CN111143694B - Information pushing method and device, storage device and program - Google Patents
Information pushing method and device, storage device and program Download PDFInfo
- Publication number
- CN111143694B CN111143694B CN201911423117.XA CN201911423117A CN111143694B CN 111143694 B CN111143694 B CN 111143694B CN 201911423117 A CN201911423117 A CN 201911423117A CN 111143694 B CN111143694 B CN 111143694B
- Authority
- CN
- China
- Prior art keywords
- information
- user
- pushing
- similarity
- semantic
- 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.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/33—Querying
- G06F16/335—Filtering based on additional data, e.g. user or group profiles
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Landscapes
- Engineering & Computer Science (AREA)
- Databases & Information Systems (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Computational Linguistics (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The invention relates to the technical field of knowledge pushing, in particular to an information pushing method and device, storage equipment and a program, wherein the method comprises the following steps: collecting user background information and information preference degree of a user and acquiring recommended information of an information base; performing feature vector representation on the user background information and the recommended information through feature words, and calculating similarity according to vector representation of the user background information and the recommended information to obtain a potential candidate pushing set; carrying out semantic analysis on the recommended information and calculating similarity in cooperation with the information preference degree of the user, and pushing a semantic candidate set which accords with the interest preference of the user; and carrying out weight proportion distribution on the potential candidate pushing set and the semantic candidate set to obtain a pushing result. The information pushing method provided by the invention effectively relieves the cold start problem and the sparse problem in knowledge pushing.
Description
Technical Field
The invention relates to the technical field of knowledge pushing, in particular to an information pushing method and device, storage equipment and a program.
Background
With the development of information technology, a large amount of information data is output, so that an information overload problem is caused, and how to find high-quality data from massive information becomes a great challenge. The information retrieval can be performed according to the display of related information of keyword search, the matching result is performed according to the single characteristic of the keyword and the information content, but the results obtained by searching the same keyword by people with different professional backgrounds are the same, so that how to quickly find information which a user is interested in and wants to know is a great challenge, the information pushing well realizes the process, and the personalized pushing of the user is generated by combining the user characteristic and the information content semantic information, thereby greatly improving the working efficiency of technological staff, saving the time cost of the self-searching, inquiring and screening processes, and bringing great convenience to the life of people by the development of the recommendation technology of audio, video, text and electronic commerce. Therefore, how to better mine user features and accurately characterize the requirements of intelligence semantic information has become a hotspot problem in the current knowledge pushing field.
Currently, recommendation technologies commonly used in the field of knowledge pushing mainly include content-based recommendation, collaborative filtering-based recommendation and model-based recommendation. The recommended technologies have certain defects:
content-based recommendation methods cannot mine potential interests of users, and only depend on the past preferences of users for certain recommended objects; the recommendation method based on coordination filtering only depends on the interaction behavior of a user and a recommendation object, and has the problems of cold start and sparse data; model-based recommendations are typically made by decomposing the original interaction matrix into two low-dimensional matrices by matrix decomposition, with cold starts and data sparseness.
Disclosure of Invention
The invention aims to solve the technical problems, and provides an information pushing method, an information pushing device, storage equipment and a program, which not only can dig potential interests of users, but also can effectively relieve the problems of cold start and data sparseness.
In order to achieve the technical effects, the invention comprises the following technical scheme: in a first aspect, the present invention provides an information pushing method, including the steps of:
collecting user background information and information preference degree of a user and acquiring recommended information of an information base;
performing feature vectorization representation on the user background information and the recommendation information through feature words, and obtaining potential candidate pushing sets after calculating similarity according to vector representation of the user background information and the recommendation information;
carrying out semantic analysis on the recommended information and calculating similarity in cooperation with the information preference degree of the user, and pushing a semantic candidate set which accords with the interest preference of the user;
the potential candidate pushing set and the semantic candidate set are subjected to weight proportion distribution to obtain a pushing result;
and pushing the obtained pushing result to the user.
In a second aspect, the present invention provides an intelligence pushing device,
an information acquisition unit: the information recommendation method comprises the steps of collecting user background information and user information preference degree and acquiring recommendation information of an information library;
a first calculation unit: the method comprises the steps of carrying out feature vectorization representation on user background information and recommendation information through feature words, and obtaining potential candidate pushing sets after similarity is calculated according to vector representation of the user background information and the recommendation information;
a second calculation unit: the semantic analysis module is used for carrying out semantic analysis on the recommended information and calculating similarity in cooperation with the information preference degree of the user, and pushing a semantic candidate set which accords with the interest preference of the user;
a third calculation unit: the method comprises the steps of carrying out weight proportion distribution on the potential candidate pushing set and the semantic candidate set to obtain a pushing result;
and a pushing unit: for pushing the obtained pushing result to the user.
