CN108734549B - Item recommendation method and device - Google Patents
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Abstract
The application discloses an article recommendation method and device. The method comprises the steps of obtaining a plurality of recommended articles and a first recommended value corresponding to each recommended article according to N target articles, and obtaining a first recommended value sequence of each recommended article, wherein N is an integer larger than 0, and each recommended article is related to at least one target article; and acquiring the generation time of each recommended article, performing data processing on the first recommended value of each recommended article according to the current time and the generation time of each recommended article, and acquiring the second recommended value of each recommended article, so as to obtain the second recommended value sequence of each recommended article. The method and the device solve the technical problems that in the prior art, the recommended articles recommended are poor in timeliness, and the interest of a user in the recommended articles is reduced.
Description
Technical Field
The application relates to the technical field of data application, in particular to an article recommendation method and device.
Background
With the rapid development of the internet, the application scenarios of the item (item) recommendation method are more and more, and the item (item) recommendation method is widely integrated into a plurality of business application systems, and the Netflix online video recommendation system, Amazon online shopping mall, today's top-of-the-day, and the like are known. In fact, most information platforms apply item recommendation methods to different degrees, such as news in New wave, today's headlines, etc. The related recommendation provided by the article recommendation method can help a user to better find the articles related to the current articles, related commodities can be sold in a binding mode in an e-commerce platform, related information recommended by the article recommendation method can be used for deep reading or interest discovery and the like in an information platform, and the method is an important application scene for showing the value of a recommendation system.
In the prior art, an article recommendation method is to recommend based on user behaviors, generally based on historical behavior records of users, determine similarity between articles, and recommend according to the similarity of the articles.
In the process of implementing the embodiment of the present application, the inventor finds that the prior art has at least the following technical problems:
in the prior art, the relevance between the recommended item recommended to the user and the interest of the user is high, but the timeliness of the recommended item is poor due to the fact that the recommended item generation time is too long, and the interest of the user in the recommended item is reduced.
Disclosure of Invention
The application mainly aims to provide an article recommendation method and device, so as to solve the problems that the timeliness of recommended articles is poor, and the interest of a user in the recommended articles is reduced.
In order to achieve the above object, in a first aspect, an embodiment of the present application provides an item recommendation method.
The item recommendation method according to the application comprises the following steps:
obtaining a plurality of recommended articles and a first recommended value corresponding to each recommended article according to the N target articles, wherein N is an integer greater than 0, and each recommended article is related to at least one target article;
and acquiring the generation time of each recommended article, performing data processing on the first recommended value of each recommended article according to the current time and the generation time of each recommended article, and acquiring the second recommended value of each recommended article, so as to obtain the second recommended value sequence of each recommended article.
Optionally, the data processing the first recommended value of each recommended item according to the current time and the generation time of each recommended item, and obtaining the second recommended value of each recommended item includes:
acquiring the generation duration of each recommended item according to the current time and the generation time of each recommended item;
acquiring an aging coefficient of each recommended article according to the first preset time duration evaluation value and the generation time duration of each recommended article;
and calculating the first recommended value of each recommended article according to the aging coefficient of each recommended article to obtain a second recommended value of each recommended article.
Optionally, obtaining an aging factor of each recommended item according to the first preset duration evaluation value and the generation duration of each recommended item, where the obtaining includes:
acquiring a first time length evaluation value of each recommended item according to the generation time length of each recommended item;
and according to the ratio of the first preset time length evaluation value to the first time length evaluation value of each recommended article, the aging factor of each recommended article.
Optionally, the obtaining a first time length evaluation value of each recommended item according to the generation time length of each recommended item includes:
acquiring a second time length evaluation value of each recommended article according to the ratio of the generation time length of each recommended article to the unit time;
and acquiring a first time length evaluation value of each recommended article according to the sum of the second preset time length evaluation value and the second time length evaluation value of each recommended article.
Optionally, the item recommendation method further includes:
acquiring N target articles and recommendation factors corresponding to each target article according to the historical behavior data of the user;
obtaining a plurality of recommended articles and a first recommended value corresponding to each recommended article according to the N target articles, including:
obtaining a plurality of recommended articles and a third recommended value corresponding to each recommended article according to the N target articles, and obtaining a third recommended value sequence of each recommended article;
and performing data processing on the third recommended value corresponding to each recommended article according to the third recommended value sequence and the recommendation factor corresponding to each target article to obtain a first recommended value corresponding to each recommended article.
In a second aspect, an article recommendation device provided in an embodiment of the present application includes:
the first obtaining module is used for obtaining a plurality of recommended articles and a first recommended value corresponding to each recommended article according to N target articles, wherein N is an integer larger than 0, and each recommended article is related to at least one target article;
and the second obtaining module is used for obtaining the generation time of each recommended article, performing data processing on the first recommended value of each recommended article according to the current time and the generation time of each recommended article, and obtaining the second recommended value of each recommended article so as to obtain the second recommended value sequence of each recommended article.
Optionally, the second obtaining module is configured to:
acquiring the generation duration of each recommended item according to the current time and the generation time of each recommended item;
acquiring an aging coefficient of each recommended article according to the first preset time duration evaluation value and the generation time duration of each recommended article;
and calculating the first recommended value of each recommended article according to the aging coefficient of each recommended article to obtain a second recommended value of each recommended article.
