CN101661487A - Method and system for searching information items - Google Patents
Method and system for searching information items Download PDFInfo
- Publication number
- CN101661487A CN101661487A CN200810213334A CN200810213334A CN101661487A CN 101661487 A CN101661487 A CN 101661487A CN 200810213334 A CN200810213334 A CN 200810213334A CN 200810213334 A CN200810213334 A CN 200810213334A CN 101661487 A CN101661487 A CN 101661487A
- Authority
- CN
- China
- Prior art keywords
- user
- evaluation
- information
- item
- emotion
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Landscapes
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The invention provides a method and a system for searching information items. The method comprises the following steps: receiving the query for an object; based on the query, searching information items related to the object to acquire the information item set of the object, wherein each information item is related to a user; extracting the historical information item record of each related user in the information item set; based on the historical information item records of all users, calculating the effective weight of each user for the object; based on the effective weight, sorting all information items in the information item set of the object; and outputting the ordered information items as searching results.
Description
Technical field
Present invention relates in general to search, more specifically, the present invention relates to assessment is searched for to object method and related system and computer program item of information.
Background technology
Along with the continuous development of computer technology and network technology, a large amount of information are transmitted by computer network.Internet universal causes the growth by leaps and bounds of information sharing technology, and the internet also more and more is penetrated in people's the life, when people share information on the internet, also faces the own required information that how to retrieve effectively from so huge quantity of information.
Present widely used search engine promptly is intended to assisting users to be retrieved from bulk information, so that the user can search and retrieve its required various information quickly and easily.Because the quantity and the numerous types of internet information content, therefore the number as a result that search engine searched is quite a few, useful is by Search Results being sorted, making that the most relevant, most important result comes the foremost, so that the user can obtain information more quickly.Therefore, improvement and optimization to various search engine techniques all are reacted directly in the ordering to Search Results.
The type of internet information content is abundant, wherein along with the raising of web utilization rate and the quick expansion of ecommerce, increasing people searches information that is associated with various products (such as clothes, electronic product etc.) or the information that is associated with various services (service items such as for example food and drink, lodging, tourism) on web.These information generally include some and have used or bought evaluation and the suggestion of the user of these products or service to them, and these are estimated and suggestion in fact all is very useful for potential client, goods producer and online merchants.The information project that how these search is obtained or directly obtain from corresponding data storage is a difficult problem of information retrieval field according to certain rank order, be shown to the user in more effective mode.Comparatively common method is that evaluation of user and suggestion were sorted according to the time at present, also is about to nearest evaluation of time and is placed on topmost, and the past more next time more early.But, thisly can't embody the importance of various evaluations, the also information that promptly can't help the user to obtain to expect effectively according to the method for time-sequencing.
Fig. 1 schematically shows in the prior art screenshot capture that Search Results that the user is estimated shows.As can be seen from Figure 1, always having 473 users estimates, the wherein star scoring that specific products or service is provided according to each bar evaluation (comprise 1 star, 2 stars ..., 5 stars) shown intuitively that with histogram the user of each star scoring estimates number, and provided concrete evaluation number by the numeral in the bracket on the right side of histogram.In addition, giving average user estimates.The latter half at Fig. 1 has shown " the most useful user estimates ", and as can be seen from the figure, 759 philtrums have 716 people to think that its listed down evaluation of great use.
Above-mentioned prior art is to come grade assessment is carried out in various evaluations and suggestion by obtain feedback information from the user, thereby the demonstration of this " the most useful user estimates " is provided.Fig. 2 schematically shows the screenshot capture of gathering field feedback in the prior art.For example after the user reads the evaluation or suggestion that other users provide, can require the user that this evaluation or suggestion are given a mark or graded, thereby be that this evaluation or suggestion obtain a score value.In Fig. 2, click by the user by two buttons " Yes " and " No " simply and select this evaluation whether useful it.Based on statistics to these field feedbacks, can be to estimating or the suggestion ordering of giving a mark, with score value the highest come the foremost, score value is low comes the back.But there is following defective at least in said method: its needs are artificial to participate in marking or grading, and its accuracy depends on the response rate of user feedback basically.And this, feedback answer person few network presence many for present reader is difficult to add up effectively.
Therefore, need a kind ofly this type of information item to be searched for effectively, Search Results is sorted but not need the method for user feedback, make item of information most important to the user, most worthy is come the foremost, thereby help the more direct information that obtains product or service fast and effectively of user, also promptly just can obtain useful information as much as possible from a spot of item of information.
Summary of the invention
Therefore, in order to overcome the deficiencies in the prior art, the invention provides a kind of method and related system and computer program that item of information is searched for, it is suitable for method that the various assessments of product or service are searched for especially.
According to an aspect of the present invention, provide a kind of method that item of information is searched for, comprising: receive inquiry object; Based on this inquiry, search for the item of information relevant to obtain the collection of information items of object with described object, wherein each item of information is associated with a user; Extract the historical information item records of each related user in this collection of information items; Based on all users' historical information item records, calculate the validity weight of each user at described object; Based on this validity weight, all items of information in the collection of information items of described object are sorted; And with the ordering item of information export as Search Results.
