A Bayesian-based anchor product allocation method and system

Abstract

The present invention relates to a Bayesian-based method and system for allocating goods to hosts, comprising the following steps: obtaining information on goods to be sold and hosts to be assigned; calculating the probability of each host among the hosts to be assigned selling each type of goods; calculating the overall sales probability of each host; calculating the overall sales probability of goods; calculating the probability of each host among the hosts to be assigned being assigned to each type of goods using a Bayesian algorithm; and assigning each good to hosts within a corresponding random number interval based on a random number and the host assignment probability. By combining historical sales data with a Bayesian algorithm to calculate the host assignment probability, the present invention automates the product assignment process, addressing existing issues such as low efficiency, inability to integrate historical sales records, and subjectivity and bias among assigners.

Description

Technical Field

[0001] The present invention belongs to the technical field of network live broadcasting, and specifically relates to a Bayesian-based anchor product allocation method and system. Background Art

[0002] At present, the development of Internet technology has enriched people's shopping methods. On the basis of online shopping, online live shopping has emerged. In online live shopping, the anchor shows the products to customers in real time through live broadcast, demonstrating the product functions, etc. When watching the shopping live broadcast, customers can directly purchase the products displayed by the anchor in the anchor's live broadcast room. Now more and more live broadcasts tend to carry more comprehensive categories, and even open different category numbers to specialize in a certain category; however, for a company, there are many types of products and multiple anchors, which makes it a problem to assign a product to which anchor. This is especially true for second-hand luxury goods. Due to their uniqueness and scarcity, and the fact that each second-hand luxury product is different in category, brand, price and quality, each second-hand luxury product needs to be sold one by one. Therefore, a different distribution method is needed for the distribution of such products. However, the distribution of such second-hand luxury goods in live broadcasts is currently done manually, but manual distribution faces many problems:

[0003] (1) Inefficiency: Manual allocation of products takes a lot of time, especially when there are a large number of products and anchors, which may lead to a slow decision-making process.

[0004] (2) Subjectivity and bias: Manual allocation may be influenced by the personal preferences of the allocator, resulting in some anchors receiving more high-quality products while others may be neglected. This allocation method may also lead to inequality among anchors, affecting team morale and cooperation.

[0005] (3) Insufficient data processing: Manual allocation often lacks in-depth analysis of sales data, customer feedback, and other information, which may lead to unreasonable product allocation. It is difficult to use historical data to analyze which products are more suitable for which anchors and thus optimize allocation.

[0006] (4) Low product utilization: Some products may not be displayed by suitable anchors due to improper allocation, resulting in poor sales. Popular products may be concentrated in a few anchors, while other anchors cannot fully utilize these resources, affecting overall sales performance.

[0007] (5) Increased communication costs: Communication between different teams (such as marketing, sales, customer service, etc.) may be hindered, resulting in poor information transmission and affecting allocation decisions. Summary of the Invention

[0008] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and provide a Bayesian-based anchor product allocation method, which includes the following steps:

[0009] Obtain information on products to be sold and information on hosts to be assigned. The product information includes product type, quantity, and historical sales records for each type of product. The host information includes the number of hosts to be assigned, their historical sales records, number of followers, and livestream room turnover.

[0010] Calculate the probability of each anchor selling each type of product based on product information and the anchor's historical sales records;

[0011] The overall sales probability of each anchor is obtained through weighted calculation based on the anchor's historical sales record, number of fans, and live broadcast room turnover in the anchor's information;

[0012] The overall sales probability of each product category is calculated based on the historical sales records of each type of product;

[0013] Based on the three probabilities calculated above, the probability of each anchor being assigned to each type of product is calculated using the Bayesian algorithm;

[0014] According to the probability of each anchor being assigned to each type of product, a random number interval corresponding to its probability is assigned to each anchor;

[0015] For each product, a corresponding random number is generated, and the random number interval in which the random number falls is determined. The product is allocated to the anchor corresponding to the random number interval. If the allocated product of the corresponding anchor is equal to the second preset value, a random number is re-generated for the product and reallocated.

