CN110827101A - Shop recommendation method and device - Google Patents
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- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0631—Item recommendations
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- G—PHYSICS
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G06Q30/06—Buying, selling or leasing transactions
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Abstract
The invention discloses a shop recommendation method and device, and relates to the technical field of computers. One embodiment of the method comprises: determining a value of a recommended dimension between the target store and the store to be compared; the recommended dimensions include at least one of: browsing flow dimension, item similarity dimension, volume dimension and credible identification; and determining the score of the shop to be compared according to the value of the recommendation dimension based on a preset scoring model, and recommending the shop to be compared to the target shop according to the sequence of the scores from high to low. According to the embodiment, multiple dimensions such as user browsing behaviors, in-store articles, store sales and the like can be comprehensively considered, the stores to be compared are scored, and then the competitive stores can be accurately and flexibly recommended to the target stores.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a shop recommendation method and device.
Background
In order to keep the own store competitive, it is necessary to know a competitive store similar to the own store in the market. The prior art recommendation method for a bidding store generally includes: searching related articles on the e-commerce platform, and recommending the stores where the articles at the front in the search results are located as competitive stores; or recommending an auction according to the offline sales data; or determining the competitive store to recommend according to the operation experience.
In the process of implementing the invention, the inventor finds that at least the following problems exist in the prior art:
1) for the method for searching for the determined competitive store of the articles on the E-commerce platform, the determined competitive store result is inaccurate and incomplete due to limited articles which can be searched;
2) for the method of recommending the competitive bidding according to the off-line sales data, the determined competitive bidding result is not accurate because the off-line sales data cannot replace the on-line sales data as a reference;
3) for the method for determining the store competition to recommend according to the operation experience, the determined store competition result is also easy to be inaccurate due to uncertainty and subjectivity of the operation experience.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method and an apparatus for recommending stores, which can comprehensively consider multiple dimensions, such as user browsing behaviors, in-store articles, store sales, and the like, to score stores to be compared, and further accurately and flexibly recommend a store competition for a target store.
To achieve the above object, according to an aspect of an embodiment of the present invention, there is provided a method of store recommendation, including:
determining a value of a recommended dimension between the target store and the store to be compared; the recommended dimensions include at least one of: browsing flow dimension, item similarity dimension, volume dimension and credible identification;
and determining the score of the shop to be compared according to the value of the recommendation dimension based on a preset scoring model, and recommending the shop to be compared to the target shop according to the sequence of the scores from high to low.
Optionally, a shop containing a target category is taken as the shop to be compared; the object class is determined as follows:
mainC＝{Ti|Tsalesi-1＜Tsales·λ≤Tsalesi，i≥2}∪{T1}，0＜λ≤1；
wherein, mainC represents a set of target categories; t isiIndicating the first i article categories after the sales of all the article categories are sorted from large to small in a preset time period in the target shop; tsalesiIndicating the sum of sales of the first i article categories after the sales of all the article categories are sorted from large to small in a preset time period in the target shop; tsales represents the total sales amount of the target store within a preset time period, and λ represents a category selection threshold.
Optionally, determining a value of a browsing traffic dimension between the target store and the store to be compared includes:
determining the skipping probability from the target store to the store to be compared based on the user browsing record;
and taking the skip probability as the numerical value of the browsing flow dimension between the target store and the store to be compared.
Optionally, determining the jump probability from the target store to the store to be compared based on the user browsing record comprises:
determining a user set M to be counted based on a user browsing record in a preset historical time period or a preset time period ending to the current moment, wherein the user to be counted is related to browsing information of object categories in a target category set menC to a target store and a store to be compared;
according to the browsing information of the user to be counted on the shops of the target category, determining a set S formed by the mth user to be counted in the M and the shops browsed by the mth target category in the main Cmc；
For set SmkThe internal elements establish a mapping relation between every two elements, wherein
According to set SmcThe internal mapping relation is that the transfer probability a from the target store to the jth store to be compared is determined by the following formulaj：
Wherein n represents the set SmcThe sum of the number of mapping relations from the target shop; n isjRepresentation set SmcThe sum of the number of mapping relations from the target store to the jth store to be compared; the | symbol represents the number of elements contained in the set in the | symbol.
Optionally, determining a value of an item similarity dimension between the target store and the store to be compared comprises:
determining vectors of all articles in the target shop and the shop to be compared based on a word frequency-inverse text frequency index algorithm;
determining the objects similar to the objects in the stores to be compared in the target stores according to the vectors of the objects in the target stores and the stores to be compared;
to be provided with
A numerical value as an item similarity dimension between the target store and the store to be compared; wherein sim _ sku represents a set of similar items in the target store to the items in the store to be compared, and Tsku represents a set of all items in the target store; the | symbol represents the number of elements contained in the set in the | symbol.
Optionally, the items in the target store that are similar to the items in the store to be compared are determined as follows:
sim_sku＝
{Tskua|(Tskua∈Tsku)∧(Cskub∈Csku)∧(cos(va，vb)＞β)}；
wherein TskuaRepresenting the a-th item in Tsku, Csku representing the set of all items in the stores to be compared, CskubRepresents the b-th item in Csku; v. ofaRepresents the direction of the a-th item in TskuAmount, vbA vector representing the b-th item in Csku; cos (v)a，vb) Denotes vaAnd vbβ denotes the similarity threshold.
Optionally, determining a value of the volume dimension between the target store and the store to be compared comprises:
acquiring a difference value of the total sales volume of the target shop and the shop to be compared in a preset time period;
to be provided with
A value as a dimension of the amount of the object between the target store and the kth store to be compared;
wherein D iskRepresenting the difference value of the total sales volume of the target shop and the kth shop to be compared in a preset time period; dmaxThe largest value among the differences in the total sales of the target store and all stores to be compared within the preset time period is represented.