According to a further aspect in the invention, provide a kind of system that item of information is searched for, comprising: receiving unit receives the inquiry to object; Search component based on this inquiry, is searched for the item of information relevant with described object to obtain the collection of information items of object, and wherein each item of information is associated with a user; Extraction assembly extracts the historical information item records of each related user in this collection of information items; The validity weight calculation component based on all users' historical information item records, is calculated the validity weight of each user at described object; Sequencing assembly based on this validity weight, sorts to all items of information in the collection of information items of described object; And output precision, the item of information of ordering is exported as Search Results.
According to a further aspect in the invention, also provide a kind of computer program that comprises the computer program code that is used to carry out the method according to this invention.
Utilize the present invention, the user comes the more forward position of information project tabulation usually to its most worthy, item of information that reliability is the highest when product or service are searched for.
Further, search ordering method of the present invention can use separately at the item of information in database or the data storage, also can be used in combination with any existing search engine, optimizes the demonstration to its Search Results.
Description of drawings
With reference to after the detailed description below in conjunction with accompanying drawing, it is more obvious that feature of the present invention, advantage and others will become, wherein in the accompanying drawings:
Fig. 1 shows in the prior art screenshot capture that Search Results that the user is estimated shows;
Fig. 2 shows the screenshot capture of gathering field feedback in the prior art;
Fig. 3 shows the process flow diagram of the method according to this invention;
Fig. 4 shows the Organization Chart according to system of the present invention; And
Fig. 5 shows and can realize computer system of the present invention.
Note that if exist, identical reference marker is represented identical parts in whole accompanying drawings.
Embodiment
In the following detailed description, for the ease of complete understanding the present invention, the mode by example has illustrated many specific details.But those skilled in the art can be very clear, and the present invention also can not need these details just can realize.In addition, in order more clearly to explain the present invention, in some examples, known method, processing, element and circuit only are to have carried out describing synoptically, and do not describe in detail.Below in conjunction with accompanying drawing the present invention is explained in more detail and illustrates.Should be appreciated that drawings and Examples of the present invention only are used for exemplary effect, be not to be used to limit protection scope of the present invention.
The present invention relates to the item of information of various objects is searched for and sorted.Wherein, " object " can comprise various tangible or invisible products, product feature and/or service, includes but not limited to, for example clothes, digital product, hotel, food and drink, tourism or the like.Can provide relevant evaluation at all these objects, the item of information of these objects for example can be to use or buy evaluation or the suggestion of the user of this series products or service to them.
Below will describe method and system of the present invention by instantiation, wherein in this example, we are with to the evaluation of various products or service or the suggestion example as item of information.But, it will be understood by those skilled in the art that to the invention is not restricted to this, but can be applied in the sort method to the various items of information that obtain by the search of any approach.
In existing various electronic business transaction website; for a product or service; usually can provide and use/bought this product or enjoyed the evaluation information of the user of this service to this product or service, these information can help the user to judge whether to be fit to select this product or service.But, along with the growth of evaluation information quantity, especially integrate under the situation of evaluation information of separate sources at need, can reach hundreds and thousands of for the quantity of the evaluation information of a product or service.The evaluation information of quantity is to be difficult to read one by one for the user like this.For this reason, the user wishes to obtain fast and effectively that reference value and objective appraisal information are arranged most.So, it be from described object being had than the use experience of horn of plenty or the evaluation information that the user delivered of experience that reference value and effectively evaluating are arranged for the user most, rather than professional writer or lack the evaluation information that the user of use experience delivers.And the user can be by the evaluation information acquisition of analysis user history to this class object to the use experience and the evaluation objectivity of described object.
Hence one can see that, and estimator's history evaluation is very useful for the evaluation criteria of analyzing this estimator.Can analyze by history evaluation, thereby the assessment as far as possible accurately at the evaluation of special object that it is provided is provided the specific user.
When assay person's history is estimated, we consider to overcome following problem, how to judge promptly whether the evaluation that the estimator provides is objective, if an estimator always is positive suggestion or always is negative suggestion then whether this estimator's suggestion valuable? be and how to discern this estimator the writer that businessman employs?
At the problems referred to above, we are that the user of the item of information that provides various objects (perhaps estimating) distributes a validity weight, the validity of the item of information of this object of reflection in this validity weight.The user is sorted to the evaluation of the object validity weight according to the user, thereby the item of information of most worthy is placed on the foremost, make the user can fast and effeciently obtain useful information.
Because the object coverage rate estimated is extensive, therefore need classify to the object of being estimated so as more in an organized way, the validity weight of distributing user more reasonably.In existing shopping website or online shopping mall, there has been classification usually to product or service, for example, can be divided into dress ornament, footwear bag, digital product, books, phonotapes and videotapes, household articles, food, hotel or the like classification roughly.The item of information of various products or service is associated with regard to its corresponding classification.The division of above-mentioned classification can reasonably distribute, be provided with flexibly according to the quantity of product that is provided or service, type etc.Above-mentioned classification can be pre-set, also can after adjust, for example when the model of certain series products, brand etc. increase, can such be segmented again, perhaps when the product number tails off, merge some classification.
In order to solve the problem that may exist in the above-mentioned reality, comprise three factors at least in the estimator's that the present invention proposes the validity weight: the object type number that this estimator estimated accounts for the ratio of total object type number; This estimator is inclined to consistent degree to the evaluation of each object type of commenting on the emotion of the overall evaluation; This estimator's evaluation is shared proportion in all are estimated.