[0016] Furthermore, the commodities are classified according to commodity category, brand, price and quality.

[0017] Furthermore, the calculation of the probability of each anchor selling each type of product among the anchors to be assigned based on the anchors' historical sales records specifically includes the following steps:

[0018] Obtain information on products to be sold and the sales records of each anchor to be assigned;

[0019] Extract the sales records of each anchor in the first preset time period, and calculate the probability of the anchor selling each type of goods to be sold in the first preset time period based on the sales records in the period;

[0020] Extracting the sales records of each anchor in the second preset time period, and calculating the probability of the anchor selling each type of goods to be sold in the second preset time period based on the sales records in the period;

[0021] The probability of each anchor selling each type of product among the anchors to be assigned is obtained by weighted summing the probability of the first preset time period and the probability of the second preset time period. i |A j ),

[0022] Determine the probability P(B) that each anchor sells each type of product i |A j ) is greater than the first preset value. If less than, the value is set to the first preset value. If greater than or equal to, it remains unchanged.

[0023] Furthermore, the probability of each anchor selling each type of product among the anchors to be assigned is calculated based on the anchor's historical sales records, and the calculation formula is:

[0024] P1(B i |A j )=x 1ij / Y 1ij

[0025] P2(B i |A j )=x 2ij / Y 2ij

[0026] P(B i |A j )=αP1(B i |A j )+βP2(B i |A j )

[0027] Among them, x 1ij is the number of products of type i sold by anchor j during the first preset time period, x 2ij Y is the number of products of type i sold by anchor j during the second preset time period, 1ij Y is the number of products of type i assigned to anchor j in the first preset time period, 2ij is the number of commodities of type i assigned to anchor j in the second preset time period, P1(B i |A j ) is the probability that anchor j sells product of type i within the first preset time period, P2(B i |A j ) is the probability that anchor j sells product of type i within the second preset time period, and α and β are weight values.

[0028] Furthermore, the first preset time period is nearly one month, the second preset time period is nearly half a year to nearly one month, and α is greater than β.

[0029] Furthermore, the overall sales probability of each anchor is obtained by weighted calculation based on the anchor's historical sales record, number of fans, and live broadcast room turnover in the anchor information. The calculation formula is:

[0030] E i =aS j +bF j +cW j

[0031]

[0032] Among them, S j is the sales volume of all products in the first preset time period of anchor j, F j is the number of fans of anchor j, W j is the live streaming volume of anchor j in the first preset time period, P(A j ) is the overall sales probability of anchor j, n is the number of anchors to be assigned, and a, b, and c are weight values.

[0033] Furthermore, the overall sales probability of each commodity is calculated by using the historical sales records of each type of commodity, and the calculation formula is:

[0034] P(B i )=x i / Y i

[0035] Among them, x i Y is the historical sales quantity of product type i, i is the total number of historical items of type i, P(B i ) is the overall sales probability of product type i.

[0036] Furthermore, the three probabilities obtained from the above calculations are used to calculate the probability of each anchor being assigned to each type of product using the Bayesian algorithm, specifically including: calculating the initial probability of each anchor being assigned to each type of product using the Bayesian algorithm based on the three probabilities obtained from the above calculations, normalizing the probability of each anchor to obtain the final probability, and the calculation formula is:

[0037]

[0038] Among them, P(A j |B i ) is the probability that anchor j is assigned to product type i,

[0039] Furthermore, according to the probability of each anchor being assigned to each type of product, a random number interval corresponding to its probability is assigned to each anchor. Specifically, the probability of anchor j being assigned to type i product is P(A j |B i ), then the random number interval of anchor j in product type i is The method generates a corresponding random number, and the range of the random number is [0,100].

[0040] Another aspect of the present invention provides a Bayesian-based anchor product distribution system, comprising:

[0041] The data acquisition module is used to obtain information about the products to be sold and the anchor information to be assigned;

[0042] The host allocation probability calculation module is used to calculate the probability of each host being assigned to each type of product;

[0043] The product allocation module allocates each product using random numbers based on the probability of each anchor being assigned to each type of product.