Optionally, the second obtaining module is configured to:
acquiring a first time length evaluation value of each recommended item according to the generation time length of each recommended item;
and according to the ratio of the first preset time length evaluation value to the first time length evaluation value of each recommended article, the aging factor of each recommended article.
Optionally, the second obtaining module is configured to:
acquiring a second time length evaluation value of each recommended article according to the ratio of the generation time length of each recommended article to the unit time;
and acquiring a first time length evaluation value of each recommended article according to the sum of the second preset time length evaluation value and the second time length evaluation value of each recommended article.
Optionally, the article recommendation device further includes a third obtaining module;
the third acquisition module is used for acquiring N target articles and recommendation factors corresponding to each target article according to the historical behavior data of the user;
a first obtaining module to:
obtaining a plurality of recommended articles and a third recommended value corresponding to each recommended article according to the N target articles, and obtaining a third recommended value sequence of each recommended article;
and performing data processing on the third recommended value corresponding to each recommended article according to the third recommended value sequence and the recommendation factor corresponding to each target article to obtain a first recommended value corresponding to each recommended article.
According to the item recommendation method provided in the embodiment of the application, a plurality of recommended items and a first recommendation value corresponding to each recommended item are obtained according to N target items, and the first recommendation value sequence of each recommended item is obtained, wherein N is an integer greater than 0, and each recommended item is related to at least one target item; and acquiring the generation time of each recommended article, performing data processing on the first recommended value of each recommended article according to the current time and the generation time of each recommended article, and acquiring the second recommended value of each recommended article, so as to obtain the second recommended value sequence of each recommended article. Therefore, the recommended articles related to the target articles are subjected to timeliness processing, the recommended value of the recommended articles with good timeliness is improved, the purpose of recommending the recommended articles with good timeliness to the user is achieved, the recommended articles with good timeliness and high relevance to the interest of the user are recommended to the user, the technical effect that the user is interested in the recommended articles is improved, and the technical problems that the recommended articles recommended by the prior art are poor in timeliness and the interest of the user in the recommended articles is reduced are solved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, serve to provide a further understanding of the application and to enable other features, objects, and advantages of the application to be more apparent. The drawings and their description illustrate the embodiments of the invention and do not limit it. In the drawings:
FIG. 1 is a flow chart of a method of item recommendation according to an embodiment of the present application;
FIG. 2 is a flowchart of a step S200 according to an embodiment of the present application;
FIG. 3 is a flowchart of a step S220 according to an embodiment of the present application;
fig. 4 is a flowchart of a step S221 according to an embodiment of the present application;
FIG. 5 is a flow chart of another method of item recommendation according to an embodiment of the present application;
FIG. 6 is a flowchart of a step S120 according to an embodiment of the present application;
FIG. 7 is a flowchart of a step S123 according to an embodiment of the present application;
FIG. 8 is a flowchart of a step S233 according to an embodiment of the present application;
FIG. 9 is a flowchart of a step S110 according to an embodiment of the present application;
FIG. 10 is a schematic diagram of an article recommendation device according to an embodiment of the present application;
fig. 11 is a schematic structural diagram of another article recommendation device according to an embodiment of the application.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. 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 application.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
An embodiment of the present application provides an item recommendation method, as shown in fig. 1, the method includes the following steps S100 and S200:
s100, obtaining a plurality of recommended articles and a first recommended value corresponding to each recommended article according to N target articles, wherein N is an integer larger than 0, and each recommended article is related to at least one target article.
In this embodiment, the target object may also be information in an information platform, for example, the target object may be a sports news or an entertainment news; the target object may also be a video in a video platform, for example, the target object may be a movie or a tv show; the target object can be a commodity or a service item in an e-commerce platform, for example, the target object can be an electronic product or a consultation service item on a certain e-commerce platform. Correspondingly, the recommended article related to the target article can be information in an information platform; the video platform can also be used for video, and goods or service items in the E-commerce platform can also be used.
In an implementation, the method for obtaining a plurality of recommended items related to the target item may be a plurality of recommended items related to the target item generated based on a content recommendation method, a plurality of recommended items related to the target item generated according to a user collaborative recommendation method or an item collaborative recommendation method, a plurality of recommended items related to the target item generated according to a business rule recommendation method, or a union set generated by the methods, where the union set includes the plurality of recommended items related to the target item generated by the methods, and the method obtains the plurality of recommended items related to the target item and obtains a recommendation value corresponding to each recommended item.
Wherein, the value of N can be 1, 2, 3, 4, 5 … …
S200, acquiring the generation time of each recommended article, performing data processing on the first recommended value of each recommended article according to the current time and the generation time of each recommended article, and acquiring the second recommended value of each recommended article, so as to obtain the second recommended value sequence of each recommended article.
In this embodiment, the generation time of the recommended item is the generation time of a Uniform Resource Locator (URL) of the recommended item; the current time is the time at which the recommendation was generated.
In the implementation, the first recommended value of each recommended item is subjected to data processing according to the current time and the generation time of each recommended item, so that in the process of carrying out data processing on the first recommended value of each recommended item, the influence of a time factor on the first recommended value is added, the timeliness of the recommended items is considered, the purpose of recommending the recommended items with good timeliness to the user is achieved, the recommended items with good timeliness and high relevance to the interest of the user are recommended to the user, and the technical effect of improving the interest of the user on the recommended items is achieved.
