from a zero-profit component and a normally distributed component, wherein each component comprises a respective probability value related to said conversion rate, wherein said zero-profit component corresponds to view-events resulting in no purchases, and wherein said normally distributed component corresponds to view events resulting in purchases; and
deriving a profit rate of said webpage layout based on said profit rate distribution.
12 . The non-transitory computer-readable storage medium of claim 11 , wherein observed profits related to said webpage layout comprise K profit categories, wherein K is an integer, and wherein said method further comprises:
determining respective category weights of said K profit categories in accordance with a multinomial distribution based on said observed profits, wherein each profit category is assumed to have a respective normal distribution; and
updating hyper-parameters of a respective normal distribution for each profit category based on a total number of purchases, a total number of purchases per category, a sum of all profit per category, and a sum of all squared profit per category.
13 . The non-transitory computer-readable storage medium of claim 11 , wherein said conversion rate is determined in accordance with a Beta-Bernoulli model, and wherein said profit rate corresponds to an expected value of a profit-per-view.
14 . The non-transitory computer-readable storage medium of claim 11 , wherein said determining a respective profit m i comprises:
sampling a variable s i from said conversion rate to determine whether or not to purchase;
sampling said respective profit m i from a normal distribution if a purchase is resulted from a view event; and
setting said respective profit m i to be zero if no purchase is resulted from a view event.
15 . A system comprising:
a processor;
a network circuit; and
a memory coupled to said processor and comprising instructions that, when executed by said processor, automatically determine a webpage layout for a website, said method comprising:
accessing a set of test layouts;
selecting for display said set of test layouts to visitors to said website in accordance with a display probability distribution, wherein each test layout is assigned with a respective display probability value within said distribution;
evaluating said set of test layouts based on profit associated with commodities presented in said set of test layouts;
adjusting said display probability distribution based on said evaluating;
repeating said displaying and said evaluating; and
selecting a resultant layout from said test layouts based on said evaluating for subsequent displays.
16 . The system of claim 15 , wherein said evaluating comprises determining expected profit rates of said set of test layout based on a determination that a profit rate is normally distributed, and wherein further said display probability distribution is adjusted based on a distribution of said expected profit rates over said set of test layouts.
17 . The system of claim 16 , wherein said determining comprises updating hyper-parameters of a respective normal distribution based on observed profit values associated with a respective test layout, and wherein an expected profit rate of said respective test layout corresponds to a mean of said observed profit values.
18 . The system of claim 17 , wherein said expected profit rate of said respective test layout is derived by a weighted integration of K components of profit rates in accordance with a multinomial distribution, wherein K represents a number of commodity categories presented through said respective test layout, and wherein each of said K components correspond to a respective Gaussian distribution.
19 . The system of claim 15 , wherein said determining comprises determining an expected profit rate of a respective test layout, wherein said expected profit rate is determined based on a conversion rate and observed profit values related to said respective test layout.
20 . The system of claim 19 , wherein said expected profit rate is computed by a combination of a zero-profit component and a normally distributed component, wherein each component comprises a respective probability value related to said conversion rate, wherein said zero-profit component corresponds to view-events with no purchases, and wherein said normally distributed component corresponds to purchase events with purchases.