In a third aspect, the present invention provides a computer readable storage medium having instructions stored therein, which when executed on a terminal device, cause the terminal device to perform the above-described information pushing method.
In a fourth aspect, the present invention provides a computer program product which, when run on a terminal device, causes the terminal device to perform the above-described knowledge pushing method.
By adopting the technical scheme, the method has the following beneficial effects: according to the information pushing method provided by the invention, the potential candidate items are calculated in advance through matching the user characteristics with the data of the information base, and then semantic analysis is carried out on the information content which is interested in the user according to the historical behavior of the user, so as to push the information content which accords with the interest characteristics of the user; and finally, carrying out weight proportion distribution on the candidate sets screened by the two to obtain a final pushing result. According to the invention, the semantic information of the interest and the intelligence content of the user is used for recommendation, a candidate set of possible results can be quickly searched, and then a final result is obtained according to the linear weight, so that the cold start problem and the sparse problem in knowledge pushing are effectively relieved.
Drawings
Fig. 1 is a general flow chart of an information pushing method according to an embodiment of the present invention.
Detailed Description
The invention will now be described in further detail with reference to specific examples thereof in connection with the accompanying drawings.
Examples:
the embodiment of the invention can be applied to pushing information in an information base (knowledge base) to specific technical personnel (the case of users), and the method can be executed by an information pushing device, wherein the device is executed by software and/or hardware and can be generally integrated in the information pushing device. The information pushing method specifically comprises the following steps:
step 1): collecting user background information and information preference degree of a user and acquiring recommended information of an information base;
and collecting user background information, such as research directions of user technicians, frequently-searched labels, and scientific research experiences in which the user technicians participate or mainly participate, and researching the preference degree of the related technicians on part of information in the information base.
Such as: u09 user is directed at the question-answering system research of specific scene, establishes the question-answering system to the internal information of enterprise and public institution, provides convenient quick information inquiry for it, provides information support for it.
['167','197','220','683','757','758']
Step 2): performing feature vectorization representation on the user background information and the recommended information through feature words, and calculating similarity according to vector representation of the user background information and the recommended information to obtain a potential candidate pushing set, wherein the method specifically comprises the following steps:
step 2.1) carrying out feature vectorization on the user background information through feature extraction, wherein the formula is as follows:
combining all user background information with word vectors through word frequency weights to obtain user feature vectors, wherein m represents feature numbers in user documents and words i Vector representation of feature words, word frequency tf (word i ) Refers to word i The number of occurrences in a document divided by the total number of words of the document.
Step 2.2) extracting feature words from the recommended information and carrying out vectorization expression, wherein the formula is as follows:
combining each information in the information library with word vectors through word frequency weights to obtain information feature vectors, wherein n represents a certain information feature number and word i Vector representation of feature word i, word frequency tf (word i ) Refers to word i The number of occurrences in a document divided by the total number of words of the document.
And 2.3) carrying out cosine similarity calculation on the vectors obtained in the two steps to obtain candidate pushing sets.
Calculating the distance between the user feature vector and the information feature vector through cosine similarity to obtain a candidate push set, u u,p Representing the semantic similarity of the user-documents, i.e. the user's preference value for the documents. u and p represent vector representations of the user and document, respectively.
Step 3): carrying out semantic analysis on the recommended information and calculating similarity in cooperation with the information preference degree of the user, and pushing a semantic candidate set which accords with the interest preference of the user, wherein the method specifically comprises the following steps:
and calculating the semantic similarity between the recommended information through a WMD (word mover distance) model according to the recommended information which is represented by the feature words in a feature vectorization way, and calculating the similarity according to the semantic similarity and the information preference degree of the user in a cooperative manner. WMD means word shift distance, which is a way to measure the distance between two text documents, and is used to determine the similarity between two texts, i.e. the larger the distance, the smaller the distance, the larger the similarity between the texts (it is to be noted that the larger the distance, the farther the distance, and the smaller the similarity must be).