[0044] Compared with the prior art, the present invention has the following advantages:

[0045] (1) The present invention combines historical sales data and calculates the distribution probability of the anchor according to the Bayesian algorithm, thereby realizing the automation of commodity distribution and solving the problems of low efficiency, inability to combine historical sales records, and subjectivity and bias of the distribution personnel in the existing technology.

[0046] (2) The present invention solves the problem of low product utilization by setting a basic initial value for the probability of each anchor selling a product. Some products may not be displayed by suitable anchors due to improper allocation, resulting in poor sales. Popular products may be concentratedly allocated to a few anchors, and other anchors cannot fully utilize these resources, affecting overall sales performance.

[0047] (3) The present invention provides a method that integrates the above method into a system through computer technology to realize the automation of commodity distribution, thereby solving the problems of low efficiency and increased communication costs in live commodity distribution, and possible communication barriers between different teams (such as marketing, sales, customer service, etc.), resulting in poor information transmission and affecting distribution decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 Flow chart of the method of the present invention. DETAILED DESCRIPTION

[0049] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0050] It is known that there are x types of goods for sale tomorrow, and there are a total of y items. There are m anchors who will broadcast live tomorrow. Assuming that all goods can be sold tomorrow, how to allocate the goods to suitable anchors? To this end, this embodiment provides a Bayesian anchor product allocation method.

[0051] Bayes' theorem is a fundamental theorem in probability theory that describes how existing information can be used to update our understanding of the probability of an event. Named after the 18th-century mathematician Thomas Bayes, Bayes' theorem is widely used in fields such as statistics, machine learning, medicine, and finance.

[0052] The formula of Bayes' theorem is:

[0053] The mathematical expression of Bayes' theorem is as follows:

[0054]

[0055] in:

[0056] P(A|B): The conditional probability of event A occurring given event B.

[0057] P(B|A): The conditional probability of event B occurring given event A.

[0058] P(A): The prior probability of event A occurring.

[0059] P(B): The total probability of event B occurring.

[0060] Based on the above Bayes theorem, if Figure 1 As shown, the specific process of this embodiment is as follows:

[0061] Step 1: Get the information of products to be sold tomorrow and the information of anchors to be assigned. The product information includes the product type, quantity and historical sales records of each type of product. The anchor information includes the number of anchors to be assigned, the anchor's historical sales record, the number of fans, and the flow of the live broadcast room. The products are classified according to the brand, price and quality of the products.

[0062] Step 2: Calculate the probability of each anchor selling each type of product based on the product information and the anchor's historical sales records. This probability is used as P(B|A) in Bayesian. The specific process includes:

[0063] Obtain information on products to be sold and the sales records of each anchor to be assigned;

[0064] Extract the sales records of each anchor in the past month, and calculate the probability of the anchor selling each type of product for sale in the past month based on the sales records in this period;

[0065] Extract the sales records of each anchor from the past six months to the previous month, and calculate the probability of each anchor selling each type of product for sale in the past six months to the previous month based on the sales records within this period;

[0066] The probability of selling each type of product among the anchors to be assigned is obtained by weighted summing the probability of the past month and the probability of the past six months to the previous month. i |A j ),

[0067] Determine the probability P(B) that each anchor sells each type of product i |A j ) is greater than the first preset value. If less than, the value is set to the first preset value. If greater than or equal to, it remains unchanged.

[0068] The calculation formula is:

[0069] P1(B i |A j )=x 1ij / Y 1ij

[0070] P2(B i |A j )=x 2ij / Y 2ij

[0071] P(B i |A j )=αP1(B i |A j )+βP2(B i |A j )

[0072] Among them, x 1ij is the number of products of type i sold by anchor j during the first preset time period, x 2ij Y is the number of products of type i sold by anchor j during the second preset time period, 1ij Y is the number of products of type i assigned to anchor j in the first preset time period,2ij is the number of commodities of type i assigned to anchor j in the second preset time period, P1(B i |A j ) is the probability that anchor j sells product of type i within the first preset time period, P2(B i |A j ) is the probability that anchor j sells product of type i within the second preset time period, and α and β are weight values.