As shown in fig. 2, optionally, step S200, performing data processing on the first recommended value of each recommended item according to the current time and the generation time of each recommended item, and obtaining the second recommended value of each recommended item, includes steps S210 to S230 as follows:
and S210, acquiring the generation duration of each recommended item according to the current time and the generation time of each recommended item.
In implementation, the generation time of the recommended item, that is, the length of time that the recommended item exists from the generation to the current time, may be obtained by subtracting the generation time of the recommended item from the current time, and the time unit of the generation time may be seconds, minutes, hours, days, and the like.
For example, if a recommended item is a piece of sports news about the west, the generation time of the sports news about the west is 18 hours, 35 minutes and 18 seconds at 4 months, 17 days, 2018 years, 4 months, 19 days, 19 hours, 45 minutes and 30 seconds at 2018 years, the generation time of the sports news about the west is specifically 2 days, 1 hour, 10 minutes and 12 seconds, the generation time of the sports news about the west is 49.17 hours when the time unit is hour, and the generation time of the sports news about the west is 177012 seconds when the time unit is second.
S220, acquiring the aging factor of each recommended item according to the first preset time length evaluation value and the generation time length of each recommended item.
In this embodiment, the first preset time duration evaluation value is a preset value, and may be set or adjusted according to actual needs, and the first preset time duration evaluation value may be a constant, or may be a value having a time unit.
In implementation, the aging factor of the recommended article is obtained by calculating the production time length of the recommended article and the first preset time length evaluation value.
For example, if a recommended item is social news about an ecological environment, the generation time of the social news about the ecological environment is 30 minutes, the first preset time duration evaluation value is 1440 minutes, and the operation is a ratio of the first preset time duration to the generation time duration, then the aging factor of the social news about the ecological environment is 48 (a ratio of 1440 minutes to 30 minutes).
And S230, calculating the first recommended value of each recommended article according to the aging factor of each recommended article to obtain a second recommended value of each recommended article.
In this embodiment, the first recommended value of the recommended item is calculated according to the aging factor of the recommended item, so as to obtain the second recommended value of the recommended item.
For example, if the operation is the product of the aging factor of the recommended item and the first recommended value of the recommended item, the aging factor of the recommended item is 2, the first recommended value of the recommended item is 0.9001, and then the second recommended value of the recommended item is 1.8002(2 is multiplied by 0.9001).
As shown in fig. 3, optionally, in step S220, obtaining an aging factor of each recommended item according to the first preset time duration evaluation value and the generation time duration of each recommended item, includes the following steps S221 to S222:
and S221, acquiring a first time length evaluation value of each recommended item according to the generation time length of each recommended item.
In this embodiment, the evaluation value corresponding to the generation time length may be obtained according to the generation time length of the recommended item, that is, the first time length evaluation value, and the first time length evaluation value may be obtained by performing data processing on the generation time length of the recommended item, for example, by dividing the generation time length of the recommended item by one unit time, so as to obtain the first time length evaluation value corresponding to the generation time length.
For example, if the generation time of a certain recommended item is 4 days and the unit time is day, the first time evaluation value may be 4.
S222, according to the ratio of the first preset time length evaluation value to the first time length evaluation value of each recommended article, the aging factor of each recommended article.
In this embodiment, the aging factor of the recommended item may be a ratio obtained from a ratio of the first preset time period evaluation value to the first time period evaluation value of the recommended item.
For example, if the first preset time period evaluation value is 1000 and the first time period evaluation value of a recommended item is 5, the aging factor of the recommended item is 200(1000 divided by 5).
As shown in fig. 4, optionally, the step S221 of acquiring the first time length evaluation value of each recommended item according to the generation time length of each recommended item includes the following steps S2211 to S2212:
and S2211, acquiring a second time length evaluation value of each recommended article according to the ratio of the generation time length of each recommended article to the unit time.
In this embodiment, the second time period evaluation value of the recommended item may be a ratio obtained from a ratio of the generation time period of the recommended item to the unit time.
For example, if the generation time period of a certain recommended item is 30 hours and the unit time is hours, then the second time period evaluation value of the recommended item is 30, and for example, if the generation time period of another recommended item is 0, that is, if the recommended item is generated at the current time, then the second time period evaluation value of the recommended item is 0.
And S2212, acquiring the first time length evaluation value of each recommended item according to the sum of the second preset time length evaluation value and the second time length evaluation value of each recommended item.
In the present embodiment, to prevent the first time length evaluation value from being 0, the first time length evaluation value is the sum of a second preset time length evaluation value, which is a value greater than 0, and a second time length evaluation value of each recommended item.
For example, the second preset time period evaluation value may be 1, and when the second time period evaluation value of a certain recommended item is 0, the first time period evaluation value of the recommended item is 1.