In this embodiment, WMD measures the distance between two text documents, i.e. the minimum distance required for a weighted set of feature words in a document to reach another document. On the basis, the similarity of documents is calculated by using the WMD word shift distance, the semantic similarity between information contents is enhanced, different expression modes of similar semantics are identified, the semantic enhancement is realized, a semantic similarity matrix between information libraries can be obtained, and the dimension of the semantic similarity matrix is the total amount of information library data. According to the semantic similarity and the information preference degree of the user, the similarity is calculated cooperatively, and the method specifically comprises the following steps:
step 3.1) obtaining a project collection according to the collected information preference degree of the user, obtaining a similar information collection according to the semantic similarity obtained by calculation, and sorting in descending order according to the length of the intersection;
d (u, K) =n (u) Σs (j, K) formula (one)
Wherein N (u) represents a collection of items of high interest to user u; s (j, K) is K sets of similar intelligence obtained by calculating relative distance values between intelligence. Ordered in descending order according to the length of intersection D (u, K).
Through the preference degree of the user on the information collected in the step 1), firstly, a primary ordered candidate set is obtained according to the information content with high preference degree. For example, for a certain user, according to the part of informations id=197, 220, 683, 757, 758 that he sees, the interest degree is 5 (assuming that the preference degree is set to 1-5, the degree increases in turn), the k informations sets most similar to these informations sets are calculated for de-duplication and the candidate set obtained by the first screening is returned.
N(u)＝set(197，220，683，757，758)
Such as: the similar intelligence set numbered 770 is:
S(770,30)＝(756，586，604，131，994，800，782，775，764，729，618，583，758，241，805，778，155，759，477，764，611，769，723，808，161，571，606，787，579，682)
step 3.2) carrying out secondary screening according to the information content of the intersection; and carrying out secondary fine sorting on the candidate sets with the consistent intersection number through the characteristic value of the corresponding preference degree.
Fine-grained prediction is again performed for information of equal intersection length by the following formula.
Wherein Du represents the intersection of documents obtained by equation (one), sim (d) p ,d q ) Representing similarity of documents p and q calculated using WMD model; score u,q Representing preference degree of informative person u to document q, v u,p Representing a predicted value of user preference for documents.
Step 4): the potential candidate pushing set and the semantic candidate set are subjected to weight proportion distribution to obtain a pushing result, specifically: the similarity between the user and the information obtained based on the user background information is linearly fused with the similarity obtained based on the filtering of the semantic content of the information, the fusion function is as follows,
Z u,p ＝u u,p ×α+v u,p ×(1-α)
wherein u is u,p Representing similarity of user and intelligence based on user background information, v u,p Representing similarity obtained based on filtering of information semantic content, and fusing proportion occupied by the two similarity calculation modes to be adjusted through parameter alpha, wherein the value range is [0,1 ]]，Z u,p And (5) representing a recommendation result obtained by calculating the mixed similarity with the weight value alpha.
Step 5): and pushing the obtained pushing result to the user.
Embodiment two:
the embodiment provides an information pushing device, which comprises an information acquisition unit, a first calculation unit, a second calculation unit, a third calculation unit and a pushing unit, wherein the first calculation unit is used for collecting user background information and information preference degree of a user and acquiring recommended information of an information library; the first computing unit is used for carrying out feature vectorization representation on the user background information and the recommended information through feature words, and obtaining potential candidate pushing sets after similarity is calculated according to vector representation of the user background information and the recommended information; the second computing unit is used for carrying out semantic analysis on the recommended information and computing similarity in cooperation with the information preference degree of the user, and pushing a semantic candidate set which accords with the interest preference of the user; the third calculation unit is used for carrying out weight proportion distribution on the potential candidate pushing set and the semantic candidate set to obtain a pushing result; the pushing unit is used for pushing the obtained pushing result to the user.
In the above embodiment, the information acquisition unit may include a user background information collection unit, a user interest feature analysis unit, and an information acquisition unit, where the user background information collection unit is configured to collect user background information, such as a research direction of a user technical staff, a frequently-searched tag, and a scientific research experience in which the user technical staff participates or mainly participates; the user interest characteristic analysis unit is used for researching the preference degree of the relevant science and technology personnel on part of information in the information base, for example, the interest degree is 5 (the preference degree is set to be 1-5, and the degree is sequentially increased); the information acquisition unit is used for acquiring information documents related to feature words in the information library. The first computing unit may include a feature vectorization unit for vectorizing the user background information by feature extraction and extracting feature words from the recommended information and vectorizing the extracted feature words, and a cosine similarity computing unit for cosine similarity computing the vectors obtained in the previous two steps to obtain candidate pushing sets.
