CN110110207B - Information recommendation method and device and electronic equipment - Google Patents
Information recommendation method and device and electronic equipment Download PDFInfo
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- CN110110207B CN110110207B CN201810050214.8A CN201810050214A CN110110207B CN 110110207 B CN110110207 B CN 110110207B CN 201810050214 A CN201810050214 A CN 201810050214A CN 110110207 B CN110110207 B CN 110110207B
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- 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
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- G—PHYSICS
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
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- G06F40/279—Recognition of textual entities
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Abstract
The application discloses an information recommendation method, an information recommendation device and electronic equipment. The information recommendation method comprises the following steps: acquiring an interest word set of a user; acquiring target words to be recommended and a reference word set associated with the target words; judging whether the target word is matched with the interest word set or not based on the reference word set; and if the target word is matched with the interest word set, recommending the target word to the user. In the technical scheme, whether the target word is matched with the interest word set of the user or not is judged through the reference word set associated with the target word to be recommended, so that the target word is recommended to the user, the technical problem of low accuracy caused by information recommendation based on the word to be recommended in the prior art is solved, and the accuracy of information recommendation is improved.
Description
Technical Field
The present application relates to the field of information technologies, and in particular, to an information recommendation method, an information recommendation device, and an electronic device.
Background
With the continuous development of information technology and network technology, a great deal of information such as: hotwords, new words, popular words, etc. In order to facilitate users to learn information in time or input information quickly, certain keywords are often recommended to users, such as hotwords generated daily.
In the prior art, information recommendation is generally performed by combining the words to be recommended with the user. With the development of network information, many times the word to be recommended contains not only its own meaning, but possibly other extended meanings. The information recommendation is performed based on the word to be recommended alone, and the due extension meaning is not considered, so that the accuracy of the information recommendation is low.
Disclosure of Invention
The embodiment of the application provides an information recommendation method, an information recommendation device and electronic equipment, which are used for solving the technical problem of low accuracy caused by information recommendation based on words to be recommended in the prior art and improving the accuracy of information recommendation.
The embodiment of the application provides an information recommendation method, which comprises the following steps:
acquiring an interest word set of a user based on target content used by the user;
acquiring target words to be recommended and a reference word set associated with the target words;
judging whether the target word is matched with the interest word set or not based on the reference word set;
and if the target word is matched with the interest word set, recommending the target word to the user.
Optionally, based on the reference word set, determining whether the target word matches the interest word set includes:
obtaining the similarity between the interest word set and the reference word set;
judging whether the similarity is larger than or equal to a similarity threshold value;
and if the similarity is greater than or equal to the similarity threshold, determining that the target word is matched with the interest word set, otherwise, determining that the target word is not matched with the interest word set.
Optionally, obtaining the similarity between the interest word set and the reference word set includes:
obtaining the number K of the same and similar words in the interest word set and the reference word set and the total number M of the words in the interest word set;
and taking the ratio of K to M as the similarity of the interest word set and the reference word set.
Optionally, obtaining the similarity between the interest word set and the reference word set includes:
establishing a first vector for the interest word set and a second vector for the reference word set according to a word vector database;
a distance between the first vector and the second vector is obtained as a similarity between the set of interest words and the set of reference words.
Optionally, the method for establishing the reference word set includes:
acquiring the real words which co-occur with the target words and the co-occurrence times of the real words;
sorting the real meaning words according to the order of the times from big to small to obtain N real meaning words with the top sorting;
and establishing the reference word set according to the N real words.
Optionally, based on the target content used by the user, acquiring the interest word set of the user includes:
acquiring target content used by the user in a preset time period;
word segmentation is carried out on the target content, and real words in the target content are obtained;
and establishing the interest word set based on the real words in the target content.
Optionally, the target content includes: input content, browse content, and/or communication content.
Optionally, recommending the target word to the user includes: and recommending the target word to the user as a first candidate.
Optionally, the method further comprises: and if the target word is not matched with the interest word set, prohibiting the recommendation of the target word to the user, or recommending the target word to the user as a last candidate.
The embodiment of the application also provides an information recommendation device, which comprises:
the acquisition unit is used for acquiring the interest word set of the user based on the target content used by the user; acquiring target words to be recommended and a reference word set associated with the target words;
the judging unit is used for judging whether the target word is matched with the interest word set or not based on the reference word set;
and the recommending unit is used for recommending the target word to the user if the target word is matched with the interest word set.
Optionally, the judging unit is configured to:
obtaining the similarity between the interest word set and the reference word set;
judging whether the similarity is larger than or equal to a similarity threshold value;
and if the similarity is greater than or equal to the similarity threshold, determining that the target word is matched with the interest word set, otherwise, determining that the target word is not matched with the interest word set.
Optionally, the judging unit is further configured to: obtaining the number K of the same and similar words in the interest word set and the reference word set and the total number M of the words in the interest word set; and taking the ratio of K to M as the similarity of the interest word set and the reference word set.
Optionally, the judging unit is further configured to: establishing a first vector for the interest word set and a second vector for the reference word set according to a word vector database; a distance between the first vector and the second vector is obtained as a similarity between the set of interest words and the set of reference words.
Optionally, the apparatus further includes:
the establishing unit is used for acquiring the real words coexisting with the target words and the times of coexisting with the real words; sorting the real meaning words according to the order of the times from big to small to obtain N real meaning words with the top sorting; and establishing the reference word set according to the N real words.
Optionally, the acquiring unit is further configured to: acquiring target content used by the user in a preset time period; word segmentation is carried out on the target content, and real words in the target content are obtained; and establishing the interest word set based on the real words in the target content.
Optionally, the target content includes: input content, browse content, and/or communication content.
Optionally, the recommending unit is further configured to: and recommending the target word to the user as a first candidate.
Optionally, the recommending unit is further configured to: and if the target word is not matched with the interest word set, prohibiting the recommendation of the target word to the user, or recommending the target word to the user as a last candidate.
Embodiments of the present application also provide an electronic device comprising a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by one or more processors, the one or more programs comprising instructions for:
acquiring an interest word set of a user based on target content used by the user;
acquiring target words to be recommended and a reference word set associated with the target words;
judging whether the target word is matched with the interest word set or not based on the reference word set;
and if the target word is matched with the interest word set, recommending the target word to the user.
The embodiment of the present application also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of:
acquiring an interest word set of a user based on target content used by the user;
acquiring target words to be recommended and a reference word set associated with the target words;
judging whether the target word is matched with the interest word set or not based on the reference word set;
and if the target word is matched with the interest word set, recommending the target word to the user.
The above technical solutions in the embodiments of the present application at least have the following technical effects:
the embodiment of the application provides an information recommendation method, which is used for acquiring an interest word set of a user based on used target content of the user, and acquiring target words to be recommended and a reference word set associated with the target words; judging whether the target word is matched with the interest word set or not based on the reference word set; and if the target word is matched with the interest word set, recommending the target word to the user. The meaning of the target word is enriched and perfected through the reference word set related to the target word to be recommended, and whether the target word is matched with the interest word set of the user or not is judged based on the reference word set, so that the target word is more accurately matched with the interest point of the user, the technical problem of low accuracy caused by information recommendation based on the word to be recommended in the prior art is solved, and the accuracy of information recommendation is improved.
Drawings
Fig. 1 is a schematic flow chart of an information recommendation method according to an embodiment of the present application;
fig. 2 is a block diagram of an information recommendation device according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In the technical scheme provided by the embodiment of the application, the target word is selected for recommendation through the reference word set associated with the target word to be recommended, and the accuracy of matching the target word with the user interest is improved, so that the technical problem of low information recommendation accuracy in the prior art is solved, and the accuracy of information recommendation is improved.
The main implementation principle, the specific implementation manner and the corresponding beneficial effects of the technical scheme of the embodiment of the application are described in detail below with reference to the accompanying drawings.
Examples
Referring to fig. 1, an embodiment of the present application provides an information recommendation method, which can be applied to personalized recommendation such as recommendation of candidate items in an input method, recommendation of search keywords in a browser, and sequencing of recommended information. The information recommendation method comprises the following steps:
s110: acquiring an interest word set of a user based on target content used by the user;
s120: acquiring target words to be recommended and a reference word set associated with the target words;
s130: judging whether the target word is matched with the interest word set or not based on the reference word set;
s140: and if the target word is matched with the interest word set, recommending the target word to the user.
In the implementation process, when the S110 obtains the interest word set of the user, the interest word set may be obtained from the user database built in advance by the electronic device or the server. The interest word set may be obtained based on target content used by the user, that is, target content included in the history. Specifically, the interest word set may be constructed in any of the following ways: acquiring a plurality of interest words preset by a user to establish an interest word set of the user, for example, acquiring basketball, entertainment eight diagrams, unmanned and the like preset by the user as the interest words to establish the interest word set; acquiring a plurality of used segmentation words of a user to establish an interest word set of the user, for example, acquiring 'Sima', 'three kingdoms', 'histories' and the like frequently used by the user as interest words to establish the interest word set; the existing plurality of interest words are acquired from other application programs such as an input method to establish an interest word set of the user.
The method comprises the steps of obtaining a plurality of used word segments of a user to establish an interest word set of the user, and obtaining target content used by the user in a preset time period. The target content may include input content, browsing content and/or communication content corresponding to the user in a preset time period, and may be obtained from application programs such as an input method, a browser, instant messaging software and the like. The preset time period may be 1 day, 1 week, 10 days, etc., and the embodiment of the present application does not limit the duration of the preset time period. After the target content is obtained, the target content is segmented, and real words (meaning words, including predicate verbs, nouns and the like) in the target content are obtained, for example, the input content 'the price of a male security new area has great fluctuation', and the segmentation can be obtained by segmentation: androstane/price/presence/magnitude/fluctuation and obtaining the real words therefrom: androstane zone/rate/fluctuation. Then, an interest word set is established based on the real words in the target content, and the obtained real words are used as the interest words to establish the interest word set. Of course, when the interest word set is established based on the real words in the target content, the real words can be screened, such as de-duplication, obtaining high-frequency real words, setting the weight of each real word according to the use frequency, and the like, and the screened real words are used as the interest words to establish the interest word set. By establishing the user interest word set according to the used word segmentation of the user, the problems of interest expansion, interest point omission and the like caused by interest classification are avoided, the specific interests of the user can be more accurately and comprehensively reflected, and accordingly, the accuracy of information recommendation can be improved by information recommendation.
Before, after, or simultaneously with execution of S110, execution S120 acquires a target word to be recommended and a set of reference words associated therewith. The target words to be recommended may be hotwords obtained by network statistics, new words, push words provided by the platform, and the like. The reference word associated with the target word may be a near meaning word of the target word, a word co-occurring with the target word, a result keyword obtained by searching the target word, or the like. The reference word set may be established by first obtaining the real words and the co-occurrence times of the real words, for example, assuming that the target words are "xiaoan", extracting the real words such as "new region", "baoding", "planning", "construction", "price" and the like, that is, the co-occurrence times of the real words and the co-occurrence times of the real words such as "new region", "baoding", "planning", "construction", "price" and the like, from the source text of "xiaoan". Sequencing all the real meaning words according to the sequence from big to small of the times of co-occurrence to obtain N real meaning words with the front sequencing; and establishing a reference word set according to the acquired first N real words, wherein the first N real words to be acquired are used as reference words to establish the reference word set, and the target word is a reference word in the reference word set. For example: the frequency of the co-occurrence of each real meaning word and the male security in the new area, the guaranty, the planning, the construction and the price of the house is respectively as follows: 110. 80, 90, 60, 110, if N is 3, then the "new zone", "room price" and "plan" of the first 3 digits of the number of ranks are obtained as reference words for "androstane" for establishing the reference word set.
After obtaining the set of interest words, the target word, and the set of reference words associated with the target word of the user, S130 is performed to determine whether the target word matches the set of interest words based on the set of reference words. Specifically, the similarity between the interest word set and the reference word set can be obtained; judging whether the similarity is larger than or equal to a similarity threshold value; and if the similarity between the interest word set and the reference word set is greater than or equal to a similarity threshold, determining that the target word is matched with the interest word set, otherwise, determining that the target word is not matched with the interest word set. The similarity between the interest word set and the reference word set can be obtained by adopting any one of the following modes:
the method comprises the steps of firstly, obtaining the number K of the same and similar words in the interest word set and the reference word set, and obtaining the total number M of the words in the interest word set. Whether the two words are close or not can be judged through the editing distance or the vector distance between the two words, if the editing distance or the vector distance is within a preset range, the two words are considered to be similar, otherwise, the two words are considered to be dissimilar. After obtaining K and M, the ratio of K to M is taken as the similarity of the interest word set and the reference word set. For example: assuming that the number of the same and similar words in the interest word set and the reference word set is 8 and the total number of the words in the interest word set is 10, 8/10 is taken as the similarity between the interest word set and the reference word set.
And in a second mode, a first vector is established for the interest word set according to the word vector database, and a second vector is established for the reference word set. Wherein, an n 1-dimensional vector is constructed for each word in advance in the word vector database. If the interest word set contains m1 interest words, a first vector with n1 multiplied by m1 dimension is established for the interest word set according to the word vector database. Correspondingly, if the reference word set contains m2 reference words, a second vector with m2 multiplied by n1 dimension is established for the reference word set according to the word vector database. Then, a distance between the first vector and the second vector is obtained as a similarity between the set of interest words and the set of reference words. The distance between the vectors can be calculated by adopting pearson correlation coefficient, euclidean distance, cosine similarity and Tanimoto coefficient, and the distance can be calculated more accurately by adopting the cosine similarity or Tanimoto coefficient aiming at the fact that the first vector and the second vector are document data in the specification.
After obtaining the similarity between the interest word set and the reference word set based on any mode, further judging whether the similarity is greater than or equal to a similarity threshold, wherein the similarity threshold can be set according to the matching accuracy requirement, for example, the similarity threshold can be set to be 0.8, 0.85 or 0.9 equivalent, and the application is not limited to the specific value of the similarity threshold. If the similarity between the interest word set and the reference word set is greater than or equal to a similarity threshold, judging that the target word is matched with the interest word set of the user, namely, the target word is the word interested by the user, otherwise, the target word is not matched.
In the case where it is determined at S130 that the target word matches the set of interest words of the user, S140 is performed to recommend the target word to the user. Under the condition that the target word is matched with the interest word set of the user, the target word is more likely to be interested in the target word by the user, and the target word can be recommended to the user as a first candidate when the target word is recommended to the user, so that the user can see the target word at first eyes, the target word can be conveniently selected and used, and the recommendation effectiveness is improved. Otherwise, if the target word is not matched with the interest word set of the user, the recommendation of the target word to the user can be forbidden, or the target word is recommended to the user as a last candidate, so that adverse effects of the target word on other recommended words or user words are avoided, and user experience is improved.
The information recommendation method provided in this embodiment is illustrated below by taking input method candidate recommendation as an example:
suppose that the user browses a lot of news about the investment in a house on a certain day. The electronic equipment extracts the interest words such as the real words 'planning', 'new area', 'room price' from the news browsed by the user and writes the interest words into the current interest word set of the user. When a user invokes an input method, the electronic device operates the input method and acquires target words to be recommended, and the target words to be recommended are assumed to be acquired as follows: the network hot words "Male" and "clean energy", the reference word set of "Male" contains reference words such as "planning", "new region", "room price", "construction", etc., while the reference word set of "clean energy" contains reference words such as "patch", "high efficiency", "popularization", etc., and based on the reference word set, it is judged that "Male" matches with the user's interest word set, while "clean energy" does not match with the user's interest word set. When the recommended candidate items in the input interface are displayed, the 'Male' is displayed at the first position of the candidate items, the system words provided by the input method and/or the user words are displayed in the middle of the candidate items, and the 'clean energy' is displayed behind the system words. By matching the words to be recommended with the interest word set of the user, arranging the target words of interest to the user at a front position and arranging the target words of little interest to the user at a rear position, the problem that the information recommendation effect is reduced because part of words are not wanted by the user and the words to be recommended are arranged at the rear position is avoided.
With reference to fig. 2, referring to fig. 2, the embodiment of the present application further provides an information recommendation device, where the information recommendation device includes:
an acquisition unit 21 for acquiring a set of interest words of a user based on target content used by the user; acquiring target words to be recommended and a reference word set associated with the target words;
a judging unit 22, configured to judge whether the target word matches the interest word set based on the reference word set;
and a recommending unit 23, configured to recommend the target word to the user if the target word matches the interest word set.
As an alternative embodiment, the judging unit 22 may obtain the similarity between the set of interest words and the set of reference words when judging whether there is a match between the target word and the set of interest words; judging whether the similarity is larger than or equal to a similarity threshold value; and if the similarity is greater than or equal to the similarity threshold, determining that the target word is matched with the interest word set, otherwise, determining that the target word is not matched with the interest word set.
Specifically, the judging unit 22 may obtain the similarity between the word sets by any one of the following means: the first mode is to obtain the number K of the same and similar words in the interest word set and the reference word set and the total number M of the words in the interest word set; and taking the ratio of K to M as the similarity of the interest word set and the reference word set. A second mode is that a first vector is established for the interest word set according to a word vector database, and a second vector is established for the reference word set; a distance between the first vector and the second vector is obtained as a similarity between the set of interest words and the set of reference words.
As an alternative embodiment, the apparatus further comprises: a building unit 24, configured to obtain a real word co-occurring with the target word and the number of times of co-occurrence thereof; sorting the real meaning words according to the order of the times from big to small to obtain N real meaning words with the top sorting; and establishing the reference word set according to the N real words.
As an alternative embodiment, the acquisition unit 21 is further configured to: acquiring target content used by the user in a preset time period; word segmentation is carried out on the target content, and real words in the target content are obtained; and establishing the interest word set based on the real words in the target content. Wherein the target content comprises: input content, browse content, and/or communication content.
As an alternative embodiment, the recommendation unit 23 may recommend the target word to the user as the first candidate when recommending the target word to the user. Of course, the recommendation unit 23 may also be used to: and under the condition that the target word is not matched with the interest word set, prohibiting the recommendation of the target word to the user, or recommending the target word to the user as a last candidate.
The specific manner in which the individual units perform the operations in relation to the apparatus of the above embodiments has been described in detail in relation to the embodiments of the method and will not be described in detail here.
Fig. 3 is a block diagram illustrating an electronic device 800 for implementing an information recommendation method, according to an example embodiment. For example, electronic device 800 may be a mobile phone, computer, digital broadcast terminal, messaging device, game console, tablet device, medical device, exercise device, personal digital assistant, or the like.
Referring to fig. 3, the electronic device 800 may include one or more of the following components: a processing component 802, a memory 804, a power component 806, a multimedia component 808, an audio component 810, an input/presentation (I/O) interface 812, a sensor component 814, and a communication component 816.
The processing component 802 generally controls overall operation of the electronic device 800, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. Processing element 802 may include one or more processors 820 to execute instructions to perform all or part of the steps of the methods described above. Further, the processing component 802 can include one or more modules that facilitate interactions between the processing component 802 and other components. For example, the processing component 802 may include a multimedia module to facilitate interaction between the multimedia component 808 and the processing component 802.
The memory 804 is configured to store various types of data to support operations at the device 800. Examples of such data include instructions for any application or method operating on the electronic device 800, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 804 may be implemented by any type or combination of volatile or nonvolatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disk.
The power supply component 806 provides power to the various components of the electronic device 800. The power components 806 may include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power for the electronic device 800.
The multimedia component 808 includes a screen between the electronic device 800 and the user that provides a presentation interface. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from a user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensor may sense not only the boundary of a touch or slide action, but also the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front camera and/or a rear camera. The front camera and/or the rear camera may receive external multimedia data when the device 800 is in an operational mode, such as a shooting mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have focal length and optical zoom capabilities.
The audio component 810 is configured to present and/or input audio signals. For example, the audio component 810 includes a Microphone (MIC) configured to receive external audio signals when the electronic device 800 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may be further stored in the memory 804 or transmitted via the communication component 816. In some embodiments, the audio component 810 further includes a speaker for rendering audio signals.
The I/O interface 812 provides an interface between the processing component 802 and peripheral interface modules, which may be a keyboard, click wheel, buttons, etc. These buttons may include, but are not limited to: homepage button, volume button, start button, and lock button.
The sensor assembly 814 includes one or more sensors for providing status assessment of various aspects of the electronic device 800. For example, the sensor assembly 814 may detect an on/off state of the device 800, a relative positioning of the components, such as a display and keypad of the electronic device 800, the sensor assembly 814 may also detect a change in position of the electronic device 800 or a component of the electronic device 800, the presence or absence of a user's contact with the electronic device 800, an orientation or acceleration/deceleration of the electronic device 800, and a change in temperature of the electronic device 800. The sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an acceleration sensor, a gyroscopic sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
The communication component 816 is configured to facilitate communication between the electronic device 800 and other devices, either wired or wireless. The electronic device 800 may access a wireless network based on a communication standard, such as WiFi,2G, or 3G, or a combination thereof. In one exemplary embodiment, the communication part 816 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, the communication component 816 further includes a Near Field Communication (NFC) module to facilitate short range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, ultra Wideband (UWB) technology, bluetooth (BT) technology, and other technologies.
In an exemplary embodiment, the electronic device 800 may be implemented by one or more Application Specific Integrated Circuits (ASICs), digital Signal Processors (DSPs), digital Signal Processing Devices (DSPDs), programmable Logic Devices (PLDs), field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic elements for executing the methods described above.
In an exemplary embodiment, a non-transitory computer readable storage medium is also provided, such as memory 804 including instructions executable by processor 820 of electronic device 800 to perform the above-described method. For example, the non-transitory computer readable storage medium may be ROM, random Access Memory (RAM), CD-ROM, magnetic tape, floppy disk, optical data storage device, etc.
A non-transitory computer readable storage medium, which when executed by a processor of a mobile terminal, causes the mobile terminal to perform an information recommendation method, the method comprising: acquiring an interest word set of a user based on target content used by the user; acquiring target words to be recommended and a reference word set associated with the target words; judging whether the target word is matched with the interest word set or not based on the reference word set; and if the target word is matched with the interest word set, recommending the target word to the user. .
Fig. 3 is a schematic structural diagram of a server according to an embodiment of the present application. The server 1900 may vary considerably in configuration or performance and may include one or more central processing units (central processing units, CPU) 1922 (e.g., one or more processors) and memory 1932, one or more storage media 1930 (e.g., one or more mass storage devices) that store applications 1942 or data 1944. Wherein the memory 1932 and storage medium 1930 may be transitory or persistent. The program stored in the storage medium 1930 may include one or more modules (not shown), each of which may include a series of instruction operations on a server. Still further, a central processor 1922 may be provided in communication with a storage medium 1930 to execute a series of instruction operations in the storage medium 1930 on the server 1900.
The server 1900 may also include one or more power supplies 1926, one or more wired or wireless network interfaces 1950, one or more input presentation interfaces 1958, one or more keyboards 1956, and/or one or more operating systems 1941, such as Windows Server, mac OS XTM, unixTM, linuxTM, freeBSDTM, and the like.
Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. This application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the application pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
It is to be understood that the application is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the application is limited only by the appended claims
The foregoing description of the preferred embodiments of the application is not intended to limit the application to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the application are intended to be included within the scope of the application.
Claims (16)
1. An information recommendation method, the method comprising:
acquiring an interest word set of a user based on target content used by the user;
acquiring target words to be recommended and a reference word set associated with the target words;
judging whether the target word is matched with the interest word set or not based on the reference word set;
if the target word is matched with the interest word set, recommending the target word to the user;
based on the reference word set, judging whether the target word is matched with the interest word set or not, including:
obtaining the similarity between the interest word set and the reference word set;
judging whether the similarity is larger than or equal to a similarity threshold value;
if the similarity is greater than or equal to the similarity threshold, determining that the target word is matched with the interest word set, otherwise, determining that the target word is not matched with the interest word set;
the method for establishing the reference word set comprises the following steps:
acquiring the real words which co-occur with the target words and the co-occurrence times of the real words;
sorting the real meaning words according to the order of the times from big to small to obtain N real meaning words with the top sorting;
and establishing the reference word set according to the N real words.
2. The method of claim 1, wherein obtaining similarity between the set of interest words and the set of reference words comprises:
obtaining the number K of the same and similar words in the interest word set and the reference word set and the total number M of the words in the interest word set;
and taking the ratio of K to M as the similarity of the interest word set and the reference word set.
3. The method of claim 1, wherein obtaining similarity between the set of interest words and the set of reference words comprises:
establishing a first vector for the interest word set and a second vector for the reference word set according to a word vector database;
a distance between the first vector and the second vector is obtained as a similarity between the set of interest words and the set of reference words.
4. The method of claim 1, wherein obtaining the set of user's interest words based on the target content used by the user comprises:
acquiring target content used by the user in a preset time period;
word segmentation is carried out on the target content, and real words in the target content are obtained;
and establishing the interest word set based on the real words in the target content.
5. The method of claim 4, wherein the target content comprises: input content, browse content, and/or communication content.
6. The method of any of claims 1-5, wherein recommending the target word to the user comprises: and recommending the target word to the user as a first candidate.
7. The method of any one of claims 1-5, further comprising:
and if the target word is not matched with the interest word set, prohibiting the recommendation of the target word to the user, or recommending the target word to the user as a last candidate.
8. An information recommendation device, characterized by comprising:
the acquisition unit is used for acquiring the interest word set of the user based on the target content used by the user; acquiring target words to be recommended and a reference word set associated with the target words;
the judging unit is used for judging whether the target word is matched with the interest word set or not based on the reference word set;
the recommending unit is used for recommending the target word to the user if the target word is matched with the interest word set;
the judging unit is used for:
obtaining the similarity between the interest word set and the reference word set;
judging whether the similarity is larger than or equal to a similarity threshold value;
if the similarity is greater than or equal to the similarity threshold, determining that the target word is matched with the interest word set, otherwise, determining that the target word is not matched with the interest word set;
the apparatus further comprises:
the establishing unit is used for acquiring the real words coexisting with the target words and the times of coexisting with the real words; sorting the real meaning words according to the order of the times from big to small to obtain N real meaning words with the top sorting; and establishing the reference word set according to the N real words.
9. The apparatus of claim 8, wherein the determination unit is further configured to:
obtaining the number K of the same and similar words in the interest word set and the reference word set and the total number M of the words in the interest word set;
and taking the ratio of K to M as the similarity of the interest word set and the reference word set.
10. The apparatus of claim 8, wherein the determination unit is further configured to:
establishing a first vector for the interest word set and a second vector for the reference word set according to a word vector database;
a distance between the first vector and the second vector is obtained as a similarity between the set of interest words and the set of reference words.
11. The apparatus of claim 8, wherein the acquisition unit is further to:
acquiring target content used by the user in a preset time period;
word segmentation is carried out on the target content, and real words in the target content are obtained;
and establishing the interest word set based on the real words in the target content.
12. The apparatus of claim 11, wherein the target content comprises: input content, browse content, and/or communication content.
13. The apparatus according to any one of claims 8 to 12, wherein the recommending unit is further configured to: and recommending the target word to the user as a first candidate.
14. The apparatus according to any one of claims 8 to 12, wherein the recommending unit is further configured to:
and if the target word is not matched with the interest word set, prohibiting the recommendation of the target word to the user, or recommending the target word to the user as a last candidate.
15. An electronic device comprising a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by one or more processors, the one or more programs comprising instructions for:
acquiring an interest word set of a user based on target content used by the user;
acquiring target words to be recommended and a reference word set associated with the target words;
judging whether the target word is matched with the interest word set or not based on the reference word set;
if the target word is matched with the interest word set, recommending the target word to the user;
based on the reference word set, judging whether the target word is matched with the interest word set or not, including:
obtaining the similarity between the interest word set and the reference word set;
judging whether the similarity is larger than or equal to a similarity threshold value;
if the similarity is greater than or equal to the similarity threshold, determining that the target word is matched with the interest word set, otherwise, determining that the target word is not matched with the interest word set;
the method for establishing the reference word set comprises the following steps:
acquiring the real words which co-occur with the target words and the co-occurrence times of the real words;
sorting the real meaning words according to the order of the times from big to small to obtain N real meaning words with the top sorting;
and establishing the reference word set according to the N real words.
16. A computer readable storage medium having stored thereon a computer program, characterized in that the program when executed by a processor performs the steps of:
acquiring an interest word set of a user based on target content used by the user;
acquiring target words to be recommended and a reference word set associated with the target words;
judging whether the target word is matched with the interest word set or not based on the reference word set;
if the target word is matched with the interest word set, recommending the target word to the user;
based on the reference word set, judging whether the target word is matched with the interest word set or not, including:
obtaining the similarity between the interest word set and the reference word set;
judging whether the similarity is larger than or equal to a similarity threshold value;
if the similarity is greater than or equal to the similarity threshold, determining that the target word is matched with the interest word set, otherwise, determining that the target word is not matched with the interest word set;
the method for establishing the reference word set comprises the following steps:
acquiring the real words which co-occur with the target words and the co-occurrence times of the real words;
sorting the real meaning words according to the order of the times from big to small to obtain N real meaning words with the top sorting;
and establishing the reference word set according to the N real words.
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| CN112784142A (en) * | 2019-10-24 | 2021-05-11 | 北京搜狗科技发展有限公司 | Information recommendation method and device |
| CN111143546A (en) * | 2019-11-15 | 2020-05-12 | 北京星选科技有限公司 | Method and device for obtaining recommendation language and electronic equipment |
| CN110909789A (en) * | 2019-11-20 | 2020-03-24 | 精硕科技（北京）股份有限公司 | Sound volume prediction method and device, electronic equipment and storage medium |
Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103324641A (en) * | 2012-03-23 | 2013-09-25 | 日电（中国）有限公司 | Information record recommendation method and device |
| CN103729360A (en) * | 2012-10-12 | 2014-04-16 | 腾讯科技（深圳）有限公司 | Interest label recommendation method and system |
| CN103761263A (en) * | 2013-12-31 | 2014-04-30 | 武汉传神信息技术有限公司 | Method for recommending information for users |
| CN104111941A (en) * | 2013-04-18 | 2014-10-22 | 阿里巴巴集团控股有限公司 | Method and equipment for information display |
| CN104199872A (en) * | 2014-08-19 | 2014-12-10 | 北京搜狗科技发展有限公司 | Information recommendation method and device |
| CN104199898A (en) * | 2014-08-26 | 2014-12-10 | 百度在线网络技术（北京）有限公司 | Method and device for acquiring attribute information and push method and device |
| CN104809154A (en) * | 2015-03-19 | 2015-07-29 | 百度在线网络技术（北京）有限公司 | Method and device for recommending information |
| CN105204661A (en) * | 2015-10-20 | 2015-12-30 | 广东欧珀移动通信有限公司 | Method and device for filtering banned words in input method |
| CN106055540A (en) * | 2016-06-01 | 2016-10-26 | 比美特医护在线（北京）科技有限公司 | Data processing method and device |
| CN106557480A (en) * | 2015-09-25 | 2017-04-05 | 阿里巴巴集团控股有限公司 | Implementation method and device that inquiry is rewritten |
| CN106776881A (en) * | 2016-11-28 | 2017-05-31 | 中国科学院软件研究所 | A kind of realm information commending system and method based on microblog |
| CN107315487A (en) * | 2016-04-27 | 2017-11-03 | 北京搜狗科技发展有限公司 | A kind of input processing method, device and electronic equipment |
| CN107436896A (en) * | 2016-05-26 | 2017-12-05 | 北京搜狗科技发展有限公司 | Method, apparatus and electronic equipment are recommended in one kind input |
-
2018
- 2018-01-18 CN CN201810050214.8A patent/CN110110207B/en active Active
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103324641A (en) * | 2012-03-23 | 2013-09-25 | 日电（中国）有限公司 | Information record recommendation method and device |
| CN103729360A (en) * | 2012-10-12 | 2014-04-16 | 腾讯科技（深圳）有限公司 | Interest label recommendation method and system |
| WO2014056397A1 (en) * | 2012-10-12 | 2014-04-17 | 腾讯科技（深圳）有限公司 | Label of interest recommendation method, system and computer readable medium |
| CN104111941A (en) * | 2013-04-18 | 2014-10-22 | 阿里巴巴集团控股有限公司 | Method and equipment for information display |
| CN103761263A (en) * | 2013-12-31 | 2014-04-30 | 武汉传神信息技术有限公司 | Method for recommending information for users |
| CN104199872A (en) * | 2014-08-19 | 2014-12-10 | 北京搜狗科技发展有限公司 | Information recommendation method and device |
| CN104199898A (en) * | 2014-08-26 | 2014-12-10 | 百度在线网络技术（北京）有限公司 | Method and device for acquiring attribute information and push method and device |
| CN104809154A (en) * | 2015-03-19 | 2015-07-29 | 百度在线网络技术（北京）有限公司 | Method and device for recommending information |
| CN106557480A (en) * | 2015-09-25 | 2017-04-05 | 阿里巴巴集团控股有限公司 | Implementation method and device that inquiry is rewritten |
| CN105204661A (en) * | 2015-10-20 | 2015-12-30 | 广东欧珀移动通信有限公司 | Method and device for filtering banned words in input method |
| CN107315487A (en) * | 2016-04-27 | 2017-11-03 | 北京搜狗科技发展有限公司 | A kind of input processing method, device and electronic equipment |
| CN107436896A (en) * | 2016-05-26 | 2017-12-05 | 北京搜狗科技发展有限公司 | Method, apparatus and electronic equipment are recommended in one kind input |
| CN106055540A (en) * | 2016-06-01 | 2016-10-26 | 比美特医护在线（北京）科技有限公司 | Data processing method and device |
| CN106776881A (en) * | 2016-11-28 | 2017-05-31 | 中国科学院软件研究所 | A kind of realm information commending system and method based on microblog |
Non-Patent Citations (2)
| Title |
|---|
| Rejecting Mismatches of Visual Words by Contextual Descriptors;Yao, JL;2014 13TH INTERNATIONAL CONFERENCE ON CONTROL AUTOMATION ROBOTICS & VISION (ICARCV);第1508-1513页 * |
| 基于三词共现分析的学者主要研究兴趣识别及个性化外文推荐服务的实现;李树青;;情报学报;第32卷(第06期);第629-639页 * |
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