CN109145221B - Content recommendation method and device, electronic equipment and readable storage medium - Google Patents
Content recommendation method and device, electronic equipment and readable storage medium Download PDFInfo
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- CN109145221B CN109145221B CN201811054068.2A CN201811054068A CN109145221B CN 109145221 B CN109145221 B CN 109145221B CN 201811054068 A CN201811054068 A CN 201811054068A CN 109145221 B CN109145221 B CN 109145221B
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Abstract
The embodiment of the invention provides a content recommendation method and device, electronic equipment and a readable storage medium, and relates to the technical field of Internet, wherein the content recommendation method comprises the following steps: at least one recommended content to be recommended is acquired. And determining that the at least one piece of recommended content comprises second recommended content indicated in the preference information of the user. Wherein the preference information includes recommended content and feedback parameters that the user dislikes. The feedback parameters comprise feedback time and/or feedback times. And determining whether the feedback parameter meets a preset condition. And recommending the second recommended content to the user when the feedback parameter meets a preset condition. The second recommended content is recommended to the client by judging whether the feedback time and/or the feedback times reach the preset conditions, so that the situation that the second recommended content is not pushed permanently due to short-term dislike or misoperation of the user can be avoided. Therefore, the practicability is stronger, and the humanization is better.
Description
Technical Field
The invention relates to the technical field of internet, in particular to a content recommendation method and device, electronic equipment and a readable storage medium.
Background
At present, when a webpage is browsed or an article is read through an APP, the webpage or the APP server recommends content according to some favorite preferences of a user, so that the content received by the user is favorite as much as possible. Thus, the server may provide setting options via a web browser or APP, the user may set or feed back the type of articles or content that is disliked (referred to in the industry as negative feedback), and the server may essentially permanently no longer push or recommend articles or content associated with the disliked articles or content based on the user's settings or feedback. Thus, the user will also permanently not receive recommendations or push content that was once negatively fed back. Although the method can shield the content which is fed back by the user once, the method is invariable and not flexible enough.
Disclosure of Invention
The invention aims to provide a content recommendation method and device, an electronic device and a readable storage medium, which can solve the problems; to achieve the above object; the technical scheme adopted by the invention is as follows:
in a first aspect, an embodiment of the present invention provides a content recommendation method, including:
acquiring at least one recommended content to be recommended;
determining that the at least one recommended content includes a second recommended content related to a first recommended content that is disliked by the user and indicated in the preference information of the user; wherein the preference information further comprises a feedback parameter; the feedback parameters comprise feedback time and/or feedback times for feeding back the first recommended content;
determining whether the feedback parameter of the first recommended content meets a preset condition;
and recommending the second recommended content to the user when the feedback parameter meets a preset condition.
Optionally, the determining whether the feedback parameter satisfies a preset condition includes:
and determining whether the feedback times are less than preset times.
And determining whether the feedback time exceeds a preset time length from the current time point. And when the feedback times are smaller than the preset times or when the feedback time exceeds the preset time length from the current time point, representing that the feedback parameters meet the preset conditions.
Optionally, before determining that the at least one recommended content includes a second recommended content indicated in a negative feedback log of the user, the method further includes:
determining at least two channels with highest correlation according to the characteristics of the disliked recommended content;
configuring a reason for dislike for the at least two channels respectively;
sending the at least two channels and the dislike reason to a client.
Optionally, after the sending the at least two channels and the reason for dislike to the client, the method further comprises:
receiving a negative feedback log of the user returned by the client; the negative feedback log includes a channel disliked by the user and a reason for the dislike;
acquiring the relative historical negative feedback times of recommended channels disliked by the user;
and generating the preference information according to the negative feedback log, the feedback time for receiving the negative feedback log and the negative feedback times.
Optionally, if the client does not return the negative feedback log, the method further includes:
determining a first channel with the highest correlation degree in the at least two channels as a channel which is not liked by the user;
determining the current time as the feedback time;
acquiring the historical negative feedback times of the channels disliked by the user;
and generating the preference information according to the first channel, the feedback time and the negative feedback times.
In a second aspect, an embodiment of the present invention provides a content recommendation apparatus, where the apparatus includes:
the acquisition module is used for acquiring at least one recommended content to be recommended.
The confirming module is used for confirming that the at least one recommended content comprises a second recommended content marked in the preference information of the user; wherein the preference information includes the user dislikes recommended content and a feedback parameter; the feedback parameters comprise feedback time and/or feedback times.
And the judging module is used for determining whether the feedback parameters meet preset conditions.
And the recommending module is used for recommending the second recommended content to the user when the feedback parameter meets a preset condition.
Optionally, the determining module is configured to determine whether the number of times of feedback is less than a preset number of times; determining whether the distance between the feedback time and the current time point exceeds a preset time length; and when the feedback times are smaller than the preset times or when the feedback time exceeds the preset time length from the current time point, representing that the feedback parameters meet the preset conditions.
Optionally, the apparatus further comprises:
and the negative feedback log acquisition module is used for receiving the negative feedback log of the user returned by the client. The negative feedback log includes recommended content that the user dislikes and a reason for the dislike.
And the negative feedback frequency acquisition module is used for acquiring historical negative feedback frequency related to the recommended content disliked by the user.
And the preference information acquisition module is used for generating the preference information according to the negative feedback log, the feedback time for receiving the negative feedback log and the negative feedback times.
In a third aspect, an embodiment of the present invention provides an electronic device, including a processor and a memory, where the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the steps in the method according to the first aspect are executed.
In a fourth aspect, an embodiment of the present invention provides a readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, performs the steps in the method of the first aspect.
According to the content recommendation method and device, the electronic device and the readable storage medium provided by the embodiment of the invention, the second recommended content is recommended to the client by judging whether the feedback time and/or the feedback times reach the preset condition, so that the situation that the second recommended content is not pushed permanently due to short-term dislike or misoperation of a user can be avoided. Therefore, the practicability is stronger, and the humanization is better.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the embodiments of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a schematic flow chart of a content recommendation method according to an embodiment of the present invention;
fig. 2 is a flowchart illustrating a further content recommendation method according to an embodiment of the present invention;
fig. 3 is a flowchart illustrating a further content recommendation method according to an embodiment of the present invention;
fig. 4 is a flowchart illustrating a further content recommendation method according to an embodiment of the present invention;
fig. 5 is a flowchart illustrating a further content recommendation method according to an embodiment of the present invention;
fig. 6 is a schematic connection diagram of a content recommendation device according to an embodiment of the present invention;
fig. 7 is a schematic connection diagram of a content recommendation device according to an embodiment of the present invention.
Summary of reference numerals:
10-a content recommendation device; 11-an acquisition module; 12-a confirmation module; 13, a judging module; 14-a recommendation module; 110-negative feedback log acquisition module; 112-negative feedback times obtaining module; 113-preference information acquisition module.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings or the orientations or positional relationships that the products of the present invention are conventionally placed in use, and are only used for convenience in describing the present invention and simplifying the description, but do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," "third," and the like are used solely to distinguish one from another and are not to be construed as indicating or implying relative importance. Furthermore, the terms "horizontal", "vertical", "overhang" and the like do not imply that the components are required to be absolutely horizontal or overhang, but may be slightly inclined. For example, "horizontal" merely means that the direction is more horizontal than "vertical" and does not mean that the structure must be perfectly horizontal, but may be slightly inclined.
In the description of the present invention, it should also be noted that, unless otherwise explicitly specified or limited, the terms "disposed," "mounted," "connected," and "connected" are to be construed broadly and may, for example, be fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Referring to fig. 1, a content recommendation method provided in an embodiment of the present invention includes:
s100: at least one recommended content to be recommended is acquired.
S110: and determining that the at least one piece of recommended content comprises second recommended content indicated in the preference information of the user. Wherein the preference information includes recommended content and feedback parameters that the user dislikes. The feedback parameters comprise feedback time and/or feedback times.
S120: and determining whether the feedback parameter meets a preset condition.
S130: and recommending the second recommended content to the user when the feedback parameter meets a preset condition.
Therefore, in the embodiment of the invention, the second recommended content is recommended to the client by judging whether the feedback time and/or the feedback times reach the preset condition, so that the situation that the second recommended content cannot be received due to short-term dislike or misoperation of the user can be avoided. Therefore, the practicability is stronger, and the humanization is better.
The detailed implementation of each step will be described in detail below.
Optionally, when the user inputs an operation of opening the web browser or APP, the server may perform step S100 based on the operation, that is, obtain at least one recommended content to be recommended. The at least one recommended content may be an article, and of course, in practical applications, the content to be recommended may also be other content, such as a video. One content to be recommended may correspond to one or more channels.
Next, step S110 is executed to determine that the at least one recommended content includes a second recommended content related to the first recommended content that is disliked by the user indicated in the preference information of the user. Alternatively, the server may maintain a user profile for each user in which the user's preference information is recorded, e.g., the preference information includes recommended content and feedback parameters that the user does not like. The feedback parameters comprise feedback time and/or feedback times. The feedback parameters will be described in detail later.
In executing step S110, the at least one recommended content acquired in step S100 may be compared with the disliked recommended content in the preference information of the user, and it may be determined whether the recommended content related to the disliked recommended content is included in the at least one recommended content. Specifically, a channel corresponding to each recommended content may be determined according to the characteristics of at least one recommended content, and if a channel is recorded in the preference information, the two channels are directly compared; if the recommended content such as a specific article or video is recorded in the preference information, the channel is determined according to the characteristics of the recommended content recorded in the preference information, and then the channel is compared. In either way, if the channels are the same, the contents to be recommended that represent the same channels are consistent with the disliked recommended contents indicated in the preference information. Of course, if the preference information records specific recommended contents such as articles or videos, the recommended contents may also be directly compared, and if the similarity between the two contents is greater than the preset threshold, it is determined that the contents to be recommended, the similarity of which is greater than the preset threshold, are consistent with the disliked recommended contents indicated in the preference information.
For example, the content to be recommended acquired in step S100 includes three contents, which are content a, content b, and content c. The disliked content recorded in the user preference information includes content a +, content d, and content e. And comparing the content a, the content b, the content c, the content a +, the content d and the content e in sequence, and if the content a + and the content a belong to the same channel, representing that the content a is related to the content a +. The content a is the second recommended content determined in step S110 and related to the content a + disliked by the user and indicated by the preference information.
Step S120 is performed next, i.e. it is determined whether the feedback parameter of the first recommended content satisfies a preset condition. Continuing with the previous example, it is determined whether the feedback parameter of the content a + satisfies the preset condition.
Optionally, referring to fig. 2, in the embodiment of the present invention, S120: determining whether the feedback parameter of the first recommended content satisfies a preset condition includes:
s121: and determining whether the feedback times are less than preset times.
S122: determining whether the distance between the feedback time and the current time point exceeds a preset time length; and when the feedback times are smaller than the preset times or when the feedback time exceeds the preset time length from the current time point, representing that the feedback parameters meet the preset conditions.
Further, the preset time duration may be two weeks, and the preset times may be three times. Of course, the preset time length and the preset times may be set according to actual situations, and are not limited herein. For example, the preset time period may be set to one week when the user feeds back that a certain type of contents is disliked for the first time, and may be set to two weeks when the user feeds back that such contents are disliked again. The preset times can also be set according to the actual situation.
Continuing with the previous example as an example, assuming that the number of times of negative feedback of the content a + is 1 and does not exceed the preset number of times by 3, it may be determined that the feedback parameter of the content a + satisfies the preset condition. Assuming that the time length of the feedback time of the content a + from the current time point is 1 month and exceeds the preset time length by one week, it can be determined that the feedback parameter of the content a + satisfies the preset condition. Of course, it may also be determined that the feedback parameter satisfies the preset condition when the feedback frequency is less than the preset frequency and the feedback time exceeds the preset time from the current time point.
And when the feedback parameter meets a preset condition, executing step S130, that is, recommending the second recommended content to the user. Continuing with the previous example, content a is recommended to the user. Of course, content b and content c are also recommended to the user.
If the method in the prior art is adopted, the content a + is negatively fed back by the user, and the content a is not recommended to the user because the content a is related to the content a + when the content a is fed back, but the method in the embodiment of the invention is adopted, the content a is still recommended to the user, the user is tried again, and the situation that the user cannot receive the content due to short-term dislike is avoided.
Next, an implementation process of generating the preference information will be described in detail.
Alternatively, referring to fig. 3, in step S110: previously, the method further comprises:
s200: and determining at least two channels with highest correlation according to the characteristics of the disliked recommended content.
S210: a reason for dislike is configured for the at least two channels, respectively.
S220: sending the at least two channels and the dislike reason to a client.
Further, the content included in the preference information is disliked content and disliked times, and the relationship between the content to be recommended and the preference information is determined every time the content is recommended to the user. For example: and when the recommended content is the content associated with the content in the preference information, whether the content to be recommended meets a preset condition is judged, if the preset condition is withheld, the user is recommended, and if the content to be recommended does not meet the preset condition, the user is not recommended. And when the recommended content is not the associated content with the content in the preference information, directly recommending the recommended content to the user.
Optionally, referring to fig. 4, before step S220, the method further includes:
s230: receiving a negative feedback log of the user returned by the client; the negative feedback log includes the channels the user dislikes and the reason for the dislikes.
S240: and acquiring the relative historical negative feedback times of the recommended channels disliked by the user.
S250: and generating the preference information according to the negative feedback log, the feedback time for receiving the negative feedback log and the negative feedback times.
For example, when web page recommendation is performed, the recommended content is titled as a ping-pong match between the height of the athlete and the queen of the athlete; for such articles, the extracted keywords may be little high, little king and ping-pong; when the user dislikes, the interface pops up these options, one or more of which can be selected by the user. And writing the option selected by the user into the preference information based on the option selected by the user, and shielding the content written into the preference information when recommending the content to the user. However, for some users, it is troublesome to select options, and at this time, one option may be randomly and default to be written into the preference information.
For example, in an article such as "station media exposition star 1 is captured and also marries star 2 into a puzzle," the technology will first identify the most relevant content of the article, such as "entertainment," "star 1," "star 2," "poor quality content," etc. These are options that the user can see when doing negative feedback. These options are available when an article is recommended to the user and the user does not like to perform the negative feedback operation, contributing to the reason why the user does not like the article precisely. After the user operates, the information such as time and the like is recorded in the user preference information, and the article recommended later is filtered according to the signals
Optionally, referring to fig. 5, if the client does not return the negative feedback log, the method further includes:
s231: determining a first channel with the highest correlation degree in the at least two channels as a channel which is not liked by the user;
s232: determining the current time as the feedback time;
s233: acquiring the historical negative feedback times of the channels disliked by the user;
and generating the preference information according to the first channel, the feedback time and the negative feedback times. Furthermore, when recommending content to a user each time, the fields related to the recommended content can be wider, the recommended content is not a single field, but the contents in the fields are pushed to the user together, and the situation that the fields are popular with the user due to the fact that the information received by the user is too single is avoided.
For example, the content at each time of recommendation to the user may be a plurality of contents among sports content, entertainment content, financial content, military content, and the like, and when the user dislikes a certain category of content, the manner of subsequent push is selected according to the reason of the dislike.
For example: when the recommended content to the user is a meeting transferring message of a basketball player xx, the reason why the user dislikes is xx; at this time, the system records xx only in the preference information of the user, and the system can recommend the meeting information of yy basketball player to the user. If the reason the user dislikes is basketball, the user will not be recommended the content related to basketball, but football, ping-pong or tennis may be recommended to the user. Therefore, the preference of the user can be further expanded, and the message received by the user is more comprehensive.
Optionally, referring to fig. 6, an embodiment of the present invention provides a content recommendation device 10, where the device 10 includes:
the obtaining module 11 is configured to obtain at least one recommended content to be recommended.
The confirming module 12 is configured to determine that the at least one piece of recommended content includes a second piece of recommended content indicated in the preference information of the user; wherein the preference information includes the user dislikes recommended content and a feedback parameter; the feedback parameters comprise feedback time and/or feedback times.
And the judging module 13 is configured to determine whether the feedback parameter meets a preset condition.
And the recommending module 14 is configured to recommend the second recommended content to the user when the feedback parameter meets a preset condition.
Optionally, the determining module 13 is configured to determine whether the feedback times are less than a preset number; determining whether the distance between the feedback time and the current time point exceeds a preset time length; and when the feedback times are smaller than the preset times or when the feedback time exceeds the preset time length from the current time point, representing that the feedback parameters meet the preset conditions.
Optionally, referring to fig. 7, the apparatus 10 further includes:
and a negative feedback log obtaining module 110, configured to receive a negative feedback log of the user returned by the client. The negative feedback log includes recommended content that the user dislikes and a reason for the dislike.
A negative feedback time obtaining module 112, configured to obtain a historical negative feedback time related to the recommended content that the user dislikes.
A preference information obtaining module 113, configured to generate the preference information according to the negative feedback log, the feedback time for receiving the negative feedback log, and the negative feedback times.
Optionally, an embodiment of the present invention further provides an electronic device, including a processor and a memory, where the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the steps in the method according to the first aspect are executed.
In a fourth aspect, the present invention further provides a readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to execute the steps in the method of the first aspect.
The content recommendation method and device, the electronic device and the readable storage medium provided by the embodiment of the invention comprise the following steps: at least one recommended content to be recommended is acquired. And determining that the at least one piece of recommended content comprises second recommended content indicated in the preference information of the user. Wherein the preference information includes the user dislikes the recommended content and the feedback parameter. The feedback parameters comprise feedback time and/or feedback times. And determining whether the feedback parameter meets a preset condition. And recommending the second recommended content to the user when the feedback parameter meets a preset condition. The second recommended content is recommended to the client by judging whether the feedback time and/or the feedback times reach the preset conditions, so that the situation that the second recommended content cannot be received due to short-term dislike or misoperation of the user can be avoided. Therefore, the practicability is stronger, and the humanization is better.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (5)
1. A content recommendation method, comprising:
acquiring at least one recommended content to be recommended;
determining that the at least one recommended content includes a second recommended content related to a first recommended content that is disliked by the user and indicated in the preference information of the user; wherein the preference information further comprises a feedback parameter; the feedback parameters comprise feedback time and/or feedback times for feeding back the first recommended content;
determining whether the feedback parameter of the first recommended content meets a preset condition;
recommending the second recommended content to the user when the feedback parameter meets a preset condition;
before determining that the at least one recommended content includes a second recommended content indicated in a negative feedback log of the user, the method further includes:
determining at least two channels with highest correlation according to the characteristics of the disliked first recommended content;
configuring a reason for dislike for the at least two channels respectively;
sending the at least two channels and the dislike reason to a client;
receiving a negative feedback log of the user returned by the client; the negative feedback log includes a channel disliked by the user and a reason for the dislike;
acquiring the relative historical negative feedback times of recommended channels disliked by the user;
generating the preference information according to the negative feedback log, the feedback time for receiving the negative feedback log and the negative feedback times;
if the client does not return the negative feedback log, the method further comprises:
determining a first channel with the highest correlation degree in the at least two channels as a channel which is not liked by the user;
determining the current time as the feedback time;
acquiring the historical negative feedback times of the channels disliked by the user;
and generating the preference information according to the first channel, the feedback time and the negative feedback times.
2. The content recommendation method according to claim 1, wherein said determining whether the feedback parameter satisfies a preset condition comprises:
determining whether the feedback times are less than preset times;
determining whether the distance between the feedback time and the current time point exceeds a preset time length; and when the feedback times are smaller than the preset times or when the feedback time exceeds the preset time length from the current time point, representing that the feedback parameters meet the preset conditions.
3. A content recommendation apparatus, characterized in that the apparatus comprises:
the acquisition module is used for acquiring at least one recommended content to be recommended;
the confirming module is used for confirming that the at least one recommended content comprises a second recommended content marked in the preference information of the user; wherein the preference information includes the user dislikes recommended content and a feedback parameter; the feedback parameters comprise feedback time and/or feedback times;
the judging module is used for determining whether the feedback parameters meet preset conditions or not;
the recommending module is used for recommending the second recommended content to the user when the feedback parameter meets a preset condition;
the judging module is also used for determining whether the feedback times are less than preset times; determining whether the distance between the feedback time and the current time point exceeds a preset time length; when the feedback times are smaller than the preset times or the feedback time exceeds the preset time from the current time point, representing that the feedback parameters meet the preset conditions;
the negative feedback log acquisition module is used for receiving the negative feedback log of the user returned by the client; the negative feedback log includes recommended contents disliked by the user and a reason for the dislike;
a negative feedback frequency acquisition module used for acquiring the historical negative feedback frequency related to the recommended content disliked by the user;
the preference information acquisition module is used for generating the preference information according to the negative feedback log, the feedback time for receiving the negative feedback log and the negative feedback times;
prior to determining that the at least one recommended content includes a second recommended content indicated in a negative feedback log of the user,
determining at least two channels with highest correlation according to the characteristics of the disliked recommended content;
configuring a reason for dislike for the at least two channels respectively;
sending the at least two channels and the dislike reason to a client;
receiving a negative feedback log of the user returned by the client; the negative feedback log includes a channel disliked by the user and a reason for the dislike;
acquiring the relative historical negative feedback times of recommended channels disliked by the user;
generating the preference information according to the negative feedback log, the feedback time for receiving the negative feedback log and the negative feedback times;
if the client does not return the negative feedback log,
determining a first channel with the highest correlation degree in the at least two channels as a channel which is not liked by the user;
determining the current time as the feedback time;
acquiring the historical negative feedback times of the channels disliked by the user;
and generating the preference information according to the first channel, the feedback time and the negative feedback times.
4. An electronic device comprising a processor and a memory, said memory storing computer readable instructions which, when executed by said processor, perform the steps of the method of any of claims 1-2.
5. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1-2.
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| CN111753177A (en) * | 2019-03-11 | 2020-10-09 | 阿里巴巴集团控股有限公司 | Personalized recommendation method and device and computer storage medium |
| CN111753182A (en) * | 2019-03-28 | 2020-10-09 | 北京达佳互联信息技术有限公司 | Multimedia information recommendation method and device, electronic equipment and readable storage medium |
| CN112905904B (en) * | 2019-11-19 | 2024-01-26 | 北京达佳互联信息技术有限公司 | Recommendation method, recommendation device, server and storage medium |
| CN111523050B (en) * | 2020-04-16 | 2023-09-19 | 咪咕文化科技有限公司 | Content recommendation method, server and storage medium |
| CN112906387B (en) * | 2020-12-25 | 2023-08-04 | 北京百度网讯科技有限公司 | Risk content identification method, apparatus, device, medium and computer program product |
| CN113518263B (en) * | 2021-07-23 | 2023-02-10 | 南京炫佳网络科技有限公司 | Video recommendation method and device for interactive network television, television and storage medium |
| CN113343024B (en) * | 2021-08-04 | 2021-12-07 | 北京达佳互联信息技术有限公司 | Object recommendation method and device, electronic equipment and storage medium |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101840301A (en) * | 2009-03-18 | 2010-09-22 | 索尼公司 | Signal conditioning package and information processing method |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6138128A (en) * | 1997-04-02 | 2000-10-24 | Microsoft Corp. | Sharing and organizing world wide web references using distinctive characters |
| US8387093B2 (en) * | 2002-08-23 | 2013-02-26 | Microsoft Corporation | Media recommendations based on negative feedback |
| BR0316008A (en) * | 2002-11-08 | 2005-09-13 | Koninkl Philips Electronics Nv | Method for providing a content recommendation for a user, computer program, recommendation device for providing a content recommendation for a user, and private video recorder |
| US7734641B2 (en) * | 2007-05-25 | 2010-06-08 | Peerset, Inc. | Recommendation systems and methods using interest correlation |
| CN103823908B (en) * | 2014-03-21 | 2017-07-14 | 北京飞流九天科技有限公司 | Content recommendation method and server based on user preference |
| US10026115B2 (en) * | 2015-04-01 | 2018-07-17 | Amazon Technologies, Inc. | Data collection for creating apparel size distributions |
| CN106651448A (en) * | 2016-12-23 | 2017-05-10 | 腾讯科技（深圳）有限公司 | Advertisement information sending method, apparatus and system |
-
2018
- 2018-09-10 CN CN201811054068.2A patent/CN109145221B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101840301A (en) * | 2009-03-18 | 2010-09-22 | 索尼公司 | Signal conditioning package and information processing method |
Non-Patent Citations (2)
| Title |
|---|
| "一种基于用户反馈的时间感知推荐方法";邢星、贾志淳、杨玉强;《计算机工程与应用》;20151130;第51卷(第22期);第141-144页 * |
| "一种基于负反馈的协同过滤推荐系统";蔡宇辰;《电脑知识与技术》;20151228;第11卷(第26期);第181-183页 * |
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