In this embodiment, in S200, the generation time of each recommended item is obtained, data processing is performed on the first recommended value of each recommended item according to the current time and the generation time of each recommended item, and the obtaining of the second recommended value of each recommended item specifically includes:
according to the generation time and the current time of the recommended article, the timeliness adjustment is carried out on the first recommended value of the recommended article to obtain a second recommended value of the recommended article, and the formula is as follows:
wherein, the left rec _ score of equal signiIs the second recommended value of the ith recommended item, rec _ score _ i on the right side of the equal sign is the first recommended value of the ith recommended item, cur _ time is the current time, the time unit is second, and create _ timeiIs the generation time of the ith recommended item, the time unit is second, consttime1The evaluation value of the first preset duration evaluation value may be specifically set by a person skilled in the art according to actual needs, for example, the evaluation value of the first preset duration evaluation value may be 1, 5, 10, 30, 100, 300, 1000, and the like; in the formula, consttime2The evaluation value of the second preset duration evaluation value may be specifically set by a person skilled in the art according to actual needs, for example, the evaluation value of the second preset duration evaluation value may be 1, and the like; in the formula, (cur _ time-create _ timei) Is the generation duration of the ith recommended item, (cur _ time-create _ time)i) /86400 is the second time period evaluation value of the ith recommended item, (consttime2+(cur_time-create_timei) 86400) is the first time length evaluation value of the ith recommended item.
Optionally, as shown in fig. 5, the item recommendation method further includes step S010:
s010, acquiring N target articles and recommendation factors corresponding to each target article according to the historical behavior data of the user;
step S100, acquiring a plurality of recommended items and a first recommended value corresponding to each recommended item according to the N target items, including the following steps S110 and S120:
s110, acquiring a plurality of recommended articles and a third recommended value corresponding to each recommended article according to the N target articles, and obtaining a third recommended value sequence of each recommended article;
and S120, performing data processing on the third recommended value corresponding to each recommended article according to the third recommended value sequence and the recommendation factor corresponding to each target article to obtain a first recommended value corresponding to each recommended article.
In this embodiment, in step 010, N target items are obtained according to the user historical behavior data, specifically: extracting M operation articles from the latest operation records (operation articles on the current day) in the user history browsing according to the category information, the relevance and the operation time of the operation articles as target articles of recent interest; and (3) by using historical browsing data of a user (generally, operating articles of 3-5 days are adopted), and Q operating articles extracted from the historical browsing data are taken as target articles of historical interest according to the category information and the correlation of the operating articles, wherein M + Q is N. And generates recommendation factors for each target item, wherein each recommendation factor can be a topic (such as a topic related to a certain entertainment star) or business information or experience information. The service information can be set for service requirements, for example, articles related to certain topics need to be popularized; the experience information may be historical experience, for example, when the title of an item contains a specific word, for example, when the target item is sports news, the content of the target item contains "mei xi", and the recommendation factor is related to "mei xi".
In implementation, in step S110, recommended article screening is performed on N target articles according to an existing article recommendation method, a plurality of recommended articles are obtained, a third recommended value corresponding to each recommended article is obtained, and a third recommended value ranking of each recommended article is obtained. In step S120, each recommended item corresponds to at least one target item, and each target item corresponds to one recommendation factor, so that each recommended item corresponds to at least one recommendation factor, and the third recommendation value of the recommended item is calculated according to the serial number position of the recommended item in the third recommendation value ranking and the corresponding recommendation factor, so as to obtain the first recommendation value of the recommended item. And then obtaining the first recommended value sequence of each recommended article.
Optionally, referring to fig. 6, in step S120, performing data processing on the third referral value corresponding to each recommended item according to the third referral value ranking and the recommendation factor corresponding to each target item, and obtaining the first recommendation value corresponding to each recommended item, includes the following steps S121 to S123:
and S121, determining a recommendation factor corresponding to each recommended item according to each recommended item and the related target item.
In this embodiment, each recommended item corresponds to at least one target item, and each target item corresponds to one recommendation factor, so that each recommended item corresponds to at least one recommendation factor.
And S122, obtaining the corresponding recommendation factor sequence of each recommended article according to the third recommendation value sequence and the recommendation factor corresponding to each recommended article, and determining the sequence number of each recommended article in the corresponding recommendation factor sequence.
In this embodiment, according to the sequence number position of the recommended item in the third recommended value sequence, the sequence between a plurality of recommended items corresponding to the same recommendation factor is determined, so as to determine the sequence number of each recommended item in the corresponding recommendation factor sequence.
For example, there are 3 recommended articles corresponding to one recommendation factor, which are respectively a first recommended article, a second recommended article, and a third recommended article, where sequence numbers of the first recommended article, the second recommended article, and the third recommended article in the third recommended value ranking are respectively 2 nd, 9 th, and 4 th, and then sequence numbers of the 3 recommended articles relative to the corresponding recommendation factor ranking are respectively: the first recommended article corresponds to the serial number 1, the second recommended article corresponds to the serial number 3, and the third recommended article corresponds to the serial number 2.
And S123, performing data processing on the third recommended value corresponding to each recommended article according to the serial number of each recommended article in the corresponding recommendation factor sequence, and acquiring the first recommended value corresponding to each recommended article.