[0073] And α is greater than β, because the sales data of the past month can better reflect the real sales ability of the anchor. However, the sales data of the anchor in the past six months should also be taken into account. Through comprehensive consideration, the sales ability of the anchor is calculated.

[0074] Step 3: Based on the anchor’s historical sales record, number of fans, and live broadcast room turnover in the anchor information, a weighted calculation is performed to obtain the overall sales probability of each anchor. This probability is used as the prior probability P(A) of event A in the Bayesian formula.

[0075] The calculation formula is:

[0076] E i =aS j +bF j +cW j

[0077]

[0078] Among them, S j is the sales volume of all products in the first preset time period of anchor j, F j is the number of fans of anchor j, W j is the live streaming volume of anchor j in the first preset time period, P(A j ) is the overall sales probability of anchor j, n is the number of anchors to be assigned, and a, b, and c are weight values.

[0079] Because the prior probability P(A) of event A in the Bayesian formula refers to our initial estimate of the probability of an event or hypothesis before considering any new evidence, the total sales volume, total live broadcast flow, and number of fans of the anchor in the past month are comprehensively considered, and this data is used as the overall sales probability of the anchor and as the prior probability of the Bayesian formula.

[0080] Step 4: Calculate the overall sales probability of each product through the historical sales records of each type of product, and use this probability as the prior probability P(B) of event B in the Bayesian formula.

[0081] The calculation formula is:

[0082] P(B i )=x i / Yi i

[0083] Among them, x i Y is the historical sales quantity of product type i, i is the total number of historical items of type i, P(B i ) is the overall sales probability of product type i.

[0084] Step 5: Based on the three probabilities calculated above, the probability of each anchor being assigned to each type of product is calculated using the Bayesian algorithm. Specifically, based on the three probabilities calculated above, the initial probability of each anchor being assigned to each type of product is calculated using the Bayesian algorithm, and the probability of each anchor is normalized to obtain the final probability. The calculation formula is:

[0085]

[0086] Among them, P(A j |B i ) is the probability that anchor j is assigned to product type i,

[0087] Step 6: According to the probability of each anchor being assigned to each type of product, a random number interval corresponding to its probability is assigned to each anchor. Specifically, the probability of anchor j being assigned to product type i is P(A j |B i ), then the random number interval of anchor j in product type i is

[0088] For each product, a corresponding random number is generated. The random number interval in which the random number falls is determined, and the product is assigned to the corresponding anchor in that random number interval. The generated random number range is [0,100].

[0089] This embodiment also provides a Bayesian-based anchor product distribution system, including:

[0090] The data acquisition module is used to obtain information about the products to be sold and the anchor information to be assigned;

[0091] The host allocation probability calculation module is used to calculate the probability of each host being assigned to each type of product;

[0092] The product allocation module allocates each product using random numbers based on the probability of each anchor being assigned to each type of product.

[0093] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0094] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and such modifications or substitutions are intended to be within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be subject to the scope of protection of the claims.

Claims

1. A Bayesian-based anchor product allocation method, characterized in that: The following steps are involved: Obtain information on products to be sold and information on hosts to be assigned. The product information includes product type, quantity, and historical sales records for each type of product. The host information includes the number of hosts to be assigned, their historical sales records, number of followers, and livestream room turnover. Calculate the probability of each anchor selling each type of product based on product information and the anchor's historical sales records; The overall sales probability of each anchor is obtained through weighted calculation based on the anchor's historical sales record, number of fans, and live broadcast room turnover in the anchor's information; The overall sales probability of each product category is calculated based on the historical sales records of each type of product; Based on the three probabilities calculated above, the probability of each anchor being assigned to each type of product is calculated using the Bayesian algorithm; According to the probability of each anchor being assigned to each type of product, a random number interval corresponding to its probability is assigned to each anchor; For each product, a corresponding random number is generated, and the random number interval in which the random number falls is determined. The product is allocated to the anchor corresponding to the random number interval. If the allocated product of the corresponding anchor is equal to the second preset value, a random number is re-generated for the product and reallocated.