Estimator's validity weight is the funtcional relationship of these three factors.In one embodiment, (Ri is Oj) by classification ratio R to the validity weight A of special object classification Oj for a certain estimator Ri
Category, emotion is inclined to consistent degree Con
SentiWith evaluation proportion R
CommentThe company of these three factors takes advantage of expression, also promptly:
A(Ri，Oj)＝R
category*Con
senti*R
comment (1)
It will be understood by those skilled in the art that the funtcional relationship that can also use other represents this validity weight, such as linear combination of three factors etc.
Classification ratio R
CategoryNumber shared ratio in total object type number of representing the object type that specific estimator Ri was estimated.From its definition, can obtain its computing method intuitively, that is:
R
CategoryThe number (2) of the object type of the number of the object type that=Ri estimated/total
Classification ratio R
CategoryThe writer that the factor can mask businessman is effectively employed.This be because, the writer that common businessman is employed often only estimates at a kind of object (product or service) of or a few classification, because the number of its object type of estimating is less, therefore, ratio occupied in all object type is also less, thereby can be reflected in this estimator's the validity weight by the classification scale factor.
Emotion is inclined to consistent degree Con
SentiRepresent the emotion tendency consistance of specific estimator to the evaluation and the overall evaluation of each object type of commenting on.When the assay person is inclined to the emotion of each evaluation object classification, uses natural language processing and analyze, and it is expressed as emotion tendency vector S entiV (Ri).Along with computing machine and broad application of Internet, the accessible natural language text quantity of computing machine unprecedentedly increases, and natural language processing technique plays an important role at application such as the text mining of magnanimity information, information extraction, man-machine interactions.Natural language processing is started with from two aspects of syntax and semantics, extracts the analysis that content corresponding is carried out the meaning of a word and sentence justice from text, excavates the expressed suggestion of estimator.Alternatively, can give certain numerical value with estimator's suggestion, for example from-5 to 5 numerical value.Clearly, it will be understood by those skilled in the art that the numerical value that also can get other scopes represents.Wherein each element of emotion tendency vector is the evaluation suggestion numerical value of this estimator Ri to each object type.
May be from malevolence or irresponsiblely make evaluation at the above-mentioned estimator who has, provide positive or negative evaluation all the time, this evaluation is not worth basically.Therefore, by specific estimator's the evaluation and the emotion tendency of the overall evaluation are compared, can suppress above-mentioned phenomenon effectively.The emotion tendency of the overall evaluation is also represented by emotion tendency vector S entiV (all).SentiV (all) can be a statistical value, for example, gives up extremum mean value afterwards, perhaps only extracts the sample average afterwards of some.In one embodiment, the emotion of overall evaluation tendency vector S entiV (all) is represented by the average of all estimators' emotion tendency vector.
In one embodiment, the emotion of estimator Ri is inclined to consistent degree Con
SentiThe emotion that is calculated as this estimator Ri is inclined to the inner product of the emotion tendency vector of the vector and the overall evaluation, promptly
Con
senti＝SentiV(Ri)·SentiV(all) (3)
Clearly, if estimator Ri is consistent to the suggestion of each object type with most of estimator's suggestion, then emotion is inclined to consistent degree Con
SentiValue also higher.If for each object type, the suggestion of estimator Ri just is being always (score value is very high) or is being always negative (score value is very low), then compares in the emotion tendency with the overall evaluation, also promptly calculates after the inner product, its numerical value is also little, thereby can be inclined to consistent degree Con by emotion
SentiThe factor is reflected in the validity weight of this estimator Ri.
Estimate proportion R
CommentEvaluation shared proportion in all are estimated of representing specific estimator.In one embodiment, estimate proportion and be made up of two parts, estimator Ri accounts for the proportion of this estimator to the evaluation number of all object type to the evaluation number of object type under the described evaluation object; And this estimator accounts for the proportion of all estimators to the evaluation number of object type under the described evaluation object to the evaluation number of object type under the described evaluation object.In one embodiment, can calculate evaluation proportion R by following formula
Comment:
Wherein, coefficient lambda
1And λ
2Be to be used for these two parts of balance at evaluation proportion R
CommentIn shared proportion, it can rule of thumb adjust λ
1And λ
2Value.From above-mentioned formula as can be seen, with himself the evaluation number of all object type is compared and the evaluation number of Oj classification is compared by estimator Ri is done the evaluation number of appearing to the Oj classification with all estimators, can judge that whether this estimator Ri lays particular emphasis on certain object type, that is to say it is not the authoritative sources in this object type field.At the evaluation proportion R of Ri at the Oj object type
CommentWhen high, the evaluation of Oj is had more value, be reflected in that it can increase the numerical value of validity weight on the validity weight with regard to meaning Ri.
The validity weight relevant with item of information of embodiments of the present invention in one application described hereinbefore, based on this validity weight, can the item of information that search be sorted, thereby the item of information that the validity weight is high comes the front, the item of information that the validity weight is low comes the back, make the user only need browse a spot of information, just can obtain desired Useful Information fast.
The method flow of searching for according to the item of information to object of embodiment of the present invention is described below with reference to Fig. 3.In this flow process, still with to describing such as the evaluation of various products or service object or suggestion example as item of information.
As shown in Figure 3, in step S300, begin this treatment scheme.
In step S302, receive the inquiry of user to evaluation object Oj.
In step S304, based on user's inquiry, search for the item of information relevant to obtain the collection of information items of this evaluation object with evaluation object Oj, wherein each item of information is associated with a user, also be that each item of information is generated by user associated therewith, this user is also referred to as the estimator.In one embodiment, for example, when being used in combination with search engine, evaluation object inquiry according to user's input among the step S302, the item of information that from each database, is associated with this evaluation object based on keyword search, also promptly to the evaluation or the suggestion of this evaluation object, thereby obtain the collection of information items of object Oj.From these evaluations or suggestion, can obtain to generate user's's (being the estimator) of this evaluation or suggestion ID, thereby form estimator's set of this evaluation object.In another embodiment, when various objects have carried out the branch time-like according to previously described mode, the evaluation of each object can be associated with the classification under it in data storage, also promptly index is carried out in the object evaluation, thereby can search out collection of information items and estimator's set of institute's query object fast by classification.
In step S306, each related user's historical information item records in the set of information extraction item is also promptly extracted the history of each estimator in estimator's set and is estimated record.For example, according to estimator's ID, all once gave the evaluation record of appearing to retrieve this estimator.
In following step, the validity weight of each estimator Ri at the evaluation object Oj that is inquired about will be calculated.
In step S308, the classification of all objects that identification and evaluation person Ri was estimated, the number of objects of statistics classification.This result can be applied in the classification scale factor in subsequently the calculating validity weight.
In step S310, calculate the validity weight of estimator Ri at the evaluation object Oj that is inquired about.As can be known, the validity weight of embodiment of the present invention comprises three factors at least: classification ratio R from above describe
Category, the object type number that estimator Ri estimated accounts for the ratio of total object type number; Emotion is inclined to consistent degree Con
Senti, estimator Ri is inclined to consistent degree to the evaluation of each object type of commenting on the emotion of the overall evaluation; Estimate proportion R
Comment, the evaluation of estimator Ri is shared proportion in all are estimated.In this step, according to the formula that provides previously, calculate this three factors respectively, for example three factors are connected then and multiply by the value that obtains the validity weight.
Wherein, classification ratio R
CategoryBe calculated as: the number that uses the object type that Ri estimated obtain in step S308 is divided by total classification number, and this total classification number can be hereinbefore described definite when all objects are classified.
Emotion is inclined to consistent degree Con
SentiCalculating relate to: at first use natural language processing technique assay person to the emotion in the evaluation of each object type tendency, for example give the degree that certain numerical value is represented its emotion tendency.In one embodiment, positive evaluation Example is as with 1 to 5 numeric representation, and negative evaluation Example is as with-5 to-1 numeric representation, 0 expression neutrality.Then, for each estimator Ri, set up the emotion tendency vector S entiV (Ri) of this estimator Ri.In general, the dimension of the emotion of estimator Ri tendency vector is the number of the object type estimated of Ri, and each element is the evaluation suggestion numerical value of this estimator Ri to corresponding object type.Because each estimator not necessarily can estimate all object type, make like this that therefore the dimension of emotion tendency vector separately is not corresponding.For unified these vectors and convenience of calculation afterwards in form, it is the number of total object type that the dimension of all emotion tendency vectors all unify, does not make the object type of any evaluation for estimator Ri, and its elements corresponding value is 0.Then, after having obtained all estimators' emotion tendency vector, in one embodiment, can ask on average, to obtain the emotion tendency vector S entiV (all) of the overall evaluation all these vectors.Clearly, it will be appreciated by those skilled in the art that the account form that can also adopt other finds the solution the emotion tendency vector of the overall evaluation according to all estimators' emotion tendency vector.At last, the emotion tendency vector S entiV (Ri) of estimator Ri and the emotion tendency vector S entiV (all) of the overall evaluation are carried out inner product, be inclined to consistent degree Con with the emotion that obtains estimator Ri
Senti
Estimate the calculating R of proportion
CommentComprise: calculating estimator Ri accounts for the proportion of this estimator Ri to the evaluation number of all object type to the evaluation number of the classification of evaluation object Oj; And calculate estimator Ri the evaluation number of the classification of evaluation object Oj is accounted for the proportion of all estimators to the evaluation number of the classification of evaluation object Oj.Wherein, estimator Ri is exactly the number of all evaluations of the given mistake of this estimator Ri to the evaluation number of all object type basically.All estimators to the evaluation number of the classification of evaluation object Oj be all estimators in estimator's set of in step S304, obtaining above-mentioned to the evaluation number of the classification of evaluation object Oj, it is that all of classification of object Oj are estimated numbers basically.Then, calculate evaluation proportion R according to aforementioned formula (4)
Comment, experience factor λ
1And λ
2Can these two parts of balance result estimate proportion R
CommentIn shared proportion.
After calculating above-mentioned three factors, just can obtain the validity weight of estimator Ri to object Oj.
Then, in step S312, judge whether that all estimators' validity weight all finishes as calculated.
If judged result is a "No", then handle and turn back to step S308, continue next estimator is calculated, and continue to carry out later step.
If judged result is a "Yes", then handles and advance to step S314.
In step S314, all validity weights of estimating based on each estimator of selected evaluation object Oj are sorted, wherein the evaluation that the validity weight is high has more forward position.By considering the validity weight of estimator, can obtain the effect that better sorts at specific evaluation object.
At last, in step S316, will export as Search Results through the item of information of ordering.In one embodiment, for example can on display screen, Search Results be presented to the user, wherein for example can only show the preceding 10-50 bar item of information after sorting, so that the user browses.It will be understood by those skilled in the art that and also can adopt various graphical informations to present to the user.
Alternatively, the ordering to Search Results of the present invention can also be further considered other factors except considering estimator's validity weight, for example provides the time of evaluation.For the object (product or service) of some classification, the evaluation that provides early may be worth little for current evaluation object, so after its position when ordering leans on.
Fig. 4 schematically shows the system schematic block diagram of realization according to the searching method of one embodiment of the present invention.Wherein, reference number 400 expressions comprise in this search system 400: receiving unit 401, search component 402, extraction assembly 404, validity weight calculation component 406, sequencing assembly 408 and output precision 409 according to the search system of embodiment of the present invention.Wherein, also show data repository 410 in Fig. 4, this data repository 410 can be included among the ordering system 400, also can be used as its outside individual components, even this data repository 410 can also be distributed in the computer network with distributed.
As shown in Figure 4, receiving unit 401 is for example from the inquiry of user's reception to object.Search component 402 is for example searched for the item of information relevant with the object of being inquired about based on the inquiry that receives from data repository 410, the collection of information items of query object to obtain, and wherein each item of information is associated with a user.Search component 402 is all associated user of search from the collection of information items of obtaining also, form user's set.Extraction assembly 404 for each user's extraction historical information item records separately in this user's set, for example also obtains these historical information item records then from data repository 410.Then, validity weight calculation component 406 is calculated the validity weight of each user to special object.Then, based on the validity weight of being calculated, sorted by all items of information in the collection of information items of 408 pairs of these special objects of sequencing assembly, wherein, the item of information that the validity weight is high is arranged in more forward position, thus the ordering effect that is improved.At last, will export by output precision 409 as Search Results, for example present to the user by display screen through the item of information of ordering.
In a kind of application of the present invention, for example, the item of information of object is evaluation or the suggestion of user to this object.In this application, validity weight calculation component 406 can comprise following sub-component at least: classification ratio computation module 412, emotion are inclined to consistent degree computation module 414, are estimated proportion computation module 416, function assembly 418 and recognizer component 420.
Wherein, classification ratio computation module 412, emotion are inclined to consistent degree computation module 414 and are estimated proportion computation module 416 and calculate the corresponding factor according to method described above respectively.Be inclined in emotion and also comprise natural language processing assembly (not shown) in the consistent degree computation module 414, this natural language processing assembly can be analyzed the emotion tendency of expressing in each item of information according to syntax and semantics, and it is expressed as emotion tendency vector.
Classification under recognizer component 420 identifying objects, also promptly discern the classification of each object in the historical information item records of extracting by extraction assembly 404, thereby count the number of relevant object type or the evaluation number of related category, to be used for follow-up use or calculating.For example, in classification ratio computation module 412, be used to calculate the ratio that object type number that estimator Ri estimated accounts for total object type number; Be inclined in the consistent degree computation module 414 in emotion, the object type that can use recognizer component 420 to discern in each item of information is inclined to vector to assist setting up emotion; And in estimating proportion computation module 416, be used to calculate estimator Ri to evaluation number of the classification of estimator's object Oj or the like.
In optional embodiment, sequencing assembly 408 can also have other inputs, time of generating of each item of information for example, thus sequencing assembly 408 can further sort to item of information according to the rise time.
Fig. 4 only shows the example that can realize a kind of ordering system of the present invention.It will be understood by those skilled in the art that on the specific implementation of each components/modules, when especially realizing each functions of modules, can have the plurality of optional scheme by software.For example, can with shown in search component 402 and extraction assembly 404 merge in the same module.
Below, will be described with reference to Figure 5 and can realize computer system of the present invention.Fig. 5 has schematically shown the block diagram that can realize computer system according to the embodiment of the present invention.
Computer system shown in Fig. 5 comprises CPU (CPU (central processing unit)) 501, RAM (random access memory) 502, ROM (ROM (read-only memory)) 503, system bus 504, hard disk controller 505, keyboard controller 506, serial interface controller 507, parallel interface controller 508, display controller 509, hard disk 510, keyboard 511, serial external unit 512, parallel external unit 513 and display 514.In these parts, what link to each other with system bus 504 has CPU 501, RAM 502, ROM 503, hard disk controller 505, keyboard controller 506, serial interface controller 507, parallel interface controller 508 and a display controller 509.Hard disk 510 links to each other with hard disk controller 505, keyboard 511 links to each other with keyboard controller 506, serial external unit 512 links to each other with serial interface controller 507, and parallel external unit 513 links to each other with parallel interface controller 508, and display 514 links to each other with display controller 509.
Each functions of components all is well-known in the present technique field among Fig. 5, and shore and structure shown in Figure 5 also are conventional.The described block diagram of Fig. 5 illustrates just to the purpose of example, is not to be limitation of the present invention.In some cases, can add or reduce wherein some parts as required.