In this embodiment, data processing is performed on the third recommended value corresponding to the recommended item according to the serial number of the recommended item in the corresponding recommendation factor ranking, and the data processing may be implemented by the following formula:
in the formula, rec _ score is on the left side of equal signiIs the first recommended value of the ith recommended item, rec _ score on the right side of the equal signiIs the third recommended value for the ith recommended item,
is the serial number of the ith recommended item in the corresponding recommendation factor ranking (or the occurrence number of the recommendation factors when the third recommendation value ranking is descending ranking), constrsnThe recommendation factor is a preset constant corresponding to each recommendation factor, optionally, the preset constant may select different values according to different recommendation factors, and the preset constant may also adopt a preset fixed value, for example, the value may be 1.
In addition, optionally, when the recommended item has at least two corresponding first recommended values, a maximum first recommended value of the at least two first recommended values of the recommended item is selected as the first recommended value corresponding to the recommended item.
Optionally, as shown in fig. 7, in step S123, performing data processing on the third recommended value corresponding to each recommended item according to the serial number of each recommended item in the corresponding recommendation factor ranking, and obtaining the first recommended value corresponding to each recommended item, includes the following steps S231 to S233:
s231, performing data processing on the third recommended value corresponding to each recommended article according to the serial number of each recommended article in the corresponding recommendation factor sequence, and acquiring the fourth recommended value corresponding to each recommended article, so as to obtain the fourth recommended value descending sequence of each recommended article.
In this embodiment, data processing is performed on the third recommended value corresponding to the recommended item according to the serial number of the recommended item in the corresponding recommendation factor ranking, and the data processing may be implemented by the following formula:
in the formula, rec _ score is on the left side of equal signiIs the fourth recommended value of the ith recommended item, rec _ score on the right side of the equal signiIs the third recommended value for the ith recommended item,
is the serial number of the ith recommended item in the corresponding recommendation factor ranking (or when the third recommendation value ranking is in descending order,
is the number of occurrences in the third recommendation value ordering that can be of the same recommendation factor), constrsnIs a preset constant corresponding to each recommendation factor.
S232, in the descending sorting of the fourth recommended value of each recommended article, the similarity value of each recommended article relative to the previous recommended article is obtained, wherein the similarity value included in the recommended article ranked at the first position is a first preset value.
In this embodiment, the recommended item ranked first in the descending order of the fourth recommended value has no recommended item in front of it, so the similarity value of the recommended item ranked first may be set to a first preset value, where optionally, the value of the first preset value may be 0.
In implementation, when the similarity value of each recommended item with respect to the previous recommended item is obtained, the similarity value of the recommended item with respect to the previous recommended item may be calculated by using titles (titles) included in two adjacent recommended items, where the calculation method may adopt an existing similarity calculation method.
And S233, performing data processing on the fourth recommended value corresponding to each recommended article according to the similarity value included in each recommended article, and acquiring the first recommended value of each recommended article.
In this embodiment, the fourth recommended value corresponding to each recommended item is subjected to data processing according to the similarity value included in each recommended item, so that the fourth recommended value corresponding to each recommended item is adjusted according to the content information, and the first recommended value of each recommended item is generated. Therefore, the content similarity of two adjacent recommended articles can be prevented, and the recommendation experience of the user is further influenced.
Optionally, as shown in fig. 8, in S233, performing data processing on the fourth recommended value corresponding to each recommended item according to the similarity value included in each recommended item, and acquiring the first recommended value of each recommended item, the method includes the following steps S331 and S332:
and S331, obtaining the dissimilarity value of each recommended article relative to the previous recommended article according to the similarity value included by each recommended article.
S332, performing data processing on the fourth recommended value corresponding to each recommended article according to the dissimilarity value included in each recommended article, and acquiring the first recommended value of each recommended article.
In this embodiment, the first recommended value of the recommended item may be a product of a dissimilarity value of the recommended item with respect to a previous recommended item and a fourth recommended value corresponding to the recommended item.
For example, if the fourth recommended value corresponding to a recommended item is 0.8 and the dissimilarity value of the recommended item with respect to the previous recommended item is 0.3, the first recommended value of the recommended item is 0.24 (product of 0.8 and 0.3).
Optionally, when the dissimilarity value of the recommended item relative to the previous recommended item is smaller than a second preset value, the second preset value is selected as the dissimilarity of the recommended item.
In implementation, the second preset value may be set to prevent that the dissimilarity value of the recommended item with respect to the previous recommended item is too small, which may result in that the first recommended value of the recommended item is too small, and further affect the processing operation of the subsequent other steps on the recommended item.
In this embodiment, data processing is performed on the fourth recommended value corresponding to each recommended item according to the similarity value included in each recommended item, so as to obtain the first recommended value of each recommended item, where the data processing may be implemented by the following formula:
rec_scorei＝rec_scorei*max((1-sim(itemi,itemi-1)),a)
wherein, the left rec _ score of equal signiIs the first recommended value of the ith recommended item, rec _ score on the right side of the equal signiIs the fourth recommended value, sim (item), for the ith recommended itemi,itemi-1) (ii) is the similarity value of the ith recommended item to the previous recommended item, (1-sim (item)i,itemi-1) And a is a second preset value, where the value of the second preset value may be 0.08, 0.1, 0.2, and the like, and those skilled in the art may specifically set the value according to actual needs.