Optionally, determining a trusted identity between the targeted store and the store to be compared comprises:
acquiring the total sale amount Tsales of the target stores in a preset time period and the total sale amount Csales of stores to be compared;
when Csales belongs to [ Tdown, Tup ], determining that the credible identification is 1;
when in use
Determining that the trusted identification is 0;
wherein Tup is Tsales ηup，ηup＞1；
Formula (III) ηupDenotes the upper limit factor, ηdown1Denotes a first lower limit factor, ηdown2Represents a second lower limit coefficient and mu represents a sales threshold.
According to still another aspect of an embodiment of the present invention, there is provided an apparatus for store recommendation, including:
the dimension determining value module is used for determining the value of the recommended dimension between the target shop and the shop to be compared; the recommended dimensions include at least one of: browsing flow dimension, item similarity dimension, volume dimension and credible identification;
and the recommending module is used for determining the score of the shop to be compared according to the numerical value of the recommended dimension based on a preset scoring model, and recommending the shop to be compared to the target shop according to the sequence of the scores from high to low.
Optionally, a shop containing a target category is taken as the shop to be compared; the dimension value determining module determines the target category according to the following method:
mainC＝{Ti|Tsalesi-1＜Tsales·λ≤Tsalesi，i≥2}∪{T1}，0＜λ≤1；
wherein, mainC represents a set of target categories; t isiIndicating the first i article categories after the sales of all the article categories are sorted from large to small in a preset time period in the target shop; tsalesiIndicating the sum of sales of the first i article categories after the sales of all the article categories are sorted from large to small in a preset time period in the target shop; tsales represents the total sales amount of the target store within a preset time period, and λ represents a category selection threshold.
Optionally, the determining a value of the browsing traffic dimension between the target store and the store to be compared by the determining a value of the dimension value module includes:
determining the skipping probability from the target store to the store to be compared based on the user browsing record;
and taking the skip probability as the numerical value of the browsing flow dimension between the target store and the store to be compared.
Optionally, the determining the dimension value module determines a probability of jumping from the target store to the store to be compared based on a user browsing record, including:
determining a user set M to be counted based on a user browsing record in a preset historical time period or a preset time period ending to the current moment, wherein the user to be counted is related to browsing information of object categories in a target category set menC to a target store and a store to be compared;
according to the browsing information of the user to be counted on the shops of the target category, determining a set S formed by the mth user to be counted in the M and the shops browsed by the mth target category in the main Cmc；
For set SmkThe internal elements establish a mapping relation between every two elements, wherein
According to set SmcThe internal mapping relation is that the transfer probability a from the target store to the jth store to be compared is determined by the following formulaj：
Wherein n represents the set SmcThe sum of the number of mapping relations from the target shop; n isjRepresentation set SmcThe sum of the number of mapping relations from the target store to the jth store to be compared; the | symbol represents the number of elements contained in the set in the | symbol.
Optionally, the determining a dimension value module determines a value of an item similarity dimension between the target store and the store to be compared, including:
determining vectors of all articles in the target shop and the shop to be compared based on a word frequency-inverse text frequency index algorithm;
determining the objects similar to the objects in the stores to be compared in the target stores according to the vectors of the objects in the target stores and the stores to be compared;
to be provided with
As a targeted shopA value of an item similarity dimension with the store to be compared; wherein sim _ sku represents a set of similar items in the target store to the items in the store to be compared, and Tsku represents a set of all items in the target store; the | symbol represents the number of elements contained in the set in the | symbol.
Optionally, the dimension-value determining module determines the items similar to the items in the store to be compared in the target store according to the following method:
sim_sku＝
{Tskua|(Tskua∈Tsku)∧(Cskub∈Csku)∧(cos(va，vb)＞β)}；
wherein TskuaRepresenting the a-th item in Tsku, Csku representing the set of all items in the stores to be compared, CskubRepresents the b-th item in Csku; v. ofaVector, v, representing the a-th item in TskubA vector representing the b-th item in Csku; cos (v)a，vb) Denotes vaAnd vbβ denotes the similarity threshold.
Optionally, the determining dimensions value module determines a value of a massing dimension between the target store and the store to be compared, including:
acquiring a difference value of the total sales volume of the target shop and the shop to be compared in a preset time period;
to be provided with
A value as a dimension of the amount of the object between the target store and the kth store to be compared;
wherein D iskRepresenting the difference value of the total sales volume of the target shop and the kth shop to be compared in a preset time period; dmaxThe largest value among the differences in the total sales of the target store and all stores to be compared within the preset time period is represented.
Optionally, the determining dimension value module determines a trusted identifier between the target store and the store to be compared, including:
acquiring the total sale amount Tsales of the target stores in a preset time period and the total sale amount Csales of stores to be compared;
when Csales belongs to [ Tdown, Tup ], determining that the credible identification is 1;
when in useDetermining that the trusted identification is 0;
wherein Tup is Tsales ηup，ηup＞1；
Formula (III) ηupDenotes the upper limit factor, ηdown1Denotes a first lower limit factor, ηdown2Represents a second lower limit coefficient and mu represents a sales threshold.
According to another aspect of the embodiments of the present invention, there is provided an electronic device recommended by a shop, including:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method of store recommendation provided by the present invention.
According to still another aspect of an embodiment of the present invention, there is provided a computer-readable medium having stored thereon a computer program which, when executed by a processor, implements the method for store recommendation provided by the present invention.