2. A Bayesian-based anchor product allocation method according to claim 1, characterized in that: The commodities are classified according to commodity category, brand, price and quality.

3. A Bayesian-based anchor product allocation method according to claim 1, characterized in that: The method of calculating the probability of each anchor selling each type of commodity among the anchors to be assigned based on the anchors' historical sales records specifically includes the following steps: Obtain information on products to be sold and the sales records of each anchor to be assigned; Extracting the sales records of each anchor in the first preset time period, and calculating the probability of each anchor selling each type of product to be sold in the first preset time period based on the sales records in the first preset time period; Extracting the sales records of each anchor in the second preset time period, and calculating the probability of each anchor selling each type of goods to be sold in the second preset time period based on the sales records in the second preset time period; The probability of each anchor selling each type of product is obtained by weighting the probability of the first preset time period and the probability of the second preset time period. , Determine the probability of each anchor selling each type of product Is it greater than the first preset value? If it is less than, the probability Set to the first preset value. If it is greater than or equal to the preset value, it remains unchanged.

4. A Bayesian-based anchor product allocation method according to claim 3, characterized in that: The probability of each anchor selling each type of product among the anchors to be assigned is calculated based on the anchor's historical sales records. The calculation formula is: in, For the anchor in the first preset time period Sold type is the quantity of goods, For the anchor in the second preset time period Sold type is the quantity of goods, Assigned to the anchor in the first preset time period The type is Quantity of goods, Assigned to the anchor in the second preset time period The type is Quantity of goods, For the anchor in the first preset time period Sold type is The probability of the product, For the anchor in the second preset time period Sold type is The probability of the product, is the weight value.

5. A Bayesian-based anchor product allocation method according to claim 3 or 4, characterized in that: The first preset time period is about one month, the second preset time period is about half a year to about one month, Greater than .

6. A Bayesian-based anchor product allocation method according to claim 1, characterized in that: The overall sales probability of each anchor is obtained by weighted calculation based on the anchor's historical sales record, number of fans, and live broadcast room turnover in the anchor information. The calculation formula is: in, For anchors The sales volume of all products within the first preset time period, For anchors The number of fans, For anchors The live streaming volume within the first preset time period, For anchors The overall probability of selling, is the number of anchors to be assigned, is the weight value.

7. A Bayesian-based anchor product allocation method according to claim 1, characterized in that: The overall sales probability of each commodity is calculated by the historical sales records of each type of commodity, and the calculation formula is: in, For type The historical sales quantity of the product, For type Total number of product history, For type The overall probability of selling the product.

8. The Bayesian-based anchor product allocation method according to claim 1 is characterized in that: The three probabilities obtained from the above calculations are used to calculate the probability of each anchor being assigned to each type of product using the Bayesian algorithm. Specifically, the three probabilities obtained from the above calculations are used to calculate the initial probability of each anchor being assigned to each type of product using the Bayesian algorithm. The probabilities of each anchor are normalized to obtain the final probability. The calculation formula is: in, For anchors Assigned to type The probability of the product, , Indicates anchor Assigned to type The initial probability of the product.

9. A Bayesian-based anchor product allocation method according to claim 8, characterized in that: According to the probability of each anchor being assigned to each type of product, a random number interval corresponding to its probability is assigned to each anchor. Assigned to type The probability of a product is , then the anchor In the type The random number interval of the product is , a corresponding random number is generated, and the range of the random number is [0,100].

10. A system for the Bayesian-based anchor product allocation method according to any one of claims 1 to 9, characterized in that: include: The data acquisition module is used to obtain information about the products to be sold and the anchor information to be assigned; The host allocation probability calculation module is used to calculate the probability of each host being assigned to each type of product; The product allocation module allocates each product using random numbers based on the probability of each anchor being assigned to each type of product.

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