Optionally, as shown in fig. 9, in S110, obtaining a plurality of recommended items and a third recommended value corresponding to each recommended item according to the N target items, and obtaining a third recommended value ranking of each recommended item, the method includes the following steps S1101 to S1107:
s1101, obtaining at least one recommendation set according to each target article, wherein each recommendation set comprises a plurality of recommendation articles related to the target article, and each recommendation article corresponds to a fifth recommendation value.
In this embodiment, each target item may be subjected to recommended item screening by using each existing item recommendation method, so as to obtain one recommendation set.
And S1102, normalizing the fifth recommended value corresponding to the recommended article in each recommended set, and acquiring a sixth recommended value corresponding to each recommended article.
In this embodiment, normalization processing is performed on the fifth recommended value corresponding to the recommended item included in each recommended set, and a sixth recommended value corresponding to each recommended item is obtained, which may be implemented by the following formula:
wherein, the left rec _ score of equal signiIs the sixth recommended value of the ith recommended item in a certain recommended set, rec _ score on the left side of equal signiIs the fifth recommendation value, rec _ score, for the ith recommended item in a recommendation set1Is the fifth recommended value with the largest value in a certain recommended set. After each recommendation set is normalized in this way, each sixth recommendation value is [0, 1 ]]Within the interval (c).
And S1103, fusing all recommendation sets into a recommendation union set, wherein when the same recommended item exists in different recommendation sets, the sixth recommendation value of the recommended item in the recommendation union set is the sum of the sixth recommendation values corresponding to the recommended item in different recommendation sets.
And S1104, determining the descending order of the sixth recommended value of each recommended item in the recommended union set.
And S1105, obtaining the category of each recommended item.
S1106, obtaining the category sequence of each recommended article according to the descending sequence of the sixth recommended value and the category to which each recommended article belongs, and determining the sequence number of each recommended article in the corresponding category sequence to which each recommended article belongs.
And S1107, performing data processing on the sixth recommended value corresponding to each recommended item according to the serial number of each recommended item in the corresponding belonged category sequence, and acquiring the third recommended value corresponding to each recommended item, so as to obtain the third recommended value sequence of each recommended item.
In this embodiment, data processing is performed on the sixth recommended value corresponding to each recommended item according to the serial number of each recommended item in the corresponding category ranking to which each recommended item belongs, so as to obtain the third recommended value corresponding to each recommended item, which can be implemented according to the following formula:
wherein, the left rec _ score of equal signiIs the third recommended value of the ith recommended item, rec _ score on the right side of the equal signiIs the sixth recommended value for the ith recommended item,
is the serial number (or
Is the number of occurrences of the ith recommended item in the sixth recommended value descending order), constcateIs a class constant that may be set to 1 or other value greater than 0.
In the item recommendation method provided in the embodiment of the application, through step 110, a plurality of recommended items and a first recommended value corresponding to each recommended item are obtained according to N target items, and a first recommended value ranking of each recommended item is obtained, where N is an integer greater than 0, and each recommended item is related to at least one target item; and step 120, obtaining the generation time of each recommended item, performing data processing on the first recommended value of each recommended item according to the current time and the generation time of each recommended item, and obtaining the second recommended value of each recommended item, so as to obtain the second recommended value sequence of each recommended item. Therefore, the recommended articles related to the target articles are subjected to timeliness processing, the recommended value of the recommended articles with good timeliness is improved, the purpose of recommending the recommended articles with good timeliness to the user is achieved, the recommended articles with good timeliness and high relevance to the interest of the user are recommended to the user, the technical effect that the user is interested in the recommended articles is improved, and the technical problems that the recommended articles recommended by the prior art are poor in timeliness and the interest of the user in the recommended articles is reduced are solved.
Based on the same technical concept as the item recommendation method, an embodiment of the present application further provides an item recommendation device, as shown in fig. 10, including:
a first obtaining module 10, configured to obtain, according to N target items, a plurality of recommended items and a first recommended value corresponding to each recommended item, where N is an integer greater than 0, and each recommended item is related to at least one target item;
the second obtaining module 20 is configured to obtain a generation time of each recommended item, perform data processing on the first recommended value of each recommended item according to the current time and the generation time of each recommended item, and obtain a second recommended value of each recommended item, so as to obtain a second recommended value ranking of each recommended item.
Optionally, the second obtaining module 20 is configured to:
acquiring the generation duration of each recommended item according to the current time and the generation time of each recommended item;
acquiring an aging coefficient of each recommended article according to the first preset time duration evaluation value and the generation time duration of each recommended article;
and calculating the first recommended value of each recommended article according to the aging coefficient of each recommended article to obtain a second recommended value of each recommended article.
Optionally, the second obtaining module 20 is configured to:
acquiring a first time length evaluation value of each recommended item according to the generation time length of each recommended item;
and according to the ratio of the first preset time length evaluation value to the first time length evaluation value of each recommended article, the aging factor of each recommended article.
Optionally, the second obtaining module 20 is configured to:
acquiring a second time length evaluation value of each recommended article according to the ratio of the generation time length of each recommended article to the unit time;
and acquiring a first time length evaluation value of each recommended article according to the sum of the second preset time length evaluation value and the second time length evaluation value of each recommended article.