Embodiment III:
the embodiment of the invention also provides a computer readable storage medium, on which a computer program is stored, which when executed by a processor, implements the information pushing method provided by the embodiment of the invention, and the method includes: collecting user background information and information preference degree of a user and acquiring recommended information of an information base; performing feature vectorization representation on the user background information and the recommendation information through feature words, and obtaining potential candidate pushing sets after calculating similarity according to vector representation of the user background information and the recommendation information; carrying out semantic analysis on the recommended information and calculating similarity in cooperation with the information preference degree of the user, and pushing a semantic candidate set which accords with the interest preference of the user; the potential candidate pushing set and the semantic candidate set are subjected to weight proportion distribution to obtain a pushing result; and pushing the obtained pushing result to the user.
The computer storage media of embodiments of the invention may take the form of any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: 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 this document, 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.
The computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. 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, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (5)
1. The information pushing method is characterized by comprising the following steps of:
collecting user background information and information preference degree of a user and acquiring recommended information of an information base;
performing feature vectorization representation on the user background information and the recommendation information through feature words, and obtaining potential candidate pushing sets after calculating similarity according to vector representation of the user background information and the recommendation information;
carrying out semantic analysis on the recommended information and calculating similarity in cooperation with the information preference degree of the user, and pushing a semantic candidate set which accords with the interest preference of the user;
the potential candidate pushing set and the semantic candidate set are subjected to weight proportion distribution to obtain a pushing result;
pushing the obtained pushing result to a user;
carrying out semantic analysis on the recommended information and pushing a semantic candidate set which accords with interest preference of a user in cooperation with the information preference degree of the user, wherein the semantic candidate set comprises the following specific steps:
calculating semantic similarity between the recommended information through a WMD model according to the recommended information which is represented through feature vectors by feature words, and calculating the similarity cooperatively according to the semantic similarity and the information preference degree of the user;
according to the semantic similarity and the information preference degree of the user, the similarity is calculated cooperatively, and the method specifically comprises the following steps:
acquiring a project collection according to the collected information preference degree of the user, acquiring a similar information collection according to the semantic similarity acquired by calculation, and sorting in descending order according to the length of the intersection;
secondary screening is carried out according to the information content of the intersection; and carrying out secondary fine sorting on the candidate sets with the consistent intersection number through the characteristic value of the corresponding preference degree.
2. The intelligence pushing method according to claim 1, wherein the feature vector representation is performed on the user background information and the recommended intelligence information by feature words, and the potential candidate pushing set is obtained after similarity is calculated according to the vector representation of the user background information and the recommended intelligence information, and the method specifically comprises:
(1) Carrying out feature vectorization on user background information through feature extraction;
(2) Extracting feature words from the recommended information and carrying out vectorization representation;
(3) And (3) carrying out cosine similarity calculation on the vectors obtained in the two steps to obtain candidate pushing sets.
3. The intelligence pushing method according to claim 1, wherein the pushing result is obtained after the weight ratio of the potential candidate pushing set to the semantic candidate set is allocated, specifically: the similarity between the user and the information obtained based on the user background information is linearly fused with the similarity obtained based on the filtering of the semantic content of the information, the fusion function is as follows,
Z u,p ＝u u,p ×α+v u,p ×(1-α)
wherein u is u,p Representing similarity of user and intelligence based on user background information, v u,p Representing similarity obtained based on filtering of information semantic content, and fusing proportion occupied by the two similarity calculation modes to be adjusted through parameter alpha, wherein the value range is [0,1 ]]，Z u,p And (5) representing a recommendation result obtained by calculating the mixed similarity with the weight value alpha.
4. An information pushing device is characterized in that,
an information acquisition unit: the information recommendation method comprises the steps of collecting user background information and user information preference degree and acquiring recommendation information of an information library;
a first calculation unit: the method comprises the steps of carrying out feature vectorization representation on user background information and recommendation information through feature words, and obtaining potential candidate pushing sets after similarity is calculated according to vector representation of the user background information and the recommendation information;
a second calculation unit: the semantic analysis module is used for carrying out semantic analysis on the recommended information and calculating similarity in cooperation with the information preference degree of the user, and pushing a semantic candidate set which accords with the interest preference of the user;
a third calculation unit: the method comprises the steps of carrying out weight proportion distribution on the potential candidate pushing set and the semantic candidate set to obtain a pushing result;
and a pushing unit: for pushing the obtained pushing result to the user;
carrying out semantic analysis on the recommended information and pushing a semantic candidate set which accords with interest preference of a user in cooperation with the information preference degree of the user, wherein the semantic candidate set comprises the following specific steps:
calculating semantic similarity between the recommended information through a WMD model according to the recommended information which is represented through feature vectors by feature words, and calculating the similarity cooperatively according to the semantic similarity and the information preference degree of the user;
according to the semantic similarity and the information preference degree of the user, the similarity is calculated cooperatively, and the method specifically comprises the following steps:
acquiring a project collection according to the collected information preference degree of the user, acquiring a similar information collection according to the semantic similarity acquired by calculation, and sorting in descending order according to the length of the intersection;
secondary screening is carried out according to the information content of the intersection; and carrying out secondary fine sorting on the candidate sets with the consistent intersection number through the characteristic value of the corresponding preference degree.