Search of the present invention and Search Results is carried out sort method can use separately at the item of information in database or the data storage also can be used in combination with any existing search engine, optimizes the demonstration to its Search Results.From above description as can be known, the process that item of information is searched for can realize with any known algorithm, process, mode, and should realization itself not belong to scope of the present invention.
In addition, it will be appreciated by those skilled in the art that, although various aspects of the present invention can be used as block diagram, process flow diagram or use other diagram expression to be illustrated and to describe, but be appreciated that these modules described here, assembly, equipment, system, technology or method can realize to make up as hardware, software, firmware, special circuit or logic, common hardware or the controller of limiting examples or other computing equipment or its.
Although instruction of the present invention is to describe in the concrete context of implementing, it will be apparent to one skilled in the art that under the situation that does not break away from spirit of the present invention, can make amendment and change each embodiment of the present invention.Description in this instructions is only used for illustrative, and should not be considered to restrictive.Scope of the present invention only is subjected to the restriction of appended claims.
Claims (20)
1. method that item of information is searched for comprises:
Reception is to the inquiry of object;
Based on this inquiry, search for the item of information relevant to obtain the collection of information items of object with described object, wherein each item of information is associated with a user;
Extract the historical information item records of each related user in this collection of information items;
Based on all users' historical information item records, calculate the validity weight of each user at described object; And
Based on this validity weight, all items of information in the collection of information items of described object are sorted;
The item of information of ordering is exported as Search Results.
2. method according to claim 1, wherein said item of information are the evaluation of user to described object.
3. method according to claim 1 and 2 also comprises:
Discern the affiliated object type of described object.
4. method according to claim 2, wherein calculate each user and comprise at the validity weight of described object:
Calculate the ratio that object type number that this user estimated accounts for total object type number;
Calculate this user the evaluation of each object type of estimating is inclined to consistent degree with the emotion of the overall evaluation;
Calculate this evaluation of user shared proportion in all are estimated; And
Classification ratio, emotion are inclined to consistent degree and are estimated proportion and multiply by mutually and obtain described validity weight.
5. method according to claim 4 is wherein used natural language processing analysis user from the user estimates the emotion of each object type is inclined to, and it is expressed as emotion tendency vector.
6. method according to claim 5 is wherein calculated this user and the evaluation of each object type of estimating is inclined to consistent degree with the emotion of the overall evaluation is comprised: this user's emotion tendency vector is inclined to vector with the emotion of the overall evaluation carries out inner product.
7. method according to claim 6, the emotion tendency vector of the wherein said overall evaluation is represented by the average of all users' emotion tendency vector.
8. according to the arbitrary described method of claim 4-7, wherein calculate described evaluation proportion according to following factor: this user accounts for the proportion of this user to the evaluation number of all object type to the evaluation number of object type under the described object; And this user accounts for the proportion of all users to the evaluation number of object type under the described object to the evaluation number of object type under the described object.
9. according to the arbitrary described method of claim 1-8, wherein that the validity weight is high item of information comes the front.
10. according to the arbitrary described method of claim 1-9, wherein search step comprises the search item of information relevant with described object from least one data repository.
11. the system that item of information is sorted comprises:
Receiving unit receives the inquiry to object;
Search component based on this inquiry, is searched for the item of information relevant with described object to obtain the collection of information items of object, and wherein each item of information is associated with a user;
Extraction assembly extracts the historical information item records of each related user in this collection of information items;
The validity weight calculation component based on all users' historical information item records, is calculated the validity weight of each user at described object;
Sequencing assembly based on this validity weight, sorts to all items of information in the collection of information items of described object; And
Output precision is exported the item of information of ordering as Search Results.
12. system according to claim 11, wherein said item of information is the evaluation of user to described object.
13., also comprise according to claim 11 or 12 described systems:
Recognizer component is used to discern the affiliated object type of described object.
14. system according to claim 13, wherein the validity weight calculation component further comprises:
Classification ratio computation module calculates the ratio that object type number that this user estimated accounts for total object type number;
Emotion is inclined to consistent degree computation module, calculates this user the evaluation of each object type of estimating is inclined to consistent degree with the emotion of the overall evaluation;
Estimate the proportion computation module, calculate this evaluation of user shared proportion in all are estimated; And
Multiplier is inclined to described classification ratio, emotion consistent degree and estimates proportion and multiply by mutually and obtain described validity weight.
15. system according to claim 14 wherein is inclined in the consistent degree computation module in emotion, comprises the natural language processing assembly, is used for estimating the emotion tendency of analysis user to each evaluation object classification from the user, and it is expressed as emotion tendency vector.
16. system according to claim 15, wherein emotion is inclined to consistent degree computation module and is comprised this user's the emotion tendency vector and the emotion of the overall evaluation are inclined to the assembly that vector carries out inner product.
17. system according to claim 16, the emotion tendency vector of the wherein said overall evaluation is represented by the average of all users' emotion tendency vector.
18. according to the arbitrary described system of claim 14-17, wherein estimate the proportion computation module and calculate described evaluation proportion according to following factor: this user accounts for the proportion of this user to the evaluation number of all object type to the evaluation number of object type under the described object; And this user accounts for the proportion of all users to the evaluation number of object type under the described object to the evaluation number of object type under the described object.
19. according to the arbitrary described system of claim 11-18, wherein the item of information that sequencing assembly is high with the validity weight comes the front.
20. according to the arbitrary described system of claim 11-19, wherein search component is searched for the item of information relevant with described object from least one data repository.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2008102133341A CN101661487B (en) | 2008-08-27 | 2008-08-27 | Method and system for searching information items |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2008102133341A CN101661487B (en) | 2008-08-27 | 2008-08-27 | Method and system for searching information items |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN101661487A true CN101661487A (en) | 2010-03-03 |
| CN101661487B CN101661487B (en) | 2012-08-08 |
Family
ID=41789513
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN2008102133341A Expired - Fee Related CN101661487B (en) | 2008-08-27 | 2008-08-27 | Method and system for searching information items |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN101661487B (en) |
Cited By (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102314443A (en) * | 2010-06-30 | 2012-01-11 | 腾讯科技（深圳）有限公司 | Method for correcting search engine and system |
| CN102970352A (en) * | 2012-11-08 | 2013-03-13 | 北京小米科技有限责任公司 | Method and device for providing information |
| WO2013034043A1 (en) * | 2011-09-05 | 2013-03-14 | 腾讯科技（深圳）有限公司 | Search ranking method and system for community users |
| CN103197974A (en) * | 2012-01-05 | 2013-07-10 | 国际商业机器公司 | Method and system of improving organizational agility across multiple computing domains |
| CN103577988A (en) * | 2012-07-24 | 2014-02-12 | 阿里巴巴集团控股有限公司 | Method and device for recognizing specific user |
| CN103646092A (en) * | 2013-12-18 | 2014-03-19 | 孙燕群 | SE (search engine) ordering method based on user participation |
| CN103853948A (en) * | 2012-11-28 | 2014-06-11 | 阿里巴巴集团控股有限公司 | User identity recognizing and information filtering and searching method and server |
| CN104281619A (en) * | 2013-07-11 | 2015-01-14 | 鸿富锦精密工业（深圳）有限公司 | System and method for ordering search results |
| TWI479439B (en) * | 2011-06-22 | 2015-04-01 | Rakuten Inc | Information processing apparatus, information processing method, information processing program product and recording medium with information processing program |
| CN104516897A (en) * | 2013-09-29 | 2015-04-15 | 国际商业机器公司 | Method and device for sorting application objects |
| CN104866474A (en) * | 2014-02-20 | 2015-08-26 | 阿里巴巴集团控股有限公司 | Personalized data searching method and device |
| CN105051776A (en) * | 2013-03-25 | 2015-11-11 | 索尼公司 | Information processing device, information processing system, and information processing method |
| CN105741130A (en) * | 2011-06-03 | 2016-07-06 | 脸谱公司 | Method and system for building and sending information streams |
| CN106776708A (en) * | 2016-11-17 | 2017-05-31 | 中国农业银行股份有限公司 | A kind of querying method and device |
| CN109474832A (en) * | 2018-11-28 | 2019-03-15 | 深圳市酷开网络科技有限公司 | A kind of information search sort method, intelligent terminal and storage medium |
| CN110119471A (en) * | 2019-04-03 | 2019-08-13 | 北京搜狗科技发展有限公司 | A kind of inspection method and device of search result consistency |
| CN111026956A (en) * | 2019-11-20 | 2020-04-17 | 拉扎斯网络科技（上海）有限公司 | Data list processing method and device, electronic equipment and computer storage medium |
| CN112559903A (en) * | 2020-12-15 | 2021-03-26 | 广州市贺氏办公设备有限公司 | Community member searching method, system, device and medium |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030101166A1 (en) * | 2001-11-26 | 2003-05-29 | Fujitsu Limited | Information analyzing method and system |
| JP2005512249A (en) * | 2001-12-13 | 2005-04-28 | コーニンクレッカ フィリップス エレクトロニクス エヌ ヴィ | Recommending media content on the media system |
| CN1818908A (en) * | 2006-03-16 | 2006-08-16 | 董崇军 | Feedbakc information use of searcher in search engine |
-
2008
- 2008-08-27 CN CN2008102133341A patent/CN101661487B/en not_active Expired - Fee Related
Cited By (27)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102314443B (en) * | 2010-06-30 | 2016-02-17 | 深圳市世纪光速信息技术有限公司 | The modification method of search engine and system |
| CN102314443A (en) * | 2010-06-30 | 2012-01-11 | 腾讯科技（深圳）有限公司 | Method for correcting search engine and system |
| CN105741130A (en) * | 2011-06-03 | 2016-07-06 | 脸谱公司 | Method and system for building and sending information streams |
| TWI479439B (en) * | 2011-06-22 | 2015-04-01 | Rakuten Inc | Information processing apparatus, information processing method, information processing program product and recording medium with information processing program |
| WO2013034043A1 (en) * | 2011-09-05 | 2013-03-14 | 腾讯科技（深圳）有限公司 | Search ranking method and system for community users |
| US9489428B2 (en) | 2011-09-05 | 2016-11-08 | Tencent Technology (Shenzhen) Company Limited | Search ranking method and system for community users |
| CN103197974A (en) * | 2012-01-05 | 2013-07-10 | 国际商业机器公司 | Method and system of improving organizational agility across multiple computing domains |
