CN111666487A - Trie tree-based commodity filtering method and device and computing equipment - Google Patents
Trie tree-based commodity filtering method and device and computing equipment Download PDFInfo
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- G06F16/90—Details of database functions independent of the retrieved data types
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- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0241—Advertisements
- G06Q30/0251—Targeted advertisements
- G06Q30/0269—Targeted advertisements based on user profile or attribute
Abstract
The invention provides a Trie tree-based commodity filtering method, a Trie tree-based commodity filtering device and computing equipment. The commodity filtering method based on the Trie tree comprises the following steps: inputting a query-commodity data pair obtained based on a user search term; filtering the input query-commodity data pairs by using a Trie tree of a filtering dictionary according to a specified filtering rule, wherein the Trie tree of the filtering dictionary is constructed by pressing each element in the filtering dictionary into a tree structure; and outputting the filtered result. According to the scheme, a Trie tree is constructed for each filtering dictionary, the violent retrieval mode in the prior art is replaced by the efficient index of the Trie tree, the dimensionality of the search diameter is reduced, the filtering efficiency can be obviously improved, the data production period is shortened, and the product iteration speed is increased. Especially, under the condition that the number of entries of the filtering dictionary is large (for example, tens of thousands, hundreds of thousands or even more), the times of filtering dictionary query can be greatly reduced, and the time and resource consumption of iteration are saved.
Description
Technical Field
The invention relates to the technical field of computational advertising, in particular to a Trie-tree-based commodity filtering method, a Trie-tree-based commodity filtering device, a computer storage medium and computing equipment.
Background
Computational advertisers are an emerging branch of disciplines that face the most major challenge of finding the "best match" between a particular user and the corresponding advertisement in a particular context, which can typically be the query terms entered by the user in a search engine. In the field of computing advertisements, a matching dictionary form is often used for mapping query to commodity title for user search words, so that an online recall process is simplified, and commodity recall efficiency is improved. For this reason, many matching dictionary models for offline mining of query-titles have been applied. For example, in a search engine, a plurality of product information provided by clients are stored, and in order to make the query of the user hit the desired product, mapping combinations from the query to the product or title are usually constructed offline at the bottom layer, and these combinations constitute an offline matching dictionary as the basis for online retrieval of the output advertisement.
However, no matter what matching dictionary model is based, the mined query-title is difficult to avoid the problems of error matching and the like, cannot be directly used, and needs to be filtered. In the prior art, a plurality of filtering dictionaries are usually established locally, and the contents of the filtering dictionaries are applied to filter the query-titles one by one. The traditional dictionary query-based mode has low efficiency under the conditions of high latitude of the dictionary and large candidate data, and causes the problems of long data production period and low product iteration speed.
Therefore, a method for improving the filtering efficiency, shortening the data production cycle, and increasing the product iteration speed is needed.
Disclosure of Invention
In view of the above, the present invention has been made to provide a Trie-tree based commodity filtering method, a Trie-tree based commodity filtering apparatus, a computer storage medium, and a computing device that overcome or at least partially solve the above-mentioned problems.
According to an aspect of the embodiments of the present invention, a method for filtering a commodity based on a Trie is provided, including:
inputting a query-commodity data pair obtained based on a user search term;
filtering the input query-commodity data pairs by using a Trie tree of a filtering dictionary according to a specified filtering rule, wherein the Trie tree of the filtering dictionary is constructed by pressing each element in the filtering dictionary into a tree structure;
and outputting the filtered result.
Optionally, pushing each element in the filter dictionary to a tree structure, comprising:
each element in the filter dictionary is pushed into the tree structure in such a way that any path from the root node to a leaf node represents an element.
Optionally, the specified filtering rules include at least one of:
whether the subject is matched, whether the region is accordant and whether sensitive words exist.
Optionally, filtering the input query-commodity data pairs by using a Trie of the filtering dictionary, including:
and searching the position of the element in the filtering dictionary and the weight of the element in the query-commodity data pair by using the Trie tree of the filtering dictionary, and filtering the query-commodity data pair according to the appearance position and the weight of the element.
Optionally, before filtering the input query-commodity data pairs using the Trie tree of the filtering dictionary, the method further comprises:
and selecting a Trie tree of the corresponding filtering dictionary according to the filtering condition of the query.
Optionally, the query-commodity data pairs are mined by a statistical model or a semantic relevance model.
According to another aspect of the embodiments of the present invention, there is also provided a Trie-based commodity filtering apparatus, including:
the input module is suitable for inputting query-commodity data pairs obtained based on the user search terms;
a filtering module adapted to filter the input query-commodity data pairs according to specified filtering rules using a Trie of a filtering dictionary, wherein the Trie of the filtering dictionary is constructed by pushing each element in the filtering dictionary into a tree structure; and
and the output module is suitable for outputting the filtered result.
Optionally, the Trie of the filter dictionary is constructed by pushing each element in the filter dictionary into a tree structure in such a way that any path from the root node to the leaf node represents an element.
Optionally, the specified filtering rules include at least one of:
whether the subject is matched, whether the region is accordant and whether sensitive words exist.
Optionally, the filtration module is further adapted to:
and searching the position of the element in the filtering dictionary and the weight of the element in the query-commodity data pair by using the Trie tree of the filtering dictionary, and filtering the query-commodity data pair according to the appearance position and the weight of the element.
Optionally, the apparatus further comprises:
and the selection module is suitable for selecting the Trie tree of the corresponding filtering dictionary according to the filtering condition of the query before the filtering module filters the input query-commodity data pair by utilizing the Trie tree of the filtering dictionary.
Optionally, the query-commodity data pairs are mined by a statistical model or a semantic relevance model.
According to yet another aspect of embodiments of the present invention, there is also provided a computer storage medium storing computer program code which, when run on a computing device, causes the computing device to perform the Trie-tree based commodity filtering method according to any one of the above.
According to still another aspect of the embodiments of the present invention, there is also provided a computing device including:
a processor; and
a memory storing computer program code;
the computer program code, when executed by the processor, causes the computing device to perform the Trie-tree based commodity filtering method according to any one of the preceding.
According to the Trie-tree-based commodity filtering method and device provided by the embodiment of the invention, a Trie tree is constructed for each filtering dictionary, and query-commodity data pairs obtained based on user search words are filtered by using the Trie trees of the filtering dictionaries according to the specified filtering rule, so that some mismatching or sensitive word matching is removed, and the commodity recall accuracy is ensured. According to the scheme, the violent retrieval mode in the prior art is replaced by the efficient index of the Trie tree, the dimensionality of the search diameter is reduced, the filtering efficiency can be obviously improved, the data production period is shortened, and the product iteration speed is increased. Especially, under the condition that the number of entries of the filtering dictionary is large (for example, tens of thousands, hundreds of thousands or even more), the times of filtering dictionary query can be greatly reduced, and the time and resource consumption of iteration are saved. The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.
The above and other objects, advantages and features of the present invention will become more apparent to those skilled in the art from the following detailed description of specific embodiments thereof, taken in conjunction with the accompanying drawings.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
FIG. 1 illustrates a flow diagram of a Trie-tree based commodity filtering method according to an embodiment of the present invention;
fig. 2 is a flow chart illustrating a Trie-based commodity filtering method according to another embodiment of the present invention;
fig. 3 is a schematic structural diagram of a Trie-based commodity filtering apparatus according to an embodiment of the present invention; and
fig. 4 is a schematic structural diagram of a Trie-based commodity filtering apparatus according to another embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
In the field of computing advertisements, certain post-processing is required to be performed by using rules to remove some mismatching or sensitive word matching by using an offline matching dictionary mined by a model. For this purpose, several filtering dictionaries are usually established locally, and the content of the filtering dictionary is applied to match and filter the similar query-titles of the matching dictionaries. However, the traditional method of performing query-by-query filtering based on a filtering dictionary is low in efficiency, time-consuming and large in calculation amount under the conditions of high latitude of the dictionary and large candidate data, and is extremely not favorable for resource saving.
In order to solve the above technical problem, an embodiment of the present invention provides a commercial product filtering method based on a Trie tree. Fig. 1 shows a flowchart of a Trie-based commodity filtering method according to an embodiment of the present invention. Referring to fig. 1, the method may include at least the following steps S102 to S106.
Step S102, inputting query-commodity data pairs obtained based on the user search terms.
Step S104, filtering the input query-commodity data pairs by utilizing the Trie trees of the filtering dictionaries according to the specified filtering rules, wherein the Trie tree of each filtering dictionary is constructed by pressing each element in the filtering dictionary into a tree structure.
And step S106, outputting the filtered result.
The Trie-tree-based commodity filtering method provided by the embodiment of the invention constructs a Trie tree for each filtering dictionary, and filters query-commodity data pairs obtained based on user search words by using the Trie trees of the filtering dictionaries according to the specified filtering rule so as to remove some error matches or sensitive word matches, thereby ensuring the commodity recall accuracy. Because the violent retrieval mode in the prior art is replaced by the efficient index of the Trie tree, the dimensionality of the search diameter is reduced, the filtering efficiency can be obviously improved, the data production period is shortened, and the product iteration speed is increased.
The query-commodity data pair (pair) mentioned in step S102 above represents a matching combination of query to commodity or title, and may also be referred to as a query-title data pair. In the invention, the query-title data pair can be a query-title data pair in an offline matching dictionary mined through a statistical model or a semantic correlation model based on the user search word, and can also be a query-title data pair recalled in online query based on the user search word.
In the step S104, the input query-title data pair is subjected to match filtering by using the Trie tree of the filtering dictionary according to the specified filtering rule.
In the foregoing, no matter what matching dictionary model is based on, the mined query-title is inevitable to have the problems of wrong matching and the like, cannot be directly used, a filtering dictionary needs to be constructed, and meanwhile, some filtering rules are set to filter the query-title.
In an alternative embodiment, the specified filtering rule may include: whether the subject of the data pair is collocated, whether the region is in accordance with the subject, and/or whether sensitive words exist.
For example, if the user searches for air ticket information and recalls train ticket information, the query-title data should be filtered out for the case of no matching subject. For another example, if the recall information includes leader information, pornography information, etc., then the query-title data pair should be removed accordingly if there are sensitive words.
In practical application, a filtering dictionary is constructed according to filtering conditions required by the query to be filtered. For example, for a hotel search query, a region, a sensitive word and other related filtering dictionaries are constructed, and for an air ticket search query, a topic, a sensitive word and other related filtering dictionaries are constructed. Then, a Trie tree is constructed for each filtering dictionary for information retrieval.
A Trie, also called a dictionary tree, a word lookup tree or a key tree, is a multi-branch tree in which characters are stored in a computer in a linked list manner, and a certain node of the Trie does not contain one or more keywords but only contains a part (characters or numbers) of the keywords. The Trie tree is typically applied to counting, sorting and storing a large number of character strings, and has the advantages that the common prefix of the character strings can be utilized to reduce the cost of query time, and unnecessary character string comparison is reduced to the maximum extent, so that the purpose of improving the query efficiency is achieved.
In the embodiment of the invention, the Trie tree of each filtering dictionary is constructed by pressing each element in the filtering dictionary into a (push) tree structure.
Further, each element in the filter dictionary is pushed into the tree structure in such a manner that an arbitrary path from the root node to the leaf node of the tree structure represents one element.
The following describes the construction process of the Trie of the filter dictionary in detail by using an example.
For example, for the region type filtering dictionary, if the filtering dictionary includes three region noun elements of wulanbu system, ukraine and wulanbuto, each node of the region Trie constructed for the filtering dictionary is a Chinese character, and any path from the root node to the leaf node represents a region noun. Specifically, the Trie structure constructed by these three regional noun elements is:
the nodes of the first layer include: wu, there are two children nodes "lan" and "ke".
The nodes of the second layer include: blue, there are two child nodes "cloth" and "bar"; gram, has a child node "blue".
The nodes of the third layer include: cloth, have a sub node "system"; blue, leaf node; bar, has a child node "torr".
The node of the fourth layer includes: a system is a leaf node; and the support is a leaf node.
It can be seen that all the child nodes of each node contain different characters, and the characters passing through the path from the first-layer node to each leaf node are connected together to form the regional noun element corresponding to the leaf node. Specifically, a regional term "ukrainian" is obtained from a leaf node "blue" by tracing to a root node layer by layer, a regional term "uranba" is obtained from a leaf node "unity" by tracing to a root node layer by layer, and a regional term "uranbuto" is obtained from a leaf node "torr" by tracing to a root node layer by layer.
The construction process of the Trie of the filter dictionary is introduced above. Before filtering the input query-title data pair, selecting a Trie tree of a corresponding filtering dictionary for searching the query according to the filtering condition required by the query, thereby improving the purpose and efficiency of filtering. For example, for the query-title data pair searched by the air ticket, a Trie tree of a topic filtering dictionary and a Trie tree of a sensitive word filtering dictionary are selected, and for the query-title data pair searched by the hotel, a Trie tree of a region filtering dictionary and a Trie tree of a sensitive word filtering dictionary are selected.