Optionally, as shown in fig. 11, the article recommendation device further includes a third obtaining module;
a third obtaining module 30, configured to obtain N target items and recommendation factors corresponding to each target item according to the historical behavior data of the user;
a first obtaining module 10 configured to:
obtaining a plurality of recommended articles and a third recommended value corresponding to each recommended article according to the N target articles, and obtaining a third recommended value sequence of each recommended article;
and performing data processing on the third recommended value corresponding to each recommended article according to the third recommended value sequence and the recommendation factor corresponding to each target article to obtain a first recommended value corresponding to each recommended article.
Optionally, the first obtaining module 10 is configured to:
and determining a recommendation factor corresponding to each recommended item according to each recommended item and the related target item.
And obtaining the corresponding recommendation factor sequence of each recommended article according to the first recommendation value sequence and the recommendation factor corresponding to each recommended article, and determining the sequence number of each recommended article in the corresponding recommendation factor sequence.
And performing data processing on the third recommended value corresponding to each recommended article according to the serial number of each recommended article in the corresponding recommendation factor sequence, and acquiring the first recommended value corresponding to each recommended article.
Optionally, the first obtaining module 10 is configured to:
and performing data processing on the third recommended value corresponding to each recommended article according to the serial number of each recommended article in the corresponding recommendation factor sequence, and acquiring the fourth recommended value corresponding to each recommended article, so as to obtain the fourth recommended value sequence of each recommended article.
And in the descending sorting of the fourth recommended value of each recommended item, acquiring the similarity value of each recommended item relative to the previous recommended item, wherein the similarity value included by the recommended item ranked first is a first preset value.
And performing data processing on the fourth recommended value corresponding to each recommended article according to the similarity value included in each recommended article to obtain the first recommended value of each recommended article.
Optionally, the first obtaining module 10 is configured to:
and acquiring the dissimilarity value of each recommended item relative to the recommended item at the previous position according to the similarity value included by each recommended item.
And performing data processing on the fourth recommended value corresponding to each recommended article according to the dissimilarity value included in each recommended article to obtain the first recommended value of each recommended article.
Optionally, when the dissimilarity value of the recommended item relative to the previous recommended item is smaller than a second preset value, the second preset value is selected as the dissimilarity of the recommended item.
Optionally, the first obtaining module 10 is configured to:
and obtaining at least one recommendation set according to each target item, wherein each recommendation set comprises a plurality of recommendation items related to the target item, and each recommendation item corresponds to a fifth recommendation value.
And normalizing the fifth recommended value corresponding to the recommended article in each recommended set to obtain a sixth recommended value corresponding to each recommended article.
And fusing all recommendation sets into one recommendation union set, wherein when the same recommended item exists in different recommendation sets, the sixth recommendation value of the recommended item in the recommendation union set is the sum of the sixth recommendation values corresponding to the recommended item in different recommendation sets.
And determining the sixth recommended value descending order of each recommended item in the recommended union.
And acquiring the category of each recommended item.
And according to the descending order of the sixth recommended value and the category to which each recommended article belongs, obtaining the category order to which each recommended article belongs, and determining the sequence number of each recommended article in the corresponding category order to which each recommended article belongs.
And performing data processing on the sixth recommended value corresponding to each recommended article according to the serial number of each recommended article in the corresponding belonged category sequence, and acquiring the third recommended value corresponding to each recommended article, so as to obtain the third recommended value sequence of each recommended article.
The article recommendation device provided in the embodiment of the application, through the first obtaining module 10, is configured to obtain, according to N target articles, a plurality of recommended articles and a first recommendation value corresponding to each recommended article, and obtain a first recommendation value ranking of each recommended article, where N is an integer greater than 0, and each recommended article is related to at least one target article; the first obtaining module 10 is configured to obtain a generation time of each recommended item, perform data processing on the first recommended value of each recommended item according to the current time and the generation time of each recommended item, and obtain a second recommended value of each recommended item, so as to obtain a second recommended value ranking of each recommended item. Therefore, the recommended articles related to the target articles are subjected to timeliness processing, the recommended value of the recommended articles with good timeliness is improved, the purpose of recommending the recommended articles with good timeliness to the user is achieved, the recommended articles with good timeliness and high relevance to the interest of the user are recommended to the user, the technical effect that the user is interested in the recommended articles is improved, and the technical problems that the recommended articles recommended by the prior art are poor in timeliness and the interest of the user in the recommended articles is reduced are solved.
It will be apparent to those skilled in the art that the modules or steps of the present application described above may be implemented by a general purpose computing device, they may be centralized on a single computing device or distributed across a network of multiple computing devices, and they may alternatively be implemented by program code executable by a computing device, such that they may be stored in a storage device and executed by a computing device, or fabricated separately as individual integrated circuit modules, or fabricated as a single integrated circuit module from multiple modules or steps. Thus, the present application is not limited to any specific combination of hardware and software.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. An item recommendation method, characterized in that the method comprises:
obtaining a plurality of recommended articles and a first recommended value corresponding to each recommended article according to N target articles, wherein N is an integer greater than 0, and each recommended article is related to at least one target article;
acquiring the generation time of each recommended article, performing data processing on the first recommended value of each recommended article according to the current time and the generation time of each recommended article, and acquiring the second recommended value of each recommended article, so as to obtain the second recommended value sequence of each recommended article;
wherein, the item recommendation method further comprises:
performing data processing on the third recommended value corresponding to each recommended article according to the serial number of each recommended article in the corresponding recommendation factor sequence, and acquiring a fourth recommended value corresponding to each recommended article, so as to obtain a descending sequence of the fourth recommended value of each recommended article;
in the descending sorting of the fourth recommended value of each recommended article, the similarity value of each recommended article relative to the previous recommended article is obtained, wherein the similarity value included in the recommended article ranked at the first position is a first preset value;
and performing data processing on the fourth recommended value corresponding to each recommended article according to the similarity value included in each recommended article to obtain the first recommended value of each recommended article.
2. The item recommendation method according to claim 1, wherein the obtaining a second recommendation value of each recommended item by performing data processing on the first recommendation value of each recommended item according to the current time and the generation time of each recommended item comprises:
obtaining the generation duration of each recommended item according to the current time and the generation time of each recommended item;
acquiring an aging coefficient of each recommended article according to a first preset time duration evaluation value and the generation time duration of each recommended article;
and calculating the first recommended value of each recommended article according to the aging coefficient of each recommended article to obtain a second recommended value of each recommended article.
3. The item recommendation method according to claim 2, wherein the obtaining an aging factor of each recommended item according to the first preset duration evaluation value and the generation duration of each recommended item comprises:
acquiring a first time length evaluation value of each recommended article according to the generation time length of each recommended article;
and according to the ratio of the first preset time length evaluation value to the first time length evaluation value of each recommended article, the aging factor of each recommended article.
4. The item recommendation method according to claim 3, wherein said obtaining a first time length evaluation value of each of the recommended items according to the generation time length of each of the recommended items comprises:
acquiring a second time length evaluation value of each recommended article according to the ratio of the generation time length of each recommended article to unit time;
and acquiring a first time length evaluation value of each recommended article according to the sum of a second preset time length evaluation value and a second time length evaluation value of each recommended article.
5. The item recommendation method according to claim 1, further comprising:
acquiring N target articles and recommendation factors corresponding to each target article according to the historical behavior data of the user;
the obtaining a plurality of recommended articles and a first recommended value corresponding to each recommended article according to the N target articles includes:
obtaining a plurality of recommended articles and a third recommended value corresponding to each recommended article according to the N target articles, and obtaining a third recommended value sequence of each recommended article;
and performing data processing on the third recommended value corresponding to each recommended article according to the third recommended value sequence and the recommendation factor corresponding to each target article to obtain a first recommended value corresponding to each recommended article.
6. An item recommendation device, the device comprising:
the device comprises a first obtaining module, a second obtaining module and a third obtaining module, wherein the first obtaining module is used for obtaining a plurality of recommended articles and a first recommended value corresponding to each recommended article according to N target articles, N is an integer larger than 0, and each recommended article is related to at least one target article;
the second obtaining module is used for obtaining the generation time of each recommended article, performing data processing on the first recommended value of each recommended article according to the current time and the generation time of each recommended article, and obtaining a second recommended value of each recommended article so as to obtain a second recommended value sequence of each recommended article;
wherein, the first obtaining module is configured to:
performing data processing on the third recommended value corresponding to each recommended article according to the serial number of each recommended article in the corresponding recommendation factor sequence, and acquiring a fourth recommended value corresponding to each recommended article, so as to obtain a descending sequence of the fourth recommended value of each recommended article;
in the descending sorting of the fourth recommended value of each recommended article, the similarity value of each recommended article relative to the previous recommended article is obtained, wherein the similarity value included in the recommended article ranked at the first position is a first preset value;
and performing data processing on the fourth recommended value corresponding to each recommended article according to the similarity value included in each recommended article to obtain the first recommended value of each recommended article.
7. The item recommendation device of claim 6, wherein the second obtaining module is configured to:
obtaining the generation duration of each recommended item according to the current time and the generation time of each recommended item;
acquiring an aging coefficient of each recommended article according to a first preset time duration evaluation value and the generation time duration of each recommended article;
and calculating the first recommended value of each recommended article according to the aging coefficient of each recommended article to obtain a second recommended value of each recommended article.
8. The item recommendation device of claim 7, wherein the second obtaining module is configured to:
acquiring a first time length evaluation value of each recommended article according to the generation time length of each recommended article;
and according to the ratio of the first preset time length evaluation value to the first time length evaluation value of each recommended article, the aging factor of each recommended article.
9. The item recommendation device of claim 8, wherein the second obtaining module is configured to:
acquiring a second time length evaluation value of each recommended article according to the ratio of the generation time length of each recommended article to unit time;
and acquiring a first time length evaluation value of each recommended article according to the sum of a second preset time length evaluation value and a second time length evaluation value of each recommended article.
10. The item recommendation device of claim 6, further comprising a third acquisition module;
the third acquisition module is used for acquiring N target articles and recommendation factors corresponding to each target article according to the historical behavior data of the user;
the first obtaining module is configured to:
obtaining a plurality of recommended articles and a third recommended value corresponding to each recommended article according to the N target articles, and obtaining a third recommended value sequence of each recommended article;
and performing data processing on the third recommended value corresponding to each recommended article according to the third recommended value sequence and the recommendation factor corresponding to each target article to obtain a first recommended value corresponding to each recommended article.
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