| CN103577988A (en) * | 2012-07-24 | 2014-02-12 | 阿里巴巴集团控股有限公司 | Method and device for recognizing specific user |
| CN103577988B (en) * | 2012-07-24 | 2017-08-04 | 阿里巴巴集团控股有限公司 | A kind of method and apparatus for recognizing specific user |
| CN102970352A (en) * | 2012-11-08 | 2013-03-13 | 北京小米科技有限责任公司 | Method and device for providing information |
| CN103853948A (en) * | 2012-11-28 | 2014-06-11 | 阿里巴巴集团控股有限公司 | User identity recognizing and information filtering and searching method and server |
| CN103853948B (en) * | 2012-11-28 | 2018-01-19 | 阿里巴巴集团控股有限公司 | The identification of user identity, the filtering of information and searching method and server |
| CN105051776A (en) * | 2013-03-25 | 2015-11-11 | 索尼公司 | Information processing device, information processing system, and information processing method |
| CN104281619A (en) * | 2013-07-11 | 2015-01-14 | 鸿富锦精密工业（深圳）有限公司 | System and method for ordering search results |
| CN104516897B (en) * | 2013-09-29 | 2018-03-02 | 国际商业机器公司 | A kind of method and apparatus being ranked up for application |
| CN104516897A (en) * | 2013-09-29 | 2015-04-15 | 国际商业机器公司 | Method and device for sorting application objects |
| CN103646092B (en) * | 2013-12-18 | 2017-07-04 | 孙燕群 | Based on the method for sequencing search engines that user participates in |
| CN103646092A (en) * | 2013-12-18 | 2014-03-19 | 孙燕群 | SE (search engine) ordering method based on user participation |
| CN104866474A (en) * | 2014-02-20 | 2015-08-26 | 阿里巴巴集团控股有限公司 | Personalized data searching method and device |
| CN106776708A (en) * | 2016-11-17 | 2017-05-31 | 中国农业银行股份有限公司 | A kind of querying method and device |
| CN109474832A (en) * | 2018-11-28 | 2019-03-15 | 深圳市酷开网络科技有限公司 | A kind of information search sort method, intelligent terminal and storage medium |
| CN109474832B (en) * | 2018-11-28 | 2021-02-02 | 深圳市酷开网络科技有限公司 | Information searching and sorting method, intelligent terminal and storage medium |
| CN110119471A (en) * | 2019-04-03 | 2019-08-13 | 北京搜狗科技发展有限公司 | A kind of inspection method and device of search result consistency |
| CN110119471B (en) * | 2019-04-03 | 2021-07-27 | 北京搜狗科技发展有限公司 | Method and device for checking consistency of search results |
| CN111026956A (en) * | 2019-11-20 | 2020-04-17 | 拉扎斯网络科技（上海）有限公司 | Data list processing method and device, electronic equipment and computer storage medium |
| CN112559903A (en) * | 2020-12-15 | 2021-03-26 | 广州市贺氏办公设备有限公司 | Community member searching method, system, device and medium |
| CN112559903B (en) * | 2020-12-15 | 2024-04-05 | 广州市贺氏办公设备有限公司 | Community member searching method, system, device and medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN101661487B (en) | 2012-08-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN101661487B (en) | Method and system for searching information items | |
| CN109191240B (en) | Method and device for recommending commodities | |
| CN108960719B (en) | Method and device for selecting products and computer readable storage medium | |
| CN102236663B (en) | Query method, query system and query device based on vertical search | |
| US8572116B2 (en) | System for recommending an article not present in an image | |
| CN111444334B (en) | Data processing method, text recognition device and computer equipment | |
| CN101408887B (en) | Recommending terms to specify body space | |
| CN106251174A (en) | Information recommendation method and device | |
| CN107633430A (en) | A kind of Method of Commodity Recommendation based on community of colony | |
| CN106339502A (en) | Modeling recommendation method based on user behavior data fragmentation cluster | |
| CN107077486A (en) | Affective Evaluation system and method | |
| Qu et al. | Matchmaking in reward-based crowdfunding platforms: A hybrid machine learning approach | |
| CN110175895B (en) | Article recommendation method and device | |
| CN1985255A (en) | Target advertising method and system using secondary keywords having relation to first internet searching keywords, and method and system for providing a list of the secondary keywords | |
| CN105574003A (en) | Comment text and score analysis-based information recommendation method | |
| CN104718547A (en) | Customer data analysis system | |
| CN102609422A (en) | Class misplacing identification method and device | |
| CN104252456A (en) | Method, device and system for weight estimation | |
| CN103455538B (en) | Information processing unit, information processing method and program | |
| CN106326318B (en) | Searching method and device | |
| CN113946752A (en) | Financial product recommendation method, device, equipment and storage medium based on big data | |
| CN113946754A (en) | User portrait based rights and interests recommendation method, device, equipment and storage medium | |
| CN111553765A (en) | E-commerce search sorting method and device and computing equipment | |
| Papadopoulos et al. | Multimodal Quasi-AutoRegression: Forecasting the visual popularity of new fashion products | |
| CN116308684A (en) | Online shopping platform store information pushing method and system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20120808 Termination date: 20150827 |
|
| EXPY | Termination of patent right or utility model |