One embodiment of the above invention has the following advantages or benefits: the technical means that the browsing flow dimension, the article similarity dimension, the volume dimension and the value of the credible identification between the target store and the store to be compared are determined, then the stores to be compared are scored according to the dimensions based on the preset scoring model, and the stores with high scores are recommended to the target store is adopted, so that the technical problem that the stores cannot be accurately and comprehensively recommended in the prior art is solved, and the technical effect of accurately and flexibly recommending the competitive stores to the target store is achieved.
Further effects of the above-mentioned non-conventional alternatives will be described below in connection with the embodiments.
Drawings
The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:
FIG. 1 is a schematic diagram of a main flow of a method of store recommendation according to an embodiment of the invention;
FIG. 2 is a schematic diagram of a transition probability determination method according to an alternative embodiment of the invention;
FIG. 3 is a schematic diagram of the major modules of an apparatus for store recommendation according to an embodiment of the present invention;
FIG. 4 is an exemplary system architecture diagram in which embodiments of the present invention may be employed;
fig. 5 is a schematic block diagram of a computer system suitable for use in implementing a terminal device or server of an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present invention are described below with reference to the accompanying drawings, in which various details of embodiments of the invention are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Fig. 1 is a schematic diagram of a main flow of a method for store recommendation according to an embodiment of the present invention, as shown in fig. 1, including:
s101, determining a value of a recommended dimension between a target store and a store to be compared; the recommended dimensions include at least one of: browsing flow dimension, item similarity dimension, volume dimension and credible identification;
and S102, determining the score of the shop to be compared according to the value of the recommendation dimension based on a preset scoring model, and recommending the shop to be compared to the target shop according to the sequence of the score from high to low.
According to the scheme, multiple dimensions such as user browsing behaviors, in-store articles, store sales and the like can be comprehensively considered, the stores to be compared are scored, and then the competitive stores are accurately and flexibly recommended to the target stores.
In some embodiments, the stores containing the target category are taken as the stores to be compared; the object class is determined as follows:
mainC＝{Ti|Tsalesi-1＜Tsales·λ≤Tsalesi，i≥2}∪{T1}，0＜λ≤1；
wherein, mainC represents a set of target categories; t isiIndicating the first i article categories after the sales of all the article categories are sorted from large to small in a preset time period in the target shop; tsalesiIndicating the sum of sales of the first i article categories after the sales of all the article categories are sorted from large to small in a preset time period in the target shop; tsales represents the total sales amount of the target store within a preset time period, and λ represents a category selection threshold.
The preset time period is adjustable, such as being set to be the last 30 days or three months;
the category selection threshold λ is adjustable, such as may be set to 0.3, 0.6, 0.8; for the formula, the larger the set lambda value is, the more the determined target types are, and the lambda value can be flexibly adjusted in practical application to adapt to various requirements.
The method of determining the object class in the present invention is illustrated below:
for the targeted store, the sales for all item categories determined within its last three months are shown in the following table:
TABLE 1 sales of all item categories in three months in the target store
When λ is set to 0.6, it can be determined that i is 2 according to the formula in the present method, so it can be determined that category 1 and category 2 are target categories of the target store.
When λ is set to 0.3, it can be determined that i is 1 according to the formula in the present method, so it can be determined that category 1 is the target category of the target store.
When λ is set to 0.9, it can be determined that i is 4 according to the formula in the present method, so it can be determined that category 1, category 2, category 3, and category 4 are target categories of the target store.
In some embodiments, determining a value for a browsing traffic dimension between the target store and the store to be compared comprises:
determining the skipping probability from the target store to the store to be compared based on the user browsing record;
and taking the skip probability as the numerical value of the browsing flow dimension between the target store and the store to be compared.
The user browsing record can be a record in a preset historical time period or a preset time period ending to the current moment;
in addition, the transition probability matrix of the plurality of shops can be determined in a mode of determining the transition probability from any shop to another shop based on the user browsing record in the preset historical time period, so that the effect of inquiring the transition probability from any shop to other shops in the plurality of shops according to the transition probability matrix is achieved;
the browsing flow dimension value is determined based on the user browsing record, the significance of the method is that the competitive store relation is measured by using the behavior of the user, and the method is an important reference basis for recommending stores.
In some embodiments, determining the probability of jumping from the target store to the store to be compared based on the user browsing records comprises:
determining a user set M to be counted based on a user browsing record in a preset historical time period or a preset time period ending to the current moment, wherein the user to be counted is related to browsing information of object categories in a target category set menC to a target store and a store to be compared;
according to the browsing information of the user to be counted on the shops of the target category, determining a set S formed by the mth user to be counted in the M and the shops browsed by the mth target category in the main Cmc；
For set SmkThe internal elements establish a mapping relation between every two elements, wherein
According to set SmcThe internal mapping relation is that the transfer probability a from the target store to the jth store to be compared is determined by the following formulaj：
Wherein n represents the set SmcThe sum of the number of mapping relations from the target shop; n isjRepresentation set SmcThe sum of the number of mapping relations from the target store to the jth store to be compared; the | symbol represents the number of elements contained in the set in the | symbol.
The significance of determining the transition probability is that the jumping behavior of the user between the target store and the store to be compared can be quantified based on the behavior of the user, so that the quantified result can be used as a measure for the recommendation of the store.
