阅读说明：本技术 搜索词生成方法、系统及计算机可读存储介质 (Search word generation method, system and computer readable storage medium ) 是由 陆家凡 于 2021-08-13 设计创作，主要内容包括：本发明公开了一种搜索词生成方法、系统及计算机可读存储介质,方法包括：获取商品信息以及用户历史搜索词信息；基于商品信息、用户历史搜索词信息以及预设模型构造搜索词；对搜索词进行排序获得搜索词列表；基于商品信息和用户历史搜索词信息构造搜索词,避免仅依赖用户有限的历史搜索数据构造搜索词而无法实现搜索的多样性与新颖性,有效提高转化率。(The invention discloses a method, a system and a computer readable storage medium for generating search words, wherein the method comprises the following steps: acquiring commodity information and historical search word information of a user; constructing search terms based on commodity information, historical search term information of a user and a preset model; sequencing the search terms to obtain a search term list; the search terms are constructed based on the commodity information and the historical search term information of the user, so that the diversity and novelty of search which cannot be realized due to the fact that the search terms are constructed only by means of limited historical search data of the user are avoided, and the conversion rate is effectively improved.)

1. A method for generating search terms, the method comprising:

acquiring commodity information and historical search word information of a user;

constructing a search word based on the commodity information, the user historical search word information and a preset model;

and sequencing the search terms to obtain a search term list.

2. The search term generation method according to claim 1, wherein the preset model is a preset model constructed based on an N-gram language model and a knowledge graph.

3. The method according to claim 2, wherein constructing search terms based on the commodity information, the user history search term information, and a preset model comprises:

acquiring commodity entities and commodity attributes based on the commodity information and the historical search word information of the user;

and constructing a search term based on the preset model, the commodity entity and the commodity attribute.

4. The method for generating search terms according to any one of claims 1 to 3, wherein after constructing a search term based on the commodity information, the user history search term information, and a preset model, the method further comprises:

and saving the search terms based on a pre-constructed prefix tree.

5. The method of claim 4, further comprising:

acquiring a search word input by a user in real time;

recalling prefix-matched candidate words from the prefix tree based on search words input by the user in real time;

judging whether the candidate word has errors or not;

if yes, correcting errors of the candidate words to obtain suggested search words;

if not, the candidate word is saved as a suggested search word;

the step of sorting the search terms to obtain a search term list comprises:

and ordering the suggested search words to obtain a suggested search word list.

6. The method according to claim 1, wherein the step of sorting the search terms to obtain a search term list comprises:

acquiring embedded characteristics of the search terms;

constructing a ranking model based on the embedding characteristics of the search terms;

and sequencing the search words based on the sequencing model to obtain a search word list.

7. The method according to claim 1, wherein the acquiring of the commodity information and the user history search term information includes:

acquiring commodity data in a database;

extracting commodity entities and commodity attributes from the commodity data, wherein the commodity information at least comprises the commodity entities and the commodity attributes;

acquiring user input word data transmitted by a preset buried point;

and obtaining the historical search word information of the user based on the input word data of the user.

8. The method according to claim 7, wherein before extracting the product entity and the product attribute from the product data, the method further comprises:

and performing word segmentation processing on the commodity data.

9. A search term generation system, the system comprising:

the acquisition module is used for acquiring commodity information and historical search word information of a user;

the construction module is used for constructing search terms based on the commodity information, the historical search term information of the user and a preset model;

and the sequencing module is used for sequencing the search words to obtain a search word list.

10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 8.

Technical Field

The invention relates to the field of computers, in particular to a method and a system for generating search terms and a computer-readable storage medium.

Background

The best-standing function of the modern search engine is the first interactive service for searching and forming queries by users. The user enters a new character in the input box, the search engine can provide a list of suggested candidates matching the user prefix, and the user is presented with the top candidates, and the query prefix is often short and ambiguous.

The models employed by traditional sunstest rely primarily on the past popularity of matching candidates for ranking. However, the popularity of certain queries may vary from audience characteristics and user to user. For example, although "dress" and "basketball shoe" have the same popularity as a whole and are both legitimate search terms that show the prefix l, the former is significantly more popular among young female users, while the latter is more likely to occur by men. However, for e-commerce, a personalized function module is needed, which can efficiently guide the user to complete the purchase.

The generation of the prior supermarket search word mainly depends on historical search of a user, but only when the user inputs a certain search word and the search word is in a single form, the search word can be collected into the supermarket search word, the historical data searched by the user is limited, the potential selling points of commodities cannot be accurately found in the collection mode, so that the user cannot be guided to purchase potential hot-selling commodities, and the diversity and novelty of the search cannot be realized only depending on the search data of the user.

Disclosure of Invention

The invention aims to: provided are a search word generation method, system, and computer-readable storage medium.

The technical scheme of the invention is as follows: in a first aspect, the present invention provides a method for generating search terms, the method comprising:

acquiring commodity information and historical search word information of a user;

constructing a search word based on the commodity information, the user historical search word information and a preset model;

and sequencing the search terms to obtain a search term list.

In a preferred embodiment, the preset model is a preset model constructed based on an N-gram language model and a knowledge graph.

In a preferred embodiment, the constructing search terms based on the commodity information, the user history search term information and a preset model includes:

acquiring commodity entities and commodity attributes based on the commodity information and the historical search word information of the user;

and constructing a search term based on the preset model, the commodity entity and the commodity attribute.

In a preferred embodiment, after constructing the search term based on the commodity information, the user history search term information and the preset model, the method further includes:

and saving the search terms based on a pre-constructed prefix tree.

In a preferred embodiment, the method further comprises:

acquiring a search word input by a user in real time;

recalling prefix-matched candidate words from the prefix tree based on search words input by the user in real time;

judging whether the candidate word has errors or not;

if yes, correcting errors of the candidate words to obtain suggested search words;

if not, the candidate word is saved as a suggested search word;

the step of sorting the search terms to obtain a search term list comprises:

and ordering the suggested search words to obtain a suggested search word list.

In a preferred embodiment, the sorting the search terms to obtain a search term list includes:

acquiring embedded characteristics of the search terms;

constructing a ranking model based on the embedding characteristics of the search terms;

and sequencing the search words based on the sequencing model to obtain a search word list.

In a preferred embodiment, the obtaining of the commodity information and the user history search term information includes:

acquiring commodity data in a database;

extracting commodity entities and commodity attributes from the commodity data, wherein the commodity information at least comprises the commodity entities and the commodity attributes;

acquiring user input word data transmitted by a preset buried point;

and obtaining the historical search word information of the user based on the input word data of the user.

In a preferred embodiment, before extracting the product entity and the product attribute from the product data, the method further includes:

and performing word segmentation processing on the commodity data.

In a second aspect, the present invention provides a search term generation system, the system comprising:

the acquisition module is used for acquiring commodity information and historical search word information of a user;

the construction module is used for constructing search terms based on the commodity information, the historical search term information of the user and a preset model;

and the sequencing module is used for sequencing the search words to obtain a search word list.

In a third aspect, the invention provides a computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 8.

Compared with the prior art, the invention has the advantages that: provided are a search word generation method, system and computer-readable storage medium, the method comprising: acquiring commodity information and historical search word information of a user; constructing search terms based on commodity information, historical search term information of a user and a preset model; sequencing the search terms to obtain a search term list; the search terms are constructed based on the commodity information and the historical search term information of the user, so that the diversity and novelty of search which cannot be realized due to the fact that the search terms are constructed only by means of limited historical search data of the user are avoided, and the conversion rate is effectively improved.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts:

fig. 1 is a flowchart of a search term generation method according to embodiment 1 of the present invention;

fig. 2 is a structural diagram of a search term generation system provided in embodiment 2 of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

As described in the background, the generation of the current suggested candidate word mainly depends on the search history of the user, and when the user inputs a certain search word and forms a list, the search word is not received in the suggested candidate word. The behavior data of the user in other scenes except the search scene, which cannot be acquired by the acquisition mode, cannot accurately find the potential selling points of the commodity, so that the user cannot be guided to purchase the potentially hot commodity. There are many goods sold well in the period, but sold little in the search, and for this reason, it is possible that the suggested search term does not find the goods sold in the period and the selling points thereof. In addition, the diversity and novelty of the search cannot be realized only by relying on the search data of the user. The historical data searched by the user is limited, the search data of the user is analyzed to find that the search words of the user are mainly some head commodity words and selling point words, many effective selling point word users do not input the search words, and the selling point words need to be clicked by the user in the suggestion stage so as to improve the conversion rate.

In order to solve the problems, the invention provides a search term generation method, a search term generation system and a computer-readable storage medium, wherein the search term is generated based on commodity information and historical search terms of a user, the generated suggested search term is more accurate, and the potential selling points of commodities can be accurately found to guide the user to purchase potential hot-sold commodities.

Example 1: the embodiment provides a search term generation method, which comprises the following steps:

and S1, acquiring commodity information and user history search word information.

In a preferred embodiment, the method comprises the steps of:

and S11, acquiring commodity information and user history search words. Preferably, the present step comprises:

and S111, acquiring commodity data in the database. Specifically, the information of the commodity on sale in the database is obtained, and the commodity data at least comprises a commodity name and commodity description information.

And S112, extracting the commodity entity and the commodity attribute from the commodity data, wherein the commodity information at least comprises the commodity entity and the commodity attribute.

The commodity entity refers to a commodity name or a commodity code having unique directivity. At present, no existing fields of commodity entities and commodity attributes of commodities can be used, commodity names, commodity descriptions, commodity attributes and the like in commodity data depend on manual maintenance, and some dirty data still exist in the commodity data. In this embodiment, the useful commodity entities and commodity attributes in the extracted commodity data are automatically identified by the NER (named entity identification) model such as Bi-LSTM + CRF. The NER model training needs a large number of manual labeling samples, and the commodity entities and the commodity attributes labeled in the manual labeling samples need to be labeled in combination with experience of operators, so as to solve the problem, in this embodiment, dictionaries such as item words, brand words, attribute words and the like are constructed in advance, and the item words, the brand words and the attribute words of the commodities are extracted by a template matching method. Based on the data matched with the dictionary, a certain amount of samples are labeled, and the labeled samples are used for training a deep NER model, so that effective commodity entities and commodity attributes can be automatically and efficiently extracted.

In a preferred embodiment, before extracting the product entity and the product attribute from the product data, the method further comprises:

and performing word segmentation processing on the commodity data.

Specifically, the preparation and completeness of a segmentation dictionary have a great relationship with the segmentation quality, but as time changes, new words are increased continuously, and it is difficult to fill all new words only by adding the new words by operators, so that a model for mining new words needs to be designed, and a dictionary for searching is enriched continuously. In the present embodiment, the freezing degree and the degree of freedom of all the search words are calculated using spark, the freezing degree represents the degree to which the words can be combined together, and the higher the score represents the higher the probability that two words are combined together. The degree of freedom indicates the degree of the word alone, and a higher score indicates a higher probability of the word alone. And setting a threshold value for the occurrence frequency, the degree of solidification and the degree of freedom respectively, extracting all candidate words meeting the threshold value requirement to obtain a single word segmentation, and removing a historical word segmentation dictionary to obtain a new word.

In an e-commerce scene, words searched by a user and candidate search words are relatively short words, and many words with the same meaning but different expressions exist, so that the text distance of the words cannot be calculated effectively. In the embodiment, the imbedding related technology in NLP is used, a Word2Vec model is used, the imbedding of all the words is obtained through training, and then the imbedding of all the search words is obtained through superposition, so that the similarity between texts can be directly calculated at a high latitude.

In another preferred embodiment, before extracting the product entity and the product attribute from the product data, the method further comprises:

and (5) carrying out template matching.

Specifically, the word segmentation algorithm for dynamic programming cuts words of all linguistic data, and then finds all entities and attributes by using a template matching mode. Since the word segmentation algorithm is used in the method, the quality of the word segmentation algorithm is related to the accuracy and richness of the dictionary, and a new word discovery model is added, and the model mainly calculates the degree of solidity, the degree of freedom and the frequency of words. The degree of solidification is defined asWhere p (x) is the probability that the text fragment x appears in the entire corpus. It is conceivable that the fragments with the greatest degree of coagulation are words such as "gulosity", "245852, and words such as" each of which almost always occurs simultaneously with another word, and are never used elsewhere. But considering only the degree of solidity does not allow words such as "ancestors" to be computed because if a text segment can be computed as a word, it should be flexible to appear in a variety of different environments, with a very rich set of left and right neighbors. Therefore, the degree of freedom of the words is calculated, and the left and right information entropy of the text segment is defined by the degree of freedom. In order to extract the fragment of the participle, all potential words can be obtained by using an n-gram method, then a threshold value is set for the occurrence frequency, the degree of solidification and the degree of freedom respectively, and finally all candidate search words meeting the threshold value requirement can be extracted.

And S113, acquiring user input word data transmitted by the preset buried point. Preferably, the user input word data includes a search word clicked and input by the user.

And S114, obtaining the historical search words of the user based on the data of the input words of the user.

The data of the user input words are directly extracted from the data of the buried points, the selection tendency of the user is obtained through the analysis of the data of the user input words, and the user can be more accurately suggested.

And S2, constructing the search terms based on the commodity information, the historical search term information of the user and the preset model.

In a preferred embodiment, the preset model is a preset model constructed based on an N-gram language model and a knowledge graph.

In a preferred embodiment, the method comprises the steps of:

and S21, acquiring commodity entities and commodity attributes based on the commodity information and the historical search word information of the user.

And S22, constructing search terms based on the preset model, the commodity entity and the commodity attributes.

After the extraction of the commodity entities and the commodity attributes is completed, all combinations of the commodity entities and the combinations of the commodity entities and the commodity attributes are used as all potential search terms. In order to solve the problem of personalized cold start, a base score needs to be added for each potential search word. The weight w of each potential search word is defined by the maximum distance w between the commodity entity and the commodity entity in the basic corpus as log (1/d), and the weight of the final potential search word is the sum of all the weights w in the superposition corpus.

Preferably, in this embodiment, an N-gram language model is used to construct all possible search terms.

In a preferred embodiment, after step S2, the method further includes:

and S2A, saving the search terms based on the pre-constructed prefix tree.

In order to implement the function of prefix matching, the present embodiment uses a data structure such as a prefix tree to store all possible search terms constructed in step S2.

More specifically, since the online service is usually written in C + +, if the storage of the prefix tree is placed in the online server, a lot of time is consumed when the model is loaded, and a lot of memory is occupied, and obviously, as the amount of data increases, the stability of the online system decreases. In this embodiment, the prefix tree is constructed by using spark online, the structure of the hash table is output, and finally the data is pushed to the VDE. The online server directly calls the VDE to access the candidate suggested search terms, so that the occupation of the memory is saved, and the time consumption of online processing is reduced. The prefix tree is constructed based on all potential search terms.

Because the originally displayed candidate words do not carry out error correction processing on the words input by the user. For example, if the user wants to search for the 'bosdend down jacket', but mistakenly inputs the 'slodend' by hand, the user suggests a search word of the 'slodend down jacket', and if the user does not pay attention to clicking the suggested search word, wrong commodity information is obtained. If no error correction is performed in the suggestion stage, the search recalling link directly uses the search words with wrongly written characters to recall, so that wrong commodities can be recalled. To solve this problem, in a preferred embodiment, the method further comprises:

S2B, performing error correction processing on the search terms input by the user in real time, specifically comprising:

and S2B1, acquiring the search words input by the user in real time.

Analyzing the search data of the user, the user often inputs some special punctuations or even emoticons due to wrong touch or other reasons when searching. If these characters are used directly for searching, it may result in no result of the item search or errors in the searched items. To solve this problem, in the present embodiment, an operation of removing a special word is added when processing the user input.

And S2B2, recalling the candidate words with the matched prefixes from the prefix tree based on the search words input by the user in real time.

Specifically, during the input process of the user in the input box, the suggested search terms presented below the input box do not present potential search terms according to the logic entered by the user. For example: when a user enters a "dress," the logical input is the brand of the dress or attributes of the dress that the user would then enter. If the 'red female one-piece dress' appears, the normal thinking logic of the user is violated, and the user is required to convert the original input logic. In this embodiment, the prefix tree holds all possible search terms, and when the user enters a different term, the prefix-matched candidate term is recalled from the prefix tree.

More specifically, the traversal gets all possible search inputs, including chinese phrases, chinese pinyin, english singles, and combinations of chinese + pinyin, and then recalls all matching candidate words from the prefix tree for all possible search inputs and falls based on the underlying ranking of the potential search words. The VDE can conveniently and quickly inquire the data in the key-value format and push the manufactured key-value data into the VDE at regular time. The stored recall candidate words are directly read from the VDE by obtaining user input.

And S2B3, judging whether the candidate word is wrong, if so, entering the step S2B4, and if not, entering the step S2B 5.

And S2B4, correcting errors of the candidate words to obtain suggested search words.

And S2B5, saving the candidate words as the suggested search words.

The step of sorting the search terms to obtain a search term list comprises:

and ordering the suggested search words to obtain a suggested search word list.

When the user inputs wrongly written characters during searching, the suggestion list page can still display correct candidate words, so that the conversion rate of commodities is improved, and the purchasing experience of the user is improved.

And S3, sequencing the search words to obtain a search word list.

In a preferred embodiment, the method comprises the steps of:

and S31, acquiring the embedded characteristics of the search terms. Specifically, the embedded features of the search terms include user historical search features, user image features and user word search statistic features.

And S32, constructing a ranking model based on the embedded characteristics of the search terms.

Specifically, a model is constructed by using a user history search feature, a user image feature, a user word statistical feature and the like. Preferably, a rank-to-rank model or a DSSM-based improved model is used, and since a lot of information can be used to determine the relevance of the input and the document, and there are a lot of search logs in the interconnection, a machine learning method is used to train a ranking model using the user's click behavior log as training data.

The Learn-to-rank model is a supervised model and therefore requires the labeling of training samples. The training samples are labeled by two methods, namely manual labeling and automatic labeling from a search log. Because the manual labeling is mixed with the interference of human factors, the real intention of a user in searching cannot be fed back, and the cost of the manual labeling method is high. The search log records the search behavior and the corresponding click behavior of the user, and the user click behavior actually implies the correlation of the query-gather pair and can be used as a basis for judging the correlation degree of the query-gather pair. Therefore, the present embodiment preferably adopts a method of automatic labeling directly from the search log data. In the current suggestion scene, a user inputs a certain query, and n candidate suggested words are displayed for the user. One simple method is to rank the word clicked by the user at the top and rank the other words behind the clicked word according to the original sequence. However, in a real scene, a user may miss a word. To remove this noise, Microsoft proposed the concept of an effective click, where words clicked by the user and browsing for longer than 30ms after clicking are considered as effective clicks. Training a machine learning model also requires extracting valid features from the training samples. Specific characteristics are shown in table 1.

Studies have shown that suggested candidate words clicked on by a user are related to the user's age, gender and region of residence. In combination with the business explanation of the company, the commodities concerned by different age groups and the commodity attribute great probability are different, young users may prefer the clothes with sweet and lovely style, and middle-aged users may prefer the clothes with mature and steady properties; the commodities concerned by users of different genders are different, and the users also input 'l', female users may be concerned with 'Langome' or 'dress', and male users may be concerned with 'Lining' or 'basketball shoes'; different areas are affected by the local climate and the goods of interest are different, also inputting "y", south users may be interested in "sports shoes" and north users may be interested in "down jackets". In this embodiment, the user age groups are subjected to barrel separation, wherein 1 to 10 years old users are 2, 11 to 20 years old users are 2, 21 to 30 years old users are 3, 31 to 40 years old users are 4, 41 to 50 years old users are 5, 50 years old users are 6, and the age characteristics of the users with missing age characteristics are set to be 0. The regional characteristics comprise a warehouse to which the user belongs and a province in which the user belongs, the province characteristic is carried out by taking the first 3 bits of the province code, and the user with the province characteristic missing is set as 0. In order to describe the preference degrees of different users for the candidate words, statistical characteristics under the same social attribute can be counted. And by combining the search scene, the statistical characteristics of click rate, conversion rate, frequency, uv amount and the like of the candidate suggested words in different time windows can be counted.

The words that the user clicks during the suggestion phase are related to historical searches and the goods that the user clicks. For example, the user has recently searched for "down jackets," and when the user enters "y," the user is more inclined to click on "down jackets" rather than "shuttles. The user has historically searched for "shirt stripes" and when the user enters "down jacket" the user is more inclined to click on "down jacket stripes" rather than "down jacket wave points". Semantic distances of the terms of the user's historical search from the candidate search terms can be calculated.

TABLE 1

To calculate the semantic distance between texts, a simple method is to encode the texts using one-hot. Assuming that the number of different words in the dictionary is N, each word may correspond one-to-one to a consecutive integer from 0 to N-1. These integers corresponding to a word are called the index of the word. Assuming that the index of a word is i, a vector of length N of all 0's is created to get a one-hot vector representation of the word and its ith bit is set to 1. Thus, each word is represented as a vector of length N that can be used directly by the neural network. While one-hot word vectors are easy to construct, they are generally not a good choice. One of the main reasons is that the one-hot word vector cannot accurately express the similarity between different words, such as the commonly used cosine similarity. Because the cosine similarity of the one-hot vector of any two different words is 0, the similarity between a plurality of different words is difficult to accurately reflect through the one-hot vector. It is to solve this problem that word2vec tools have been proposed. It represents each word as a fixed-length vector and enables the vectors to better represent similarity and analogy relationships between different words. Therefore, the word2vec tool is preferably used for extracting the embedding feature of the text. The training embedding step is that firstly, commodities browsed by clicking of a single session of each user are extracted from a user search behavior log, and all the commodities are spliced according to a time sequence to construct a training sample. And then segmenting the spliced sample into words by using a word segmentation module. And finally, training by using a spare word2ved tool to obtain embedding of each word, and obtaining the embedding of the search word by using the embedding of the word segmentation phrase of the search word in a weighted summation mode. After the embedding is obtained, the semantic similarity between the words can be calculated through cosine similarity.

And S33, sequencing the search words based on the sequencing model to obtain a search word list.

Specifically, the ranking model scores each candidate word and finally ranks the candidate words upside down according to the score.

The method, the system and the computer-readable storage medium for generating the search term provided by the embodiment comprise the following steps: acquiring commodity information and historical search word information of a user; constructing search terms based on commodity information, historical search term information of a user and a preset model; sequencing the search terms to obtain a search term list; the search terms are constructed based on the commodity information and the historical search term information of the user, so that the diversity and novelty of search which cannot be realized due to the fact that the search terms are constructed only by means of limited historical search data of the user are avoided, and the conversion rate is effectively improved.

Example 2: the present embodiment provides a search term generation system, including:

the obtaining module 21 is configured to obtain commodity information and user history search term information.

And the constructing module 22 is used for constructing the search terms based on the commodity information, the historical search term information of the user and the preset model.

And the sorting module 23 is configured to sort the search terms to obtain a search term list.

In a preferred embodiment, the construction module 22 comprises:

a first obtaining unit 221, configured to obtain a commodity entity and a commodity attribute based on commodity information and user search data;

a constructing unit 222, configured to construct a search term based on the preset model, the commodity entity and the commodity attribute.

In a preferred embodiment, the system further comprises:

and the storage module 24 is configured to store the search term based on a pre-constructed prefix tree.

In a preferred embodiment, the system further comprises:

the error correction module 25 specifically includes:

a second obtaining unit 251, configured to obtain a search term input by a user in real time;

a recall unit 252, configured to recall the candidate words with prefixes matched from the prefix tree based on the search words input by the user in real time;

the judging unit 253 is used for judging whether the candidate word is wrong;

an error correction unit 254 for, when the judgment unit 253 judges that the candidate word is incorrect, error-correcting the candidate word to obtain a suggested search word;

a saving unit 255 configured to save the candidate word as the suggested search word when the judging unit 253 judges that the candidate word is free.

In a preferred embodiment, the sorting module 23 includes:

a third obtaining unit 231 for obtaining an embedding feature of the search term;

a constructing unit 232, configured to construct a ranking model based on the embedding characteristics of the search terms;

and a sorting unit 233, configured to sort the search terms based on the sorting model to obtain a search term list.

In a preferred embodiment, the obtaining module 21 includes:

a fourth acquiring unit 211, configured to acquire commodity data in a database;

an extracting unit 212, configured to extract the commodity entity and the commodity attribute from the commodity data, where the commodity information at least includes the commodity entity and the commodity attribute;

a fifth obtaining unit 213, configured to obtain user input word data transmitted by a preset buried point;

a sixth obtaining unit 214, configured to obtain user history search word information based on the user input word data.

In a preferred embodiment, the obtaining module 21 further includes:

a word segmentation unit 215, configured to perform word segmentation on the product data before the extraction unit 212 extracts the product entity and the product attribute from the product data.

It should be noted that: in the search term generation system provided in the above embodiment, when a search term is suggested, only the division of the above functional modules is taken as an example, and in practical applications, the above function distribution may be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the above described functions. In addition, the search term generation system provided in the above embodiment and the search term generation method provided in embodiment 1 belong to the same concept, that is, the system is based on the method, and the specific implementation process thereof is described in detail in the method embodiment and is not described herein again.

Example 3: the present embodiment provides a computer-readable storage medium having stored thereon a computer program which, when being executed by a processor, carries out the steps of the method of any of the embodiment 1.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, the system or system embodiments are substantially similar to the method embodiments and therefore are described in a relatively simple manner, and reference may be made to some of the descriptions of the method embodiments for related points.

While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the embodiments of the invention.

It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.