CN111695040A - Fashion product recommendation method, system and device based on emotion label - Google Patents

Fashion product recommendation method, system and device based on emotion label Download PDF

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CN111695040A
CN111695040A CN202010537197.8A CN202010537197A CN111695040A CN 111695040 A CN111695040 A CN 111695040A CN 202010537197 A CN202010537197 A CN 202010537197A CN 111695040 A CN111695040 A CN 111695040A
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fashion
emotion
user
score
recommendation
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CN111695040B (en
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黄昭
范理涛
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Shaanxi Normal University
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Abstract

The invention discloses a fashion product recommendation method, a fashion product recommendation system and a fashion product recommendation device based on emotion labels. Acquiring scoring information and emotion label information of a user; establishing a user emotion dictionary according to the emotion label information of the user; acquiring fashion information, and calculating a popularity score according to the life cycle of the fashion; recommending fashion products according to the fashion product recommendation scores of similar users; according to the method, the objective feedback of the user on the rating of the fashion is considered, the feedback of the user is supplemented through the emotional tag of the fashion, the preference of the user can be mastered more accurately, and the recommendation performance is improved; by predicting the life cycle of the fashion, the fashion which meets the preference of the user and the current fashion style can be recommended to the user according to the change of the fashion along with the time fashion.

Description

Fashion product recommendation method, system and device based on emotion label
Technical Field
The invention relates to the field of recommendation systems in computer technology, in particular to a fashion recommendation method, system and device based on emotion labels.
Background
In the process of fashion recommendation, the most important consideration is how to recommend fashion which meets the current fashion style and the preference of the user. Fashion items have different life cycles, and once the fashion item is marketed, the popularity changes over time, and after the life cycle of the fashion item, the fashion item is eventually replaced with a new fashion item. The emotion label added to the fashion product reflects the emotional preference of the user on the fashion product to a certain extent. Therefore, the fashion recommendation method based on the emotion labels is used for recommending fashion products which accord with the current fashion style and meet the self preference for users according to the popularity of the fashion products and the score of the total weight of the fashion products.
Disclosure of Invention
In order to solve the problems, the invention provides a fashion product recommendation method, a fashion product recommendation system and a fashion product recommendation device based on emotion labels.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows: a fashion recommendation method based on emotion labels comprises the following steps:
s1, acquiring the grading information of the fashion and the emotion score of the added label for the fashion purchased by the user; acquiring the emotion score of the added label for the fashion browsed by the user;
s2, establishing an emotion dictionary of the user;
s3, obtaining information of the fashion, wherein the information of the fashion at least comprises a predicted life cycle of the fashion, and calculating the popularity score of the fashion according to the life cycle of the fashion;
s4, calculating and sequencing the total weight scores of the fashion according to the grading information of the user to the fashion and the emotion scores of the labels at S1 to obtain a sequence list of the total weight scores of the fashion; the fashion total weight score calculation process is as follows:
calculating a fashion weight score based on the scoring information;
calculating a fashion weight score based on emotion;
a total fashion weight score ═ (1- α) — total fashion weight score based on the scoring information + α — + an emotional fashion weight score;
s5, based on the total weight score of fashion of each user obtained in S4Calculating the average total weight score mu of each user by using the total weight score of the labels of each user uuThen, calculating a Pearson correlation coefficient by utilizing a similarity principle to obtain a ranking list of similar users v of the user u;
s6, calculating and sequencing the recommendation scores of the fashion according to the weight score result of the fashion of the user obtained in the S4 and the popularity score of the fashion obtained in the S3 to obtain a recommendation score sequencing list;
fashion recommendation score (popularity score) fashion total weight score
And recommending the fashion to the similar users from the fashion recommendation score list according to the similar user ranking list obtained in the step 5.
In S2, the emotion dictionary of the user records the emotion tags and emotion scores added by the user, and the user adds and deletes the emotion tags and modifies the emotion scores at any time.
At S3, the information of the fashion includes an issue date, a brand, and a price.
In S3, fashion popularity score:
Figure BDA0002537414530000021
wherein: t is the predicted life cycle of the fashion product, T is the number of days after the fashion product is issued, the fashion product popularity score is lower along with the increase of the number of issued days, and FScoreThe minimum value is 0, and the popularity score of the fashion product of the classic style is always 1.
In S4, the fashion weight score based on the scoring information is calculated:
Figure BDA0002537414530000031
wherein r isu,iIs the value of the user's score for fashion, ru,i(h) Vectorizing the score of each user for the normalized score value of the label h added by the user for the fashion item i, and then normalizing the score to be a unit vector:
Figure BDA0002537414530000032
i is the total number of all fashion items scored by the user.
In S4, the emotion-based fashion weight score is calculated as follows:
(1) deleting the special characters contained in the label;
(2) removing proper nouns in the tag;
(3) calculating the emotion scores of the tags, calculating the emotion scores of the tags by using an emotion dictionary of the user, and if the tags exist in the emotion dictionary of the user, using the scores in the emotion dictionary as the emotion scores of the tags:
we(hu,i)＝EmotionScore(hu,i)
wherein EmotionScore (h)u,i) Is label h in emotion dictionaryu,iIf the label is contained in the emotion dictionary, the emotion score of the label is the emotion score in the emotion dictionary;
if a plurality of tags exist in the emotion dictionary of the user at the same time, calculating emotion scores according to the emotion values of the tags:
Figure BDA0002537414530000033
where set is a set composed of multiple tags, | set | is the number of all tags in the set, | set |, which is a set composed of multiple tagsemotionAnd | is the number of emotion labels in the set.
In S5, the average total weight score μ of each user is calculated using the total weight score of the label of each user uu：
Figure BDA0002537414530000041
IuA set of numbers representing fashion items that have been evaluated by user u; pearson correlation coefficient may be used to measure similarity Sim (u, v) of scoring vectors between user u and user v, user (line)The Pearson correlation coefficient between u and v is defined as follows:
Figure BDA0002537414530000042
wherein Iu∩IvIs a set of label total weight scores known to both user u and user v.
A fashion recommendation system comprising: the information acquisition module is used for acquiring the grading information of the fashion product and the emotion score of the added label from the user, acquiring an emotion dictionary of the user and acquiring the information of the fashion product;
the data processing module is used for calculating and obtaining the following information based on the grading information of the fashion product and the emotion score of the added label of the user, the emotion dictionary of the user and the information of the fashion product: the method comprises the following steps of (1) obtaining a fashion weight score based on scoring information, a fashion weight score based on emotion, a total fashion weight score, a fashion popularity score, a fashion recommendation score and a fashion recommendation score list; calculating to obtain similar users by utilizing a similarity principle based on the total weight score of the fashion products by the users;
and the recommending module is used for recommending the fashion products to the similar users based on the fashion product recommending scores, the fashion product recommending score list and the similar users.
A fashion recommendation device comprises a processor, a memory, an information acquisition device and a recommendation result output device, wherein the processor is connected with the memory through an I/O interface, the information acquisition device is connected with the input end of the processor, and the recommendation result output device is connected with the output end of the processor; the memory stores an executable computer program, and the processor can execute the fashion recommendation method in the invention when executing the executable computer program and display the recommendation result through the recommendation result output device.
Compared with the prior art, the invention has at least the following beneficial effects: according to the method, the objective feedback of the user on the rating of the fashion is considered, the feedback of the user is supplemented through the emotional tag of the fashion, the preference of the user can be mastered more accurately, and the recommendation performance is improved; by predicting the life cycle of the fashion, the fashion which accords with the preference of the user and the current popular style can be recommended to the user according to the change of the fashion along with the time popularity; the emotion dictionary can be used for conveniently managing and maintaining the emotion labels added by the user, and the fashion to be recommended can be quickly adjusted according to the emotion preference change of the user.
Drawings
Fig. 1 is a flow chart of fashion recommendation based on emotion labels.
Detailed Description
The following describes an embodiment of the present invention with reference to fig. 1.
The fashion recommendation method based on the emotion label comprises the following specific steps:
step 1, acquiring grading information of a fashion product purchased by a user and emotion scores of added labels of the fashion product; acquiring the emotion score of the added label for the fashion browsed by the user; the range of the emotion score is-1 to 1, the negative emotion score represents that the user is a negative emotion, and the closer to-1, the deeper the negative emotion degree is; a positive emotion value indicates that the user has positive emotion, and the closer to 1, the more positive emotion is.
Step 2, establishing an emotion dictionary of the user; and recording the emotion labels and the emotion scores added by the user, and adding and deleting the emotion labels and modifying the emotion scores at any time.
Step 3, obtaining information of the fashion, wherein the information of the fashion comprises a predicted life cycle, a brand and a price of the fashion; if the monthly fashion is released, the life cycle of the fashion is predicted to be 30 days; if a quarterly fashion new product is issued, the life cycle of the fashion product is predicted to be 90 days; if an annual fashion novelty is released, the life cycle of the fashion is predicted to be 365 days.
According to the life cycle of the fashion, calculating to obtain the popularity score of the fashion:
Figure BDA0002537414530000061
wherein: t is the predicted life cycle of the fashion, and T is the number of days after the fashion is released. Fashion products have a lower and lower prevalence score as the number of days of release increases, FScoreThe minimum value is 0; for classic fashion, the popularity score is always 1.
Step 4, calculating the total weight score of the fashion products and sequencing the total weight score according to the grading information of the user on the fashion products and the emotion scores of the added labels in the step 1;
the fashionable product total weight score calculation steps and the method are as follows:
1. calculating the fashion weight score based on the scoring information:
Figure BDA0002537414530000062
wherein r isu,iIs the value of the user's credit for fashion. r isu,i(h) The normalized score value of label h added by the user for fashion item i. If raw scores are used as label weights, deviations may occur because the range of scores given to fashion items varies from user to user. The scores for each user are vectorized and then normalized to a unit vector:
Figure BDA0002537414530000063
2. emotion-based fashion weight score calculation:
the fashion weight score based on the emotion is embodied by the emotion value of the label, and