To facilitate understanding of the method for determining transition probability in the embodiments of the present invention, fig. 2 is a schematic diagram of the method for determining transition probability in the alternative embodiments of the present invention; in fig. 2, store a is a targeted store; store B, C, D, E is the store to be compared;
according to browsing information of the user to be counted about the shop where the object category in the target category set is located, five sets are determined, as shown in fig. 2: set 201, set 202, set 203, set 204, set 205; wherein there is a store A, B in set 201, a store A, B, C in set 202, a store A, B, D in set 203, a store A, B, D, E in set 204, and a store A, B, E in set 205;
as shown in fig. 2, for the five sets, mapping relationships inside the sets are respectively established;
according to the mapping relations, the number of the mapping relations starting from the shop A is 10, the total number of the mapping relations from the shop A to the shop B is 5, the total number of the mapping relations from the shop A to the shop C is 1, the total number of the mapping relations from the shop A to the shop D is 2, and the total number of the mapping relations from the shop A to the shop E is 2;
and then determining that the transition probability from the shop A to the shop B is
The transition probability from store A to store C is
The transition probability from store A to store C is
The transition probability from store A to store D is
In some embodiments, determining a value for an item similarity dimension between the target store and the store to be compared comprises:
determining vectors of all articles in the target shop and the shop to be compared based on a word frequency-inverse text frequency index algorithm;
determining the objects similar to the objects in the stores to be compared in the target stores according to the vectors of the objects in the target stores and the stores to be compared;
to be provided with
As a targeted shopA value of an item similarity dimension with the store to be compared; wherein sim _ sku represents a set of similar items in the target store to the items in the store to be compared, and Tsku represents a set of all items in the target store; the | symbol represents the number of elements contained in the set in the | symbol.
A term frequency-inverse text frequency index algorithm (term frequency-inverse document frequency) is a common weighting technique used for information retrieval and data mining;
the names of the objects sold in the target store and all stores to be compared can be obtained first, all nouns are subjected to word segmentation processing, and a word set formed by all words is obtained; determining the occurrence frequency of each word in the word set in the shop by using a word frequency-inverse text frequency index algorithm; and for each article in each store, arranging the frequency of each term in the article name according to the term sequence in the term set to obtain the vector of each article in the store.
The following illustrates a method for determining vectors of various articles in the target shop and the shop to be compared by applying the algorithm in the embodiment of the present invention:
obtain the object A in the A shop1、A1In store B, there is an article B1、B2(ii) a After word segmentation processing, A can be determined1By the word C1、C2Constitution A2By the word C1、C3Constitution B1By the word C2、C4Constitution B2By the word C3、C5Forming;
further, it can be determined that there are words in the word set: c1、C2、C3、C4、C5；
In store A, word C1Frequency of occurrence of 0.5, C2Frequency of occurrence of 0.25, C3The frequency of occurrence was 0.25; according to the words C1、C2、C3、C4、C5Can determine the order of the article A in the store A1Has a vector of (0.5, 0.25, 0, 0, 0), article A2The vector of (0.5, 0, 0.25,0，0)；
in store B, C2Frequency of occurrence of 0.25, C3Frequency of occurrence of 0.25, C4Frequency of occurrence of 0.25, C5The frequency of occurrence was 0.25; according to the words C1、C2、C3、C4、C5Can determine the order of the articles B in the store B1The vector of (2) is (0, 0.25, 0, 0.25, 0), article B2The vector of (c) is (0, 0, 0.25, 0, 0.25).
The similarity of the objects in the shops is measured by using a natural language processing method, so that the similarity between the shops is determined, and the measuring of the competitive store relationship is an important reference basis for recommending the shops.
In some embodiments, items in the target store that are similar to items in the store to be compared are determined as follows:
sim_sku＝
{Tskua|(Tskua∈Tsku)∧(Cskub∈Csku)∧(cos(va，vb)＞β)}；
wherein TskuaRepresenting the a-th item in Tsku, Csku representing the set of all items in the stores to be compared, CskubRepresents the b-th item in Csku; v. ofaVector, v, representing the a-th item in TskubA vector representing the b-th item in Csku; cos (v)a，vb) Denotes vaAnd vbβ denotes the similarity threshold.
The similarity threshold β is flexibly adjusted and set according to actual requirements, and in this embodiment, the similarity between vectors is measured by using a cosine similarity algorithm, so that the objects in the target store similar to the objects in the store to be compared can be determined, and the method is easy to implement.
In some embodiments, determining the value of the volume dimension between the target store and the store to be compared comprises:
acquiring a difference value of the total sales volume of the target shop and the shop to be compared in a preset time period;
to be provided with
A value as a dimension of the amount of the object between the target store and the kth store to be compared;
wherein D iskRepresenting the difference value of the total sales volume of the target shop and the kth shop to be compared in a preset time period; dmaxThe largest value among the differences in the total sales of the target store and all stores to be compared within the preset time period is represented.
The preset time period can be set in an adjustable way, such as the last three months or the last half year;
the actual difference of the total sales of the target store and the store to be compared is quantified by using logarithmic operation, so that the huge sales difference in some cases can be stabilized, and calculation is easy.
In some embodiments, determining a trusted identity between the targeted store and the store to be compared comprises:
acquiring the total sale amount Tsales of the target stores in a preset time period and the total sale amount Csales of stores to be compared;
when Csales belongs to [ Tdown, Tup ], determining that the credible identification is 1;
when in use
Determining that the trusted identification is 0;
wherein Tup is Tsales ηup，ηup＞1；
Formula (III) ηupDenotes the upper limit factor, ηdown1Denotes a first lower limit factor, ηdown2Represents a second lower limit coefficient and mu represents a sales threshold.
The upper limit coefficient, the first lower limit coefficient, the second lower limit coefficient and the sales threshold value are all adjustably set; the significance of determining the trusted identity is to mark whether two stores are in a trusted competitive relationship: if the sales difference between the target store and the store to be compared is too large, the target store and the store to be compared can be considered to have no competitive relationship;
in the formula in this embodiment, two determination methods under different conditions are set for the lower limit value Tdown in the trusted interval [ Tdown, Tup ], which is more practical.