5. A computer readable storage medium, characterized in that the computer readable storage medium has stored therein instructions, which when run on a terminal device, cause the terminal device to perform the intelligence push method of any of claims 1-3.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911423117.XA CN111143694B (en) | 2019-12-31 | 2019-12-31 | Information pushing method and device, storage device and program |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911423117.XA CN111143694B (en) | 2019-12-31 | 2019-12-31 | Information pushing method and device, storage device and program |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111143694A CN111143694A (en) | 2020-05-12 |
| CN111143694B true CN111143694B (en) | 2023-06-16 |
Family
ID=70523062
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911423117.XA Active CN111143694B (en) | 2019-12-31 | 2019-12-31 | Information pushing method and device, storage device and program |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111143694B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110188208A (en) * | 2019-06-04 | 2019-08-30 | 河海大学 | A kind of the information resources inquiry recommended method and system of knowledge based map |
| CN110399563A (en) * | 2019-08-05 | 2019-11-01 | 中国人民解放军军事科学院评估论证研究中心 | A kind of preference pattern and customized information active recommended method based on user behavior analysis |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10460247B2 (en) * | 2015-12-08 | 2019-10-29 | Adobe Inc. | Attribute weighting for media content-based recommendation |
-
2019
- 2019-12-31 CN CN201911423117.XA patent/CN111143694B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110188208A (en) * | 2019-06-04 | 2019-08-30 | 河海大学 | A kind of the information resources inquiry recommended method and system of knowledge based map |
| CN110399563A (en) * | 2019-08-05 | 2019-11-01 | 中国人民解放军军事科学院评估论证研究中心 | A kind of preference pattern and customized information active recommended method based on user behavior analysis |
Non-Patent Citations (1)
| Title |
|---|
| 基于位置社交网络中地点聚类推荐方法;李朔;石宇良;;山东大学学报(工学版)(第03期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111143694A (en) | 2020-05-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US5907838A (en) | Information search and collection method and system | |
| CN111694965B (en) | Image scene retrieval system and method based on multi-mode knowledge graph | |
| Khusro et al. | On methods and tools of table detection, extraction and annotation in PDF documents | |
| CN100447779C (en) | Document information processing apparatus, document information processing method, and document information processing program | |
| MX2013005056A (en) | Multi-modal approach to search query input. | |
| CN108090104B (en) | Method and device for acquiring webpage information | |
| US9639633B2 (en) | Providing information services related to multimodal inputs | |
| CN107977678B (en) | Method and apparatus for outputting information | |
| CN111444304A (en) | Search ranking method and device | |
| Vijayarani et al. | Multimedia mining research-an overview | |
| CN111723256A (en) | Government affair user portrait construction method and system based on information resource library | |
| CN110737824B (en) | Content query method and device | |
| US20090327877A1 (en) | System and method for disambiguating text labeling content objects | |
| US20190082236A1 (en) | Determining Representative Content to be Used in Representing a Video | |
| EP3706014A1 (en) | Methods, apparatuses, devices, and storage media for content retrieval | |
| KR101651963B1 (en) | Method of generating time and space associated data, time and space associated data generation server performing the same and storage medium storing the same | |
| CN112100216A (en) | Creative keyword processing method and device | |
| Ishikawa et al. | T-scroll: Visualizing trends in a time-series of documents for interactive user exploration | |
| CN111143694B (en) | Information pushing method and device, storage device and program | |
| CN116541578A (en) | Asset digital multidimensional management method and system | |
| CN116108181A (en) | Client information processing method and device and electronic equipment | |
| Zhang et al. | A text mining based method for policy recommendation | |
| CN114706996A (en) | Supply chain online knowledge graph construction method based on multivariate heterogeneous data mining | |
| CN114282119A (en) | Scientific and technological information resource retrieval method and system based on heterogeneous information network | |
| CN113377963A (en) | Well site test data processing method and device based on knowledge graph |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |