CN107169837B

CN107169837B - Method, device, electronic equipment and computer readable medium for assisting search

Info

Publication number: CN107169837B
Application number: CN201710357106.0A
Authority: CN
Inventors: 史亚妮; 谢群群; 郝晖; 邵荣防; 欧阳硕; 李玩伟
Original assignee: Beijing Jingdong Century Trading Co Ltd; Beijing Jingdong Shangke Information Technology Co Ltd
Current assignee: Beijing Jingdong Century Trading Co Ltd; Beijing Jingdong Shangke Information Technology Co Ltd
Priority date: 2017-05-19
Filing date: 2017-05-19
Publication date: 2021-10-01
Anticipated expiration: 2037-05-19
Also published as: CN107169837A

Abstract

The application discloses a method, a device, electronic equipment and a computer readable medium for assisting search. The method comprises the following steps: responding to the search operation of the user and acquiring a user ID; acquiring user behavior data through the user ID, wherein the user behavior data comprises long-term behavior data and short-term behavior data; inputting the user behavior data into a user-assisted search model to obtain a ranking score; and providing auxiliary information for the search operation of the user according to the ranking score. The method, the device, the electronic equipment and the computer readable medium for assisting in searching can more accurately match the user requirements and achieve personalized assisted searching.

Description

Method, device, electronic equipment and computer readable medium for assisting search

Technical Field

The invention relates to the field of computer information processing, in particular to a method and a device for assisting search, electronic equipment and a computer readable medium.

Background

With more and more products provided by e-commerce, the amount of purchased products generated by an electric field occupies more and more space in the market, and how to search for the articles desired by the user on the e-commerce platform becomes a problem which needs to be solved urgently. For e-commerce platforms, searching is a very important way for users to find goods. However, in the face of a large number of products and different product characteristics, in order to accurately know the needs of customers, various products for assisting the user in searching, such as drop-down, hotword, scout, related search, etc., have been derived in recent years. The search entry space is limited, and hot words are used as candidate sets at the beginning to meet most requirements; with the upgrading of consumption, the personalized demands of users are increasing. And the most relevant words of the users are recommended for different users, so that the user search can be assisted.

The existing auxiliary search scheme is optimized for the overall quality, the user historical search, click and ordering behaviors are used as basic data, and the overall keyword quality is improved by enlarging a data source and optimizing the sequence; the sorting mainly utilizes KPI sorting factors and machine learning. The prior art has the following defects: the prior art has a Martha effect, the quality of the front row is good, the number of exposed clicks of a user is more, and the algorithm considers the quality of the front row to be better. All users see the same data at present, but for individuals, the quality is not necessarily what the users want. And, the data feedback of the current auxiliary search does not have timeliness.

Therefore, a new method, apparatus, electronic device, and computer-readable medium for assisting a search is needed.

The above information disclosed in this background section is only for enhancement of understanding of the background of the invention and therefore it may contain information that does not constitute prior art that is already known to a person of ordinary skill in the art.

Disclosure of Invention

In view of this, the present invention provides a method, an apparatus, an electronic device and a computer readable medium for assisting search, which can more accurately match user requirements and implement personalized assisted search.

Additional features and advantages of the invention will be set forth in the detailed description which follows, or may be learned by practice of the invention.

According to an aspect of the present invention, there is provided a method for assisting a search, the method comprising: responding to the search operation of the user and acquiring a user ID; acquiring user behavior data through a user ID, wherein the user behavior data comprises long-term behavior data and short-term behavior data; inputting user behavior data into a user-assisted search model to obtain a ranking score; and providing auxiliary information for the search operation of the user according to the ranking scores.

In an exemplary embodiment of the present disclosure, further comprising: and establishing an auxiliary search model through historical user behavior data.

In an exemplary embodiment of the present disclosure, building an assisted search model from historical user behavior data includes: extracting personalized feature data through historical user behavior data, wherein the personalized feature data comprises category preference data, gender preference data and recent search data; and establishing a user-assisted search model by utilizing the personalized feature data.

In an exemplary embodiment of the present disclosure, building a user-assisted search model using personalized feature data includes: and establishing a user-assisted search model through the category preference data.

In an exemplary embodiment of the present disclosure, building a user-assisted search model from category preference data includes: acquiring historical user behavior data; the unit data of the stock quantity and the corresponding category data are extracted through historical user behavior data; and establishing a user-assisted search model through the stock unit data and the category data.

In an exemplary embodiment of the present disclosure, building a user-assisted search model by stock level unit data and category data includes: extracting first preset behavior related data and second preset behavior related data through stock unit data and corresponding category data; obtaining the score of the category through the first preset behavior related data, the second preset behavior related data and the corresponding weight; and sequencing all the categories according to the scores to obtain the preset category numbers.

In an exemplary embodiment of the present disclosure, the category score is obtained by the following formula:

wherein, f (uuid)_M,cid3_N) For the user uuid_MAt cid3_NCategory score under category N, N₁Is the number of the first predetermined actions, alpha is the weight of the number of the first predetermined actions, t0 is the occurrence time of the first predetermined actions, n₂Is the number of the second predetermined actions, δ is the weight of the number of the second predetermined actions, β is the weight of the second predetermined actions, t is the current time, and t1 is the time of occurrence of the second predetermined actions.

In an exemplary embodiment of the present disclosure, building a user-assisted search model using personalized feature data includes: a user-assisted search model is built from gender preference data.

In an exemplary embodiment of the present disclosure, building a user-assisted search model from gender preference data includes: and establishing a user auxiliary search model through the user portrait and the gender preference data.

In an exemplary embodiment of the present disclosure, building a user-assisted search model using personalized feature data includes: a user-assisted search model is built from recent search data.

According to an aspect of the present invention, there is provided an apparatus for assisting a search, the apparatus comprising: the response module is used for responding to the search operation of the user to acquire the user ID; the data module is used for acquiring user behavior data through the user ID, and the user behavior data comprises long-term behavior data and short-term behavior data; the scoring module is used for inputting the user behavior data into the user auxiliary search model to obtain a ranking score; and the auxiliary module is used for providing auxiliary information for the search operation of the user according to the ranking scores.

In an exemplary embodiment of the present disclosure, further comprising: and the model module is used for establishing an auxiliary search model through historical user behavior data.

According to an aspect of the present invention, an electronic device is provided, the electronic device including: one or more processors; storage means for storing one or more programs; when executed by one or more processors, cause the one or more processors to implement a method as above.

According to an aspect of the invention, a computer-readable medium is proposed, on which a computer program is stored, characterized in that the program, when executed by a processor, implements a method as above.

According to the method, the device, the electronic equipment and the computer readable medium for auxiliary search, the user requirements can be matched more accurately, and personalized auxiliary search is realized.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.

Drawings

The above and other objects, features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings. The drawings described below are only some embodiments of the invention and other drawings may be derived from those drawings by a person skilled in the art without inventive effort.

FIG. 1 is a flow chart illustrating a method for assisting a search in accordance with an exemplary embodiment.

FIG. 2 is a process flow diagram illustrating a method for assisting a search in accordance with an exemplary embodiment.

FIG. 3 is a flow chart illustrating a method for assisting a search in accordance with another exemplary embodiment.

FIG. 4 is a process flow diagram illustrating a method for assisting a search in accordance with another exemplary embodiment.

FIG. 5 is a process flow diagram illustrating a method for assisting a search in accordance with another exemplary embodiment.

FIG. 6 is a block diagram illustrating an apparatus for assisting a search in accordance with an example embodiment.

FIG. 7 is a block diagram illustrating an electronic device in accordance with another example embodiment.

DETAILED DESCRIPTION OF EMBODIMENT (S) OF INVENTION

Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as 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 concept of example embodiments to those skilled in the art. The same reference numerals denote the same or similar parts in the drawings, and thus, a repetitive description thereof will be omitted.

Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to provide a thorough understanding of embodiments of the invention. One skilled in the relevant art will recognize, however, that the invention may be practiced without one or more of the specific details, or with other methods, components, devices, steps, and so forth. In other instances, well-known methods, devices, implementations or operations have not been shown or described in detail to avoid obscuring aspects of the invention.

The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.

The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.

It will be understood that, although the terms first, second, third, etc. may be used herein to describe various components, these components should not be limited by these terms. These terms are used to distinguish one element from another. Thus, a first component discussed below may be termed a second component without departing from the teachings of the disclosed concept. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.

It will be appreciated by those skilled in the art that the drawings are merely schematic representations of exemplary embodiments, and that the blocks or flow charts in the drawings are not necessarily required to practice the present invention and are, therefore, not intended to limit the scope of the present invention.

The following detailed description of exemplary embodiments of the disclosure refers to the accompanying drawings.

As shown in fig. 1, in S102, a user ID is acquired in response to a search operation by a user. In the embodiment of the present invention, whether the user performs the search operation may be determined, for example, by monitoring the user operation on the web page. The user can be judged to be performing the search operation by capturing the click operation of the user in the search box, for example. When a user performs a search operation, the ID of the user is acquired. The user ID may be obtained, for example, through a user login operation, or may be obtained, for example, through a cookie file in a web page, which is not limited in the present invention.

In S104, user behavior data is acquired by the user ID, and the user behavior data includes long-term behavior data and short-term behavior data. In the embodiment of the present invention, the long-term behavior data may be, for example, long-term interest data of the user, and the long-term interest data of the user may be obtained, for example, through a purchasing or browsing operation of the user in the system, so as to generate the long-term behavior data. The short-term behavior data may be, for example, short-term interest data of the user, and the short-term interest data may be generated, for example, by searching data recently searched by the user, and the short-term behavior data may be generated.

In S106, user behavior data is input into the user-assisted search model to obtain a ranking score. The user search assistance model may be established, for example, from historical operational data of the user, and based on the obtained data above, the user search assistance model may be established, for example, from long-term behavior data and short-term behavior data in the user's history. In this embodiment, the long-term and short-term behavior data may be, for example, data of a user's purchase of a certain category of commodity within a certain period of time, and may also be, for example, historical search data of the user. The user-assisted search model may be constructed, for example, by using a large amount of historical data and a currently existing mathematical algorithm, so as to rank and score each item of auxiliary information to be given when a current user performs a search operation. For example, a user may perform a search operation, the entered word is "hand", and the auxiliary information for the search may provide the following pull-down vocabulary: "mobile phone, watch, hand cream, accomodate", according to user's search auxiliary model, grade above-mentioned pull-down vocabulary, give different scores to different vocabularies.

In S108, the auxiliary information is provided for the search operation of the user in accordance with the ranking score. After obtaining the ranking scores, the above-mentioned pull-down vocabularies are limited in the pull-down box of the search bar in order according to the scoring data, as described above. For the user A, the search operation is carried out, the input characters are 'hands', and the auxiliary information sequentially comprises the following steps according to the scores: the "watch, mobile phone, hand cream, and storage" displays the auxiliary information to the user a in order of "watch, mobile phone, hand cream, and storage". For the user B, the search operation is carried out, the input characters are 'hands', and the auxiliary information sequentially comprises the following steps according to the scores: the "storage, hand frost, watch, and mobile phone" display the auxiliary information to the user B in order of "storage, hand frost, watch, and mobile phone".

According to the method for assisting in searching, the auxiliary searching information is ranked and scored through the auxiliary model, and then the auxiliary searching information is displayed in sequence through the scored data, so that the requirements of users can be matched more accurately, and personalized auxiliary searching is realized.

It should be clearly understood that the present disclosure describes how to make and use particular examples, but the principles of the present disclosure are not limited to any details of these examples. Rather, these principles can be applied to many other embodiments based on the teachings of the present disclosure.

In an exemplary embodiment of the present disclosure, further comprising: and establishing an auxiliary search model through historical user behavior data. In an exemplary embodiment of the present disclosure, building an assisted search model from historical user behavior data includes: extracting personalized feature data through historical user behavior data, wherein the personalized feature data comprises category preference data, gender preference data and recent search data; and establishing a user-assisted search model by utilizing the personalized feature data. Fig. 2 is a process flow diagram illustrating a method for assisting a search in accordance with another exemplary embodiment. As shown in fig. 2, the historical data includes long-term interest data and short-term interest data, and the data processing is performed on the long-term interest data and the short-term interest data, and the data processing may be, for example, data cleaning, where the original data (the long-term interest data and the short-term interest data) is subjected to data cleaning to obtain data information in a predetermined format. Data cleansing is the process of re-examining and verifying data with the aim of deleting duplicate information, correcting existing errors, and providing data consistency. ETL data cleansing techniques may be employed, for example. The ETL data cleansing is a process of data extraction (Extract), transformation (Transform), and loading (Load). And generating personalized feature data through the data after data processing, and establishing a user-assisted search model through the personalized feature data.

FIG. 3 is a flow chart illustrating a method for assisting a search in accordance with another exemplary embodiment. FIG. 3 is an exemplary illustration of building a user-assisted search model.

As shown in fig. 3, in S302, a user-assisted search model is built through category preference data. In an exemplary embodiment of the present disclosure, building a user-assisted search model from category preference data includes: acquiring historical user behavior data; the unit data of the stock quantity and the corresponding category data are extracted through historical user behavior data; and establishing a user-assisted search model through stock unit (sku) data and category data. With the increase of time, the probability of browsing the same kind of objects by the user is greatly reduced. In the embodiment of the invention, considering that the direct weighting is carried out on the categories on the line, in order to distinguish the influence of the category behaviors in different time periods on the current sequencing, a larger attenuation function is adopted to attenuate the categories. And analyzing browsing behaviors of the same category of the user, and counting frequency analysis of different users for browsing the same category of the commodity.

In the embodiment of the present invention, sku is a stock quantity unit. The category, which may be cid3, for example, first takes the raw data: the data source may, for example, be from App's data browsing over the past 30 days and sku added to the shopping cart. The raw data may for example be in the form:

user behavior data

User' s	Browsing sku	time	sku corresponds to cid3	Behavior
					uuid1	sku 7	2016/12/8 10:47	E	Add car
uuid1	sku 7	2016/12/8 10:46	E	Browsing
					uuid1	sku 6	2016/12/8 10:46	E	Browsing
uuid1	sku 5	2016/12/7 10:45	B	Browsing
					uuid1	sku 4	2016/12/6 10:45	W	Browsing
uuid2	sku
		3	2016/12/5 10:45	D	Browsing
uuid2	sku
		2	2016/12/4 10:45	C	Browsing
uuid2	sku
		1	2016/12/3 10:45	A	Add car
uuid2	sku
		1	2016/12/3 10:44	A	Browsing
……

Then, based on a certain class, a score of the class is calculated. In this embodiment, taking the calculation of the three-level category score as an example, the scores of all skus under a cid3 are added to obtain the final score of the user at the cid 3. The calculation formula is described below by way of example.

And finally, calculating the category scores of the cid3, calculating the score of the cid3 of all skus in 30 days of the user uuid1, and finally outputting the score in an inverted manner according to the score. For simplicity of calculation, for example, only the top cid3list of each user is selected to be generated, where the top cid3list is the top 10 most preferred categories for a certain user.

User 1

Serial number	User Top cid3list
		1	A
2	B
		3	C
4	D
		5	E
6	F
		7	G
8	H
		9	I
10	J

In an exemplary embodiment of the present disclosure, building a user-assisted search model by stock level unit data and category data includes: extracting first preset behavior related data and second preset behavior related data through stock unit data and corresponding category data; obtaining the score of the category through the first preset behavior related data, the second preset behavior related data and the corresponding weight; and sequencing all the categories according to the scores to obtain the preset category numbers. In the embodiment of the invention, the commodity belongs to three levels of high-correlation classification information. The first predetermined behavior may be, for example, a "car add" behavior, i.e., a behavior that adds sku to a shopping cart for the user. The second predetermined behavior may be, for example, "browse," which is a sku browsed by the user.

In the embodiment of the invention, the timeliness of the model, the number of commodities and the shopping line of the user are comprehensively considered, and the category score is obtained through the following formula:

In S304, a user-assisted search model is built from gender preference data. In an exemplary embodiment of the present disclosure, building a user-assisted search model from gender preference data includes: and establishing a user auxiliary search model through the user portrait and the gender preference data. And introducing a user portrait module to obtain the gender of the user, and matching according to the word portrait-gender preference of the query word. The method for gender portrayal in specific word portrayal comprises the following steps:

1. description of field attributes

The word sex attribute is score, score belongs to [0,1000], the larger the number, the stronger the male attribute, is unknown at 0, 400-500 or neutral in the middle interval.

2. Excavation scheme

Taking data of real gender screened from registered users as samples, taking word sets of male and female search words, and respectively calculating the probability of the word X appearing in the male set A and the female set B; the male sex characteristics of the words are represented by Bayesian theory in combination with the frequency of occurrence of the words in the male sample and the scale factor of the words.

The purpose of the annotation is to label words with the correct gender based on facts, such as "lipstick" as a strong female feature. The words may also be labeled, for example, with a gender that gives the platform user a habit to use, such as "lipstick" being labeled as a strong male feature if it is assumed that it is a boy buy; (most boys buy it and can understand it).

a. For each word in the test set, taking N samples of persons of known gender (one sample for a person who searched for the word and a male or female of known gender);

word and phrase	Total number of samples	Male as the aggregate A	Set B as female
				Word X₁	N＝X_1,1+X_1,2	X_1,1	X_1,2
Word X₂	N＝X_2,1+X_2,2	X_2,1	X_2,2
				Word X₃	N＝X_3,1+X_3,2	X_3,1	X_3,2
……	……	……	……
				Word X_n	N＝X_n,1+X_n,2	X_n,1	X_n,2

b. According to Bayesian theory, the probability of a male user search in the word Xn is P (X)_n,1|X_n)＝(X_n,1/N), male features of the word; word X_nThe probability of searching by the female user is P (X)_n,2|X_n)＝(X_n,2/N)＝1-P(X_n,2|X_n)。

c. The probability (frequency of occurrence of the rephrase/total frequency of the set of words) or the frequency of occurrence of the word in the corpus is used as a scale factor.

d. The term male gender propensity p ═ (scale factor) X (X)_n,1/N)。

In the embodiment of the present invention, for example, in the user-assisted search model, different weights may be set for different gender preferences to preferentially select when performing assisted message pushing.

In S304, a user-assisted search model is built from the most recent search data. And acquiring the most recently searched historical words by utilizing click stream data. Referring to the flowchart shown in fig. 4, the user click stream is received, the message tuple is parsed, the related information in the message tuple, which may be, for example, the user ID and the search word, is parsed, and the information is stored. And when the auxiliary search information is provided for the user in real time, the latest N user search records are obtained through real-time storage and calling. The memberships correspond to the user search terms; score corresponds to the timestamp of the associated member, and the storage command may be, for example: zAdd key member score.

According to the method for assisting in searching, the user assisting searching model is established through the category preference data, the calculation scheme formula is simple, the effect is obvious, the method is very suitable for calculating the category preference of the user, the future personalization is ubiquitous, the most frequent personalization in e-commerce is the user category preference, and the method can be used for supporting business requirements in a large quantity subsequently.

According to the method for assisting search, disclosed by the invention, a user assisting search model is established through gender preference data, in the prior art, the characteristics of a word are utilized for mining, in the invention, according to a large number of behaviors of known user gender, a Bayesian probability memory scale factor is utilized for counting whether the word has gender preference, and an accurate distinguishing result can be obtained.

As shown in the flow of fig. 5, according to the method for assisting search of the present invention, a search assist model for different users can be established according to personalized data of the users, and thus, different search assist information can be provided for thousands of people. It is also possible to provide, for example, a simple and efficient calculation of category preference formula, facilitating fast multiplexing. Personalized experience is introduced, user experience is improved, and user conversion rate is improved. Taking a search assistant product pull-down word as an example, the specific personalized application display effect is as follows:

1. and acquiring the latest search word according to the user id requesting the pull-down service, establishing an inverted index for the prefix of the latest search word, and taking the latest search word as the first pull-down word if the prefix input by the user is matched with the prefix. If not, skip.

2. According to the user id requesting the pull-down service, the category preference of the user is obtained, the keywords are searched for the user in the pull-down word candidate set of a certain prefix word, and if the high-correlation category of the keywords is the preference category of the user, the keywords are ranked before being added according to the preference weight multiplier coefficient.

3. The same kind of words are preferred by gender, for jeans, men generally like to search for jeans, and for women, generally for jeans women with holes, Korean edition, etc., the words we have mined are precisely matched according to whether the words have gender preferences or not. And associating the user portrait module according to the user id to acquire the gender of the user. And then, acquiring a pull-down candidate set according to the prefix input by the user, matching the gender preference and the preference weight of the search keywords of the user with the gender of the user, and performing pre-ranking.

Those skilled in the art will appreciate that all or part of the steps implementing the above embodiments are implemented as computer programs executed by a CPU. The computer program, when executed by the CPU, performs the functions defined by the method provided by the present invention. The program may be stored in a computer readable storage medium, which may be a read-only memory, a magnetic or optical disk, or the like.

Furthermore, it should be noted that the above-mentioned figures are only schematic illustrations of the processes involved in the method according to exemplary embodiments of the invention, and are not intended to be limiting. It will be readily understood that the processes shown in the above figures are not intended to indicate or limit the chronological order of the processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, e.g., in multiple modules.

The following are embodiments of the apparatus of the present invention that may be used to perform embodiments of the method of the present invention. For details which are not disclosed in the embodiments of the apparatus of the present invention, reference is made to the embodiments of the method of the present invention.

The response module 602 is configured to obtain a user ID in response to a search operation of a user.

The data module 604 is configured to obtain user behavior data through the user ID, where the user behavior data includes long-term behavior data and short-term behavior data.

The scoring module 606 is used to input user behavior data into the user-assisted search model to obtain a ranking score.

The assistance module 608 is used to provide assistance information to the user's search operation according to the ranking score.

According to the device for assisting in searching, the auxiliary searching information is ranked and scored through the auxiliary model, and then the auxiliary searching information is displayed in sequence through the scored data, so that the requirements of users can be matched more accurately, and personalized auxiliary searching is realized.

In an exemplary embodiment of the present disclosure, further comprising: a model module (not shown) is used to build the auxiliary search model from historical user behavior data.

Referring now to FIG. 7, a block diagram of an electronic device 70 suitable for use in implementing embodiments of the present application is shown. The electronic device shown in fig. 7 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.

As shown in fig. 7, the computer system 70 includes a Central Processing Unit (CPU)701, which can perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)702 or a program loaded from a storage section 708 into a Random Access Memory (RAM) 703. In the RAM 703, various programs and data necessary for the operation of the system 70 are also stored. The CPU701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. An input/output (I/O) interface 705 is also connected to bus 704.

The following components are connected to the I/O interface 705: an input portion 706 including a keyboard, a mouse, and the like; an output section 707 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 708 including a hard disk and the like; and a communication section 709 including a network interface card such as a LAN card, a modem, or the like. The communication section 709 performs communication processing via a network such as the internet. A drive 710 is also connected to the I/O interface 705 as needed. A removable medium 711 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 710 as necessary, so that a computer program read out therefrom is mounted into the storage section 708 as necessary.

In particular, according to an embodiment 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 can be downloaded and installed from a network through the communication section 709, and/or installed from the removable medium 711. The computer program executes the above-described functions defined in the system of the present application when executed by the Central Processing Unit (CPU) 701.

It should be noted that the computer readable medium shown in the present application may 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 application, 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 this application, 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 application. 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 units described in the embodiments of the present application may be implemented by software or hardware. The described units may also be provided in a processor, and may be described as: a processor includes a transmitting unit, an obtaining unit, a determining unit, and a first processing unit. The names of these units do not in some cases constitute a limitation to the unit itself, and for example, the sending unit may also be described as a "unit sending a picture acquisition request to a connected server".

As another aspect, the present application also provides a computer-readable medium, which 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: responding to the search operation of the user and acquiring a user ID; acquiring user behavior data through a user ID, wherein the user behavior data comprises long-term behavior data and short-term behavior data; inputting user behavior data into a user-assisted search model to obtain a ranking score; and providing auxiliary information for the search operation of the user according to the ranking scores.

Those skilled in the art will appreciate that the modules described above may be distributed in the apparatus according to the description of the embodiments, or may be modified accordingly in one or more apparatuses unique from the embodiments. The modules of the above embodiments may be combined into one module, or further split into multiple sub-modules.

Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiment of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the embodiment of the present invention.

From the foregoing detailed description, those skilled in the art can readily appreciate that the method, apparatus, electronic device, and computer-readable medium for assisting a search in accordance with embodiments of the present invention have one or more of the following advantages.

According to some embodiments, the method for assisting in searching provided by the invention ranks and scores the search auxiliary information through the auxiliary model, and then displays the auxiliary information in sequence through the scored data, so that the user requirements can be matched more accurately, and personalized auxiliary searching is realized.

According to other embodiments, the method for assisting in searching establishes the user-assisted searching model through the category preference data, the calculation scheme formula is simple and has an obvious effect, the method is very suitable for calculating the category preference of the user, the future personalization is ubiquitous, the most frequent personalization in e-commerce is the user category preference, and the method can be used for supporting business requirements in a large quantity subsequently.