In an optional embodiment of the present invention, the step of filtering the input query-commodity data pair by using the Trie tree of the filtering dictionary may be implemented as the following manner:
and searching the position of an element in the query-title data pair and the weight of the element by using the Trie tree of the filtering dictionary, and filtering the query-title data pair according to the appearance position and the weight of the element.
For example, if a sensitive word appears in the title of the recalled commodity information, and the weight of the sensitive word exceeds a predetermined threshold, the query-title data pair is removed. If a sensitive word appears in the text introduction of the recalled merchandise information and the weight of the sensitive word does not exceed a predetermined threshold, the sensitive word may be deleted, and the merchandise may be displayed in a later position in sequence, and so on.
In the above, various implementation manners of each link of the embodiment shown in fig. 1 are introduced, and the following describes in detail an implementation process of the Trie-based commodity filtering method according to the present invention by using a specific embodiment with reference to fig. 2.
Fig. 2 is a flow chart illustrating a Trie-based commodity filtering method according to an embodiment of the present invention. Referring to fig. 2, in this embodiment, the filtering dictionary 1, the filtering dictionaries 2, …, and the filtering dictionary N may be constructed in advance according to the filtering conditions of all queries to be filtered. Furthermore, each element in each filtering dictionary is pressed into (push) tree structure in a way that any path from the root node to the leaf node of the tree structure represents one element, and a filtering dictionary 1, a filtering dictionary 2, a filtering dictionary …, a Trie tree 1, a Trie tree 2, a filtering dictionary … and a Trie tree N of the filtering dictionary N are respectively constructed.
The Trie tree-based commodity filtering method can comprise the following steps of:
the method comprises the following steps: and inputting query-title data pairs obtained by mining through a statistical model or a semantic correlation model based on the search terms of the user.
Step two: and selecting a Trie tree of a corresponding filtering dictionary according to the input filtering condition of the query.
Step three: and searching the position of an element in the filtering dictionary and the weight of the element in the input query-title data pair by using the Trie tree of the selected filtering dictionary according to the specified filtering rule, and filtering the query-title data pair according to the appearance position and the weight of the element.
In this step, the specifying the filtering rule includes: whether the subjects of the query-title data pair are matched, whether the regions are matched and whether sensitive words exist.
Step four: and outputting the filtered result.
In the embodiment, a Trie is constructed for each filtering dictionary, the brute force retrieval mode in the prior art is replaced by the efficient index of the Trie, and the filtering efficiency is obviously improved under the condition that the number of entries of the filtering dictionary is large (for example, tens of thousands, hundreds of thousands or even more).
Based on the same inventive concept, the embodiment of the invention also provides a goods filtering device based on the Trie tree, which is used for supporting the goods filtering method based on the Trie tree provided by any one of the embodiments or the combination thereof. Fig. 3 is a schematic structural diagram of a Trie-based commodity filtering apparatus 300 according to an embodiment of the present invention. Referring to fig. 3, the apparatus 300 may include at least: an input module 310, a filtering module 320, and an output module 330.
The functions of the components or devices of the Trie-tree-based commodity filtering apparatus 300 and the connection relationship between the components will now be described:
an input module 310 adapted to input query-commodity data pairs obtained based on the user search terms.
A filtering module 320, connected to the input module 310, adapted to filter the input query-commodity data pairs according to specified filtering rules using a Trie of a filtering dictionary, wherein the Trie of the filtering dictionary is constructed by pushing each element in the filtering dictionary into a tree structure.
And an output module 330 connected to the filtering module 320 and adapted to output the filtered result.
In an alternative embodiment of the invention, the Trie of each filter dictionary is constructed by pushing each element in the filter dictionary into the tree structure in such a way that any path from the root node to a leaf node represents an element.
In an alternative embodiment of the invention, the specified filtering rules may include at least one of:
whether the subject is matched, whether the region is accordant and whether sensitive words exist.
In an alternative embodiment of the invention, the filtering module 320 is further adapted to:
and searching the position of an element in the filtering dictionary and the weight of the element in the query-commodity data pair by using the Trie tree of the filtering dictionary, and filtering the query-commodity data pair according to the appearance position and the weight of the element.
In an alternative embodiment of the present invention, referring to fig. 4, the Trie-based commodity filtering apparatus 300 shown in fig. 3 may further include a selection module 340. The selecting module 340 is respectively connected to the input module 310 and the filtering module 320, and is adapted to select a Trie of the corresponding filtering dictionary according to the filtering condition of the query before the filtering module 320 filters the input query-commodity data pair by using the Trie of the filtering dictionary.
In an alternative embodiment of the present invention, the query-commodity data pairs are mined using a statistical model or a semantic relevance model.
Based on the same inventive concept, the embodiment of the invention also provides a computer storage medium. The computer storage medium stores computer program code which, when run on a computing device, causes the computing device to perform the Trie-tree based commodity filtering method according to any one of the above embodiments or combinations thereof.
Based on the same inventive concept, the embodiment of the invention also provides the computing equipment. The computing device may include:
a processor; and
a memory storing computer program code;
the computer program code, when executed by a processor, causes the computing device to perform the Trie-tree based commodity filtering method according to any one of the above embodiments or combinations thereof.
According to any one or a combination of multiple optional embodiments, the embodiment of the present invention can achieve the following advantages:
according to the Trie-tree-based commodity filtering method and device provided by the embodiment of the invention, a Trie tree is constructed for each filtering dictionary, and query-commodity data pairs obtained based on user search words are filtered by using the Trie trees of the filtering dictionaries according to the specified filtering rule, so that some mismatching or sensitive word matching is removed, and the commodity recall accuracy is ensured. According to the scheme, the violent retrieval mode in the prior art is replaced by the efficient index of the Trie tree, the dimensionality of the search diameter is reduced, the filtering efficiency can be obviously improved, the data production period is shortened, and the product iteration speed is increased. Especially, under the condition that the number of entries of the filtering dictionary is large (for example, tens of thousands, hundreds of thousands or even more), the times of filtering dictionary query can be greatly reduced, and the time and resource consumption of iteration are saved.
It is clear to those skilled in the art that the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and for the sake of brevity, further description is omitted here.
In addition, the functional units in the embodiments of the present invention may be physically independent of each other, two or more functional units may be integrated together, or all the functional units may be integrated in one processing unit. The integrated functional units may be implemented in the form of hardware, or in the form of software or firmware.
Those of ordinary skill in the art will understand that: the integrated functional units, if implemented in software and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computing device (e.g., a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention when the instructions are executed. And the aforementioned storage medium includes: u disk, removable hard disk, Read Only Memory (ROM), Random Access Memory (RAM), magnetic or optical disk, and other various media capable of storing program code.
Alternatively, all or part of the steps of implementing the foregoing method embodiments may be implemented by hardware (such as a computing device, e.g., a personal computer, a server, or a network device) associated with program instructions, which may be stored in a computer-readable storage medium, and when the program instructions are executed by a processor of the computing device, the computing device executes all or part of the steps of the method according to the embodiments of the present invention.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments can be modified or some or all of the technical features can be equivalently replaced within the spirit and principle of the present invention; such modifications or substitutions do not depart from the scope of the present invention.
According to an aspect of the embodiments of the present invention, a1. a goods filtering method based on a Trie tree is provided, including:
inputting a query-commodity data pair obtained based on a user search term;
filtering the input query-commodity data pairs by using a Trie tree of a filtering dictionary according to a specified filtering rule, wherein the Trie tree of the filtering dictionary is constructed by pressing each element in the filtering dictionary into a tree structure;
and outputting the filtered result.
A2. The method according to a1, wherein pushing each element in the filter dictionary into a tree structure comprises:
each element in the filter dictionary is pushed into the tree structure in such a way that any path from the root node to a leaf node represents an element.
A3. The method of a1, wherein the specified filtering rule includes at least one of:
whether the subject is matched, whether the region is accordant and whether sensitive words exist.
A4. The method according to a1, wherein filtering the input query-commodity data pairs using a Trie of a filter dictionary, comprises:
and searching the position of the element in the filtering dictionary and the weight of the element in the query-commodity data pair by using the Trie tree of the filtering dictionary, and filtering the query-commodity data pair according to the appearance position and the weight of the element.
A5. The method according to a1, wherein, before filtering the input query-commodity data pairs by using the Trie tree of the filtering dictionary, the method further comprises:
and selecting a Trie tree of the corresponding filtering dictionary according to the filtering condition of the query.
A6. The method of any of A1-A5, wherein the query-commodity data pairs are mined by a statistical model or a semantic relevance model.
According to another aspect of the embodiments of the present invention, there is further provided B7. a Trie-based commodity filtering apparatus, including:
the input module is suitable for inputting query-commodity data pairs obtained based on the user search terms;
a filtering module adapted to filter the input query-commodity data pairs according to specified filtering rules using a Trie of a filtering dictionary, wherein the Trie of the filtering dictionary is constructed by pushing each element in the filtering dictionary into a tree structure; and
and the output module is suitable for outputting the filtered result.
B8. The apparatus of B7, wherein the Trie of the filter dictionary is constructed by pushing each element in the filter dictionary into a tree structure in such a way that any path from a root node to a leaf node represents an element.
B9. The apparatus of B7, wherein the specified filtering rule includes at least one of:
whether the subject is matched, whether the region is accordant and whether sensitive words exist.
B10. The apparatus of B7, wherein the filtering module is further adapted to:
and searching the position of the element in the filtering dictionary and the weight of the element in the query-commodity data pair by using the Trie tree of the filtering dictionary, and filtering the query-commodity data pair according to the appearance position and the weight of the element.
B11. The apparatus of B7, further comprising:
and the selection module is suitable for selecting the Trie tree of the corresponding filtering dictionary according to the filtering condition of the query before the filtering module filters the input query-commodity data pair by utilizing the Trie tree of the filtering dictionary.
B12. The apparatus of any one of B7-B11, wherein the query-commodity data pairs are mined by a statistical model or a semantic relevance model.
According to yet another aspect of embodiments of the present invention, there is also provided c13. a computer storage medium storing computer program code which, when run on a computing device, causes the computing device to perform the Trie-tree-based commodity filtering method according to any one of a1-a 6.
There is also provided, in accordance with yet another aspect of an embodiment of the present invention, apparatus for computing, including:
a processor; and
a memory storing computer program code;
the computer program code, when executed by the processor, causes the computing device to perform any of the Trie-tree based commodity filtering methods of A1-A6.
Claims (10)
1. A goods filtering method based on a Trie tree comprises the following steps:
inputting a query-commodity data pair obtained based on a user search term;
filtering the input query-commodity data pairs by using a Trie tree of a filtering dictionary according to a specified filtering rule, wherein the Trie tree of the filtering dictionary is constructed by pressing each element in the filtering dictionary into a tree structure;
and outputting the filtered result.
2. The method of claim 1, wherein pushing each element in the filter dictionary into a tree structure comprises:
each element in the filter dictionary is pushed into the tree structure in such a way that any path from the root node to a leaf node represents an element.
3. The method of claim 1, wherein the specified filtering rule comprises at least one of:
whether the subject is matched, whether the region is accordant and whether sensitive words exist.
4. The method of claim 1, wherein filtering the input query-commodity data pairs using a Trie of a filter dictionary comprises:
and searching the position of the element in the filtering dictionary and the weight of the element in the query-commodity data pair by using the Trie tree of the filtering dictionary, and filtering the query-commodity data pair according to the appearance position and the weight of the element.
5. The method of claim 1, wherein prior to filtering the input query-commodity data pairs with the Trie-tree of the filtering dictionary, further comprising:
and selecting a Trie tree of the corresponding filtering dictionary according to the filtering condition of the query.
6. The method of any of claims 1-5, wherein the query-commodity data pairs are mined by a statistical model or a semantic relevance model.
7. A Trie-based commodity filtering apparatus, comprising:
the input module is suitable for inputting query-commodity data pairs obtained based on the user search terms;
a filtering module adapted to filter the input query-commodity data pairs according to specified filtering rules using a Trie of a filtering dictionary, wherein the Trie of the filtering dictionary is constructed by pushing each element in the filtering dictionary into a tree structure; and
and the output module is suitable for outputting the filtered result.
8. The apparatus of claim 7, wherein the Trie of the filter dictionary is constructed by pushing each element of the filter dictionary into a tree structure in such a way that any path from a root node to a leaf node represents an element.
9. A computer storage medium storing computer program code which, when run on a computing device, causes the computing device to perform the Trie-tree based commodity filtering method of any one of claims 1-6.
10. A computing device, comprising:
a processor; and
a memory storing computer program code;
the computer program code, when executed by the processor, causes the computing device to perform the Trie-tree based commodity filtering method of any one of claims 1-6.
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| CN201910176640.0A CN111666487A (en) | 2019-03-08 | 2019-03-08 | Trie tree-based commodity filtering method and device and computing equipment |
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