For the determined dimension values of the browsing flow dimension, the item similarity dimension, the volume dimension and the credible identification, the score of the shop to be compared can be determined based on the following preset scoring models:
TABLE 2 Preset scoring model
According to the dimension fusion setting, a scoring model shown in table 2 can be determined, a plurality of dimensions can be comprehensively considered to score stores to be compared, and finally stores with higher scores are recommended to the target stores as competitive stores.
Fig. 3 is a schematic diagram of main modules of an apparatus for store recommendation according to an embodiment of the present invention, and as shown in fig. 3, an apparatus 300 for store recommendation includes:
a dimension determining value module 301, configured to determine a value of a recommended dimension between the target store and the store to be compared; the recommended dimensions include at least one of: browsing flow dimension, item similarity dimension, volume dimension and credible identification;
and the recommending module 302 is configured to determine a score of the store to be compared according to the value of the recommendation dimension based on a preset scoring model, and recommend the store to be compared to the target store according to the sequence of the scores from high to low.
According to the scheme, multiple dimensions such as user browsing behaviors, in-store articles, store sales and the like can be comprehensively considered, the stores to be compared are scored, and then the competitive stores are accurately and flexibly recommended to the target stores.
In some embodiments, the stores containing the target category are taken as the stores to be compared; the dimension value determining module 301 determines the object class as follows:
mainC＝{Ti|Tsalesi-1＜Tsales·λ≤Tsalesi，i≥2}∪{T1}，0＜λ≤1；
wherein, mainC represents a set of target categories; t isiIndicating the first i article categories after the sales of all the article categories are sorted from large to small in a preset time period in the target shop; tsalesiIndicating the sum of sales of the first i article categories after the sales of all the article categories are sorted from large to small in a preset time period in the target shop; tsales represents the total sales amount of the target store within a preset time period, and λ represents a category selection threshold.
The preset time period is adjustable, such as being set to be the last 30 days or three months;
the category selection threshold λ is adjustable, such as may be set to 0.3, 0.6, 0.8; for the formula, the larger the set lambda value is, the more the determined target types are, and the lambda value can be flexibly adjusted in practical application to adapt to various requirements.
In some embodiments, the determine dimension value module 301 determines the value of the browsing traffic dimension between the target store and the store to be compared, including:
determining the skipping probability from the target store to the store to be compared based on the user browsing record;
and taking the skip probability as the numerical value of the browsing flow dimension between the target store and the store to be compared.
The user browsing record can be a record in a preset historical time period or a preset time period ending to the current moment;
in addition, the transition probability matrix of the plurality of shops can be determined in a mode of determining the transition probability from any shop to another shop based on the user browsing record in the preset historical time period, so that the effect of inquiring the transition probability from any shop to other shops in the plurality of shops according to the transition probability matrix is achieved;
the browsing flow dimension value is determined based on the user browsing record, the significance of the method is that the competitive store relation is measured by using the behavior of the user, and the method is an important reference basis for recommending stores.
In some embodiments, the module 301 for determining a dimension value determines the probability of jumping from the target store to the store to be compared based on the user browsing record, including:
determining a user set M to be counted based on a user browsing record in a preset historical time period or a preset time period ending to the current moment, wherein the user to be counted is related to browsing information of object categories in a target category set menC to a target store and a store to be compared;
according to the browsing information of the user to be counted on the shops of the target category, determining a set S formed by the mth user to be counted in the M and the shops browsed by the mth target category in the main Cmc；
For set SmkThe internal elements establish a mapping relation between every two elements, wherein
According to set SmcThe internal mapping relation is that the transfer probability a from the target store to the jth store to be compared is determined by the following formulaj：
Wherein n represents the set SmcThe sum of the number of mapping relations from the target shop; n isjRepresentation set SmcThe sum of the number of mapping relations from the target store to the jth store to be compared; the | symbol represents the number of elements contained in the set in the | symbol.
The significance of determining the transition probability is that the jumping behavior of the user between the target store and the store to be compared can be quantified based on the behavior of the user, so that the quantified result can be used as a measure for the recommendation of the store.
In some embodiments, the determine dimension value module 301 determines the value of the item similarity dimension between the target store and the store to be compared, including:
determining vectors of all articles in the target shop and the shop to be compared based on a word frequency-inverse text frequency index algorithm;
determining the objects similar to the objects in the stores to be compared in the target stores according to the vectors of the objects in the target stores and the stores to be compared;
to be provided withA numerical value as an item similarity dimension between the target store and the store to be compared; wherein sim _ sku represents a set of similar items in the target store to the items in the store to be compared, and Tsku represents a set of all items in the target store; the | symbol represents the number of elements contained in the set in the | symbol.
A term frequency-inverse text frequency index algorithm (term frequency-inverse document frequency) is a common weighting technique used for information retrieval and data mining;
the names of the objects sold in the target store and all stores to be compared can be obtained first, all nouns are subjected to word segmentation processing, and a word set formed by all words is obtained; determining the occurrence frequency of each word in the word set in the shop by using a word frequency-inverse text frequency index algorithm; for each article in each store, arranging the frequency of each word in the article name according to the word sequence in the word set to obtain the vector of each article in the store;
the similarity of the objects in the shops is measured by using a natural language processing method, so that the similarity between the shops is determined, and the measuring of the competitive store relationship is an important reference basis for recommending the shops.
In some embodiments, the determine dimension value module 301 determines items similar to the items in the comparison store in the target store as follows:
sim_sku＝
{Tskua|(Tskua∈Tsku)∧(Cskub∈Csku)∧(cos(va，vb)＞β)}；
wherein TskuaRepresenting the a-th item in Tsku, Csku representing the set of all items in the stores to be compared, CskubRepresents the b-th item in Csku; v. ofaVector, v, representing the a-th item in TskubA vector representing the b-th item in Csku; cos (v)a，vb) Denotes vaAnd vbβ denotes the similarity threshold.
The similarity threshold β is flexibly adjusted and set according to actual requirements, and in this embodiment, the similarity between vectors is measured by using a cosine similarity algorithm, so that the objects in the target store similar to the objects in the store to be compared can be determined, and the method is easy to implement.
In some embodiments, the determine dimension value module 301 determines a value of a massing dimension between the target store and the stores to be compared, including:
acquiring a difference value of the total sales volume of the target shop and the shop to be compared in a preset time period;
to be provided with
A value as a dimension of the amount of the object between the target store and the kth store to be compared;
wherein D iskRepresenting the difference value of the total sales volume of the target shop and the kth shop to be compared in a preset time period; dmaxThe largest value among the differences in the total sales of the target store and all stores to be compared within the preset time period is represented.
The preset time period can be set in an adjustable way, such as the last three months or the last half year;
the actual difference of the total sales of the target store and the store to be compared is quantified by using logarithmic operation, so that the huge sales difference in some cases can be stabilized, and calculation is easy.
In some embodiments, the determine dimension value module 301 determines a trusted identification between the target store and the store to be compared, including:
acquiring the total sale amount Tsales of the target stores in a preset time period and the total sale amount Csales of stores to be compared;
when Csales belongs to [ Tdown, Tup ], determining that the credible identification is 1;
when in use
Determining that the trusted identification is 0;
wherein Tup is Tsales ηup，ηup＞1；
Formula (III) ηupDenotes the upper limit factor, ηdown1Denotes a first lower limit factor, ηdown2Represents a second lower limit coefficient and mu represents a sales threshold.
The upper limit coefficient, the first lower limit coefficient, the second lower limit coefficient and the sales threshold value are all adjustably set; the significance of determining the trusted identity is to mark whether two stores are in a trusted competitive relationship: if the sales difference between the target store and the store to be compared is too large, the target store and the store to be compared can be considered to have no competitive relationship;
in the formula in this embodiment, two determination methods under different conditions are set for the lower limit value Tdown in the trusted interval [ Tdown, Tup ], which is more practical.
FIG. 4 illustrates an exemplary system architecture 400 of a method or apparatus for store recommendation to which embodiments of the present invention may be applied.
As shown in fig. 4, the system architecture 400 may include terminal devices 401, 402, 403, a network 404, and a server 405. The network 404 serves as a medium for providing communication links between the terminal devices 401, 402, 403 and the server 405. Network 404 may include various types of connections, such as wire, wireless communication links, or fiber optic cables, to name a few.
A user may use terminal devices 401, 402, 403 to interact with a server 405 over a network 404 to receive or send messages or the like. The terminal devices 401, 402, 403 may have various communication client applications installed thereon, such as shopping applications, web browser applications, search applications, instant messaging tools, mailbox clients, social platform software, and the like.
The terminal devices 401, 402, 403 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 405 may be a server providing various services, such as a background management server (for example only) providing support for shopping websites browsed by users using the terminal devices 401, 402, 403. The backend management server may analyze and perform other processing on the received data such as the product information query request, and feed back a processing result (for example, target push information, product information — just an example) to the terminal device.
It should be noted that the method for recommending a store according to the embodiment of the present invention is generally executed by the server 405, and accordingly, the apparatus for recommending a store is generally installed in the server 405.
It should be understood that the number of terminal devices, networks, and servers in fig. 4 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Referring now to FIG. 5, shown is a block diagram of a computer system 500 suitable for use with a terminal device implementing an embodiment of the present invention. The terminal device shown in fig. 5 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 5, the computer system 500 includes a Central Processing Unit (CPU)501 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)502 or a program loaded from a storage section 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data necessary for the operation of the system 500 are also stored. The CPU 501, ROM 502, and RAM 503 are connected to each other via a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
The following components are connected to the I/O interface 505: an input portion 506 including a keyboard, a mouse, and the like; an output portion 507 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage portion 508 including a hard disk and the like; and a communication section 509 including a network interface card such as a LAN card, a modem, or the like. The communication section 509 performs communication processing via a network such as the internet. The driver 510 is also connected to the I/O interface 505 as necessary. A removable medium 511 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 510 as necessary, so that a computer program read out therefrom is mounted into the storage section 508 as necessary.
In particular, according to the embodiments of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 509, and/or installed from the removable medium 511. The computer program performs the above-described functions defined in the system of the present invention when executed by the Central Processing Unit (CPU) 501.
It should be noted that the computer readable medium shown in the present invention can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules described in the embodiments of the present invention may be implemented by software or hardware. The described modules may also be provided in a processor, which may be described as: a processor comprises a dimension value determining module and a recommending unit. The names of these modules do not form a limitation on the unit itself in some cases, for example, the module for determining the dimension value may also be described as a "module for sending a picture acquisition request to a connected server".
As another aspect, the present invention also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be separate and not incorporated into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to comprise: s101, determining a value of a recommended dimension between a target store and a store to be compared; the recommended dimensions include at least one of: browsing flow dimension, item similarity dimension, volume dimension and credible identification; and S102, determining the score of the shop to be compared according to the value of the recommendation dimension based on a preset scoring model, and recommending the shop to be compared to the target shop according to the sequence of the score from high to low.
According to the technical scheme of the embodiment of the invention, the technical means that the browsing flow dimension, the article similarity dimension, the volume dimension and the value of the credible identification between the target store and the store to be compared are firstly determined, then the stores to be compared are scored according to the dimensions based on the preset scoring model, and then the stores with high scores are recommended to the target store is adopted, so that the technical problem that the stores cannot be accurately and comprehensively recommended in the prior art is solved, and the technical effect of accurately and flexibly recommending the competitive stores to the target store is achieved.
The above-described embodiments should not be construed as limiting the scope of the invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions can occur, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (18)
1. A method of store recommendation, comprising:
determining a value of a recommended dimension between the target store and the store to be compared; the recommended dimensions include at least one of: browsing flow dimension, item similarity dimension, volume dimension and credible identification;
and determining the score of the shop to be compared according to the value of the recommendation dimension based on a preset scoring model, and recommending the shop to be compared to the target shop according to the sequence of the scores from high to low.
2. The method according to claim 1, characterized in that stores containing target categories are used as stores to be compared; the object class is determined as follows:
mainC＝{Ti|Tsalesi-1＜Tsales·λ≤Tsalesi，i≥2}∪{T1}，0＜λ≤1；
wherein, mainC represents a set of target categories; t isiIndicating the first i article categories after the sales of all the article categories are sorted from large to small in a preset time period in the target shop; tsalesiIndicating the sum of sales of the first i article categories after the sales of all the article categories are sorted from large to small in a preset time period in the target shop; tsales represents the total sales amount of the target store within a preset time period, and λ represents a category selection threshold.
3. The method of claim 2, wherein determining a value for a browsing traffic dimension between the target store and the store to be compared comprises:
determining the skipping probability from the target store to the store to be compared based on the user browsing record;
and taking the skip probability as the numerical value of the browsing flow dimension between the target store and the store to be compared.
4. The method of claim 3, wherein determining the probability of jumping from the target store to the store to be compared based on the user browsing history comprises:
determining a user set M to be counted based on a user browsing record in a preset historical time period or a preset time period ending to the current moment, wherein the user to be counted is related to browsing information of object categories in a target category set menC to a target store and a store to be compared;
according to the browsing information of the user to be counted on the shops of the target category, determining a set S formed by the mth user to be counted in the M and the shops browsed by the mth target category in the main Cmc；
For set SmkThe internal elements establish a mapping relation between every two elements, wherein
According to set SmcThe internal mapping relation is that the transfer probability a from the target store to the jth store to be compared is determined by the following formulaj：
Wherein n represents the set SmcThe sum of the number of mapping relations from the target shop; n isjRepresentation set SmcThe sum of the number of mapping relations from the target store to the jth store to be compared; the | symbol represents the number of elements contained in the set in the | symbol.
5. The method of claim 2, wherein determining a value for an item similarity dimension between the target store and the store to be compared comprises:
determining vectors of all articles in the target shop and the shop to be compared based on a word frequency-inverse text frequency index algorithm;
determining the objects similar to the objects in the stores to be compared in the target stores according to the vectors of the objects in the target stores and the stores to be compared;
to be provided withA numerical value as an item similarity dimension between the target store and the store to be compared; wherein sim _ sku represents a set of similar items in the target store to the items in the store to be compared, and Tsku represents a set of all items in the target store; the | symbol represents the number of elements contained in the set in the | symbol.
6. The method of claim 5, wherein items in the target store that are similar to items in the store to be compared are determined as follows:
sim_sku＝
{Tskua|(Tskua∈Tsku)∧(Cskub∈Csku)∧(cos(va，vb)＞β)}；
wherein TskuaRepresenting the a-th item in Tsku, Csku representing the set of all items in the stores to be compared, CskubRepresents the b-th item in Csku; v. ofaVector, v, representing the a-th item in TskubA vector representing the b-th item in Csku; cos (v)a，vb) Denotes vaAnd vbβ denotes the similarity threshold.
7. The method of claim 2, wherein determining a value for a volume dimension between the target store and the store to be compared comprises:
acquiring a difference value of the total sales volume of the target shop and the shop to be compared in a preset time period;
to be provided with
A value as a dimension of the amount of the object between the target store and the kth store to be compared;
wherein D iskRepresenting the difference value of the total sales volume of the target shop and the kth shop to be compared in a preset time period; dmaxThe largest value among the differences in the total sales of the target store and all stores to be compared within the preset time period is represented.
8. The method of claim 2, wherein determining a trusted identity between the targeted store and the store to be compared comprises:
acquiring the total sale amount Tsales of the target stores in a preset time period and the total sale amount Csales of stores to be compared;
when Csales belongs to [ Tdown, Tup ], determining that the credible identification is 1;
when in use
Determining that the trusted identification is 0;
wherein Tup is Tsales ηup，ηup＞1；
Formula (III) ηupDenotes the upper limit factor, ηdown1Denotes a first lower limit factor, ηdown2Represents a second lower limit coefficient and mu represents a sales threshold.
9. An apparatus for store recommendation, comprising:
the dimension determining value module is used for determining the value of the recommended dimension between the target shop and the shop to be compared; the recommended dimensions include at least one of: browsing flow dimension, item similarity dimension, volume dimension and credible identification;
and the recommending module is used for determining the score of the shop to be compared according to the numerical value of the recommended dimension based on a preset scoring model, and recommending the shop to be compared to the target shop according to the sequence of the scores from high to low.
10. The apparatus according to claim 9, wherein a store containing a target category is taken as a store to be compared; the dimension value determining module determines the target category according to the following method:
mainC＝{Ti|Tsalesi-1＜Tsales·λ≤Tsalesi，i≥2}∪{T1}，0＜λ≤1；
wherein, mainC represents a set of target categories; t isiIndicating the first i article categories after the sales of all the article categories are sorted from large to small in a preset time period in the target shop; tsalesiIndicating the sum of sales of the first i article categories after the sales of all the article categories are sorted from large to small in a preset time period in the target shop; tsales represents the total sales amount of the target store within a preset time period, and λ represents a category selection threshold.
11. The apparatus of claim 10, wherein the determine dimension value module determines a value of a browsing traffic dimension between the target store and the store to be compared, comprising:
determining the skipping probability from the target store to the store to be compared based on the user browsing record;
and taking the skip probability as the numerical value of the browsing flow dimension between the target store and the store to be compared.
12. The apparatus of claim 11, wherein the determine dimension value module determines a probability of jumping from the target store to the store to be compared based on a user browsing record, comprising:
determining a user set M to be counted based on a user browsing record in a preset historical time period or a preset time period ending to the current moment, wherein the user to be counted is related to browsing information of object categories in a target category set menC to a target store and a store to be compared;
according to the browsing information of the user to be counted on the shops of the target category, determining a set S formed by the mth user to be counted in the M and the shops browsed by the mth target category in the main Cmc；
For set SmkThe internal elements establish a mapping relation between every two elements, wherein
According to set SmcThe internal mapping relation is that the transfer probability a from the target store to the jth store to be compared is determined by the following formulaj：
Wherein n represents the set SmcThe sum of the number of mapping relations from the target shop; n isjRepresentation set SmcThe sum of the number of mapping relations from the target store to the jth store to be compared; the | symbol represents the number of elements contained in the set in the | symbol.
13. The apparatus of claim 10, wherein the determine dimension value module determines a value of an item similarity dimension between the target store and the store to be compared, comprising:
determining vectors of all articles in the target shop and the shop to be compared based on a word frequency-inverse text frequency index algorithm;
determining the objects similar to the objects in the stores to be compared in the target stores according to the vectors of the objects in the target stores and the stores to be compared;
to be provided with
A numerical value as an item similarity dimension between the target store and the store to be compared; wherein sim _ sku represents a set of similar items in the target store to the items in the store to be compared, and Tsku represents a set of all items in the target store; the | symbol represents the number of elements contained in the set in the | symbol.
14. The apparatus of claim 13, wherein the determine dimensional value module determines items in the target store that are similar to items in the store to be compared as follows:
sim_sku＝
{Tskua|(Tskua∈Tsku)∧(Cskub∈Csku)∧(cos(va，vb)＞β)}；
wherein TskuaRepresenting the a-th item in Tsku, Csku representing the set of all items in the stores to be compared, CskubRepresents the b-th item in Csku; v. ofaVector, v, representing the a-th item in TskubA vector representing the b-th item in Csku; cos (v)a，vb) Denotes vaAnd vbβ denotes the similarity threshold.
15. The apparatus of claim 10, wherein the determine dimension value module determines a value of a massing dimension between a target store and a store to be compared, comprising:
acquiring a difference value of the total sales volume of the target shop and the shop to be compared in a preset time period;
to be provided with
A value as a dimension of the amount of the object between the target store and the kth store to be compared;
wherein D iskRepresenting the difference value of the total sales volume of the target shop and the kth shop to be compared in a preset time period; dmaxThe largest value among the differences in the total sales of the target store and all stores to be compared within the preset time period is represented.
16. The apparatus of claim 10, wherein the determine dimension value module determines a trusted identification between the target store and the store to be compared, comprising:
acquiring the total sale amount Tsales of the target stores in a preset time period and the total sale amount Csales of stores to be compared;
when Csales belongs to [ Tdown, Tup ], determining that the credible identification is 1;
when in useDetermining that the trusted identification is 0;
wherein Tup is Tsales ηup，ηup＞1；
Formula (III) ηupDenotes the upper limit factor, ηdown1Denotes a first lower limit factor, ηdown2Represents a second lower limit coefficient and mu represents a sales threshold.
17. An electronic device recommended by a shop, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-8.
18. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-8.
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| CN113763080A (en) * | 2020-08-26 | 2021-12-07 | 北京沃东天骏信息技术有限公司 | Method and device for determining recommended article, electronic equipment and storage medium |
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| JP4960526B1 (en) * | 2011-09-29 | 2012-06-27 | 株式会社ぐるなび | Store information provision system |
| CN105005579A (en) * | 2015-05-28 | 2015-10-28 | 携程计算机技术（上海）有限公司 | Personalized sorting method and system of hotel room types in OTA (Online Travel Agent) website |
| CN107851268A (en) * | 2015-07-24 | 2018-03-27 | 万事达卡国际股份有限公司 | For carrying out the method and system of ranking to businessman |
| CN105631717A (en) * | 2016-02-07 | 2016-06-01 | 徐璇炫 | Cost performance real-time bidding method and device |
| CN107247786A (en) * | 2017-06-15 | 2017-10-13 | 北京小度信息科技有限公司 | Method, device and server for determining similar users |
| CN107464037A (en) * | 2017-07-05 | 2017-12-12 | 九次方大数据信息集团有限公司 | Enterprise's portrait method and system based on multi objective dimensional model |
| CN107291939A (en) * | 2017-07-06 | 2017-10-24 | 携程计算机技术（上海）有限公司 | The clustering match method and system of hotel information |
| CN107730313A (en) * | 2017-09-30 | 2018-02-23 | 口碑(上海)信息技术有限公司 | Recommend method and device in shop based on rationale for the recommendation |
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| CN113763080A (en) * | 2020-08-26 | 2021-12-07 | 北京沃东天骏信息技术有限公司 | Method and device for determining recommended article, electronic equipment and storage medium |
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