According to still other embodiments, the method for assisting search of the present invention establishes a user-assisted search model through gender preference data, and in the prior art, the characteristics of a word are utilized to perform mining.

Exemplary embodiments of the present invention are specifically illustrated and described above. It is to be understood that the invention is not limited to the precise construction, arrangements, or instrumentalities described herein; on the contrary, the invention is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.

In addition, the structures, the proportions, the sizes, and the like shown in the drawings of the present specification are only used for matching with the contents disclosed in the specification, so as to be understood and read by those skilled in the art, and are not used for limiting the limit conditions which the present disclosure can implement, so that the present disclosure has no technical essence, and any modification of the structures, the change of the proportion relation, or the adjustment of the sizes, should still fall within the scope which the technical contents disclosed in the present disclosure can cover without affecting the technical effects which the present disclosure can produce and the purposes which can be achieved. In addition, the terms "above", "first", "second" and "a" as used in the present specification are for the sake of clarity only, and are not intended to limit the scope of the present disclosure, and changes or modifications of the relative relationship may be made without substantial technical changes and modifications.

Claims

1. A method for assisting a search, comprising:

responding to the search operation of the user and acquiring a user ID;

acquiring user behavior data through the user ID, wherein the user behavior data comprises long-term behavior data and short-term behavior data;

inputting the user behavior data into a user-assisted search model to obtain a ranking score; and

providing auxiliary information for the search operation of the user according to the ranking score, wherein the auxiliary information comprises information displayed in a drop-down box of a search bar of the search operation;

wherein the user-assisted search model is determined by:

extracting personalized feature data through historical user behavior data, wherein the personalized feature data comprises category preference data;

establishing the user-assisted search model using the category preference data;

the building the user-assisted search model using the category preference data comprises:

acquiring historical user behavior data;

the unit data of the stock quantity and the corresponding category data are extracted through historical user behavior data;

extracting first preset behavior related data and second preset behavior related data through stock unit data and corresponding category data;

obtaining the score of the category through the first preset behavior related data, the second preset behavior related data and the corresponding weight; and

sorting all the categories according to scores to obtain preset category numbers;

obtaining the score of the category by the following formula:

wherein, f (uuid)_M,cid3_N) For the user uuid_MAt cid3_NCategory score under category N, t being the current time, N₁Is the number of the first predetermined actions, alpha is the weight of the number of the first predetermined actions, t0 is the occurrence time of the first predetermined actions, n₂Is the number of the second predetermined behaviors, δ is the weight of the number of the second predetermined behaviors, β is the weight of the second predetermined behaviors, and t1 is the occurrence time of the second predetermined behaviors.

2. The method of claim 1, wherein the personalized features data further comprises gender preference data and recent search data.

3. The method of claim 2, wherein the method further comprises:

and establishing the user-assisted search model through the gender preference data.

4. The method of claim 3, wherein said building said user-assisted search model from said gender preference data comprises:

and establishing the user-assisted search model through the user portrait and the gender preference data.

5. The method of claim 2, wherein the method further comprises:

and establishing the user-assisted search model through the latest search data.

6. An apparatus for assisting a search, comprising:

the response module is used for responding to the search operation of the user to acquire the user ID;

the data module is used for acquiring user behavior data through the user ID, and the user behavior data comprises long-term behavior data and short-term behavior data;

the scoring module is used for inputting the user behavior data into a user auxiliary search model to obtain a ranking score; and

the auxiliary module is used for providing auxiliary information for the search operation of the user according to the ranking score, wherein the auxiliary information comprises information displayed in a drop-down box of a search bar of the search operation;

wherein the user-assisted search model is determined by:

extracting personalized feature data through historical behavior data, wherein the personalized feature data comprises category preference data;

establishing the user-assisted search model using the category preference data;

acquiring historical user behavior data;

obtaining the score of the category by the following formula:

wherein, f (uuid)_M,cid3_N) For the user uuid_MAt cid3_NCategory score under category N, t being the current time, N₁Is the number of the first predetermined actions, α is the weight of the number of the first predetermined actions, and t0 is the first predetermined actionsTime of occurrence of (n)₂Is the number of the second predetermined behaviors, δ is the weight of the number of the second predetermined behaviors, β is the weight of the second predetermined behaviors, and t1 is the occurrence time of the second predetermined behaviors.

7. An electronic device, comprising:

one or more processors;

storage means 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 1-5.

8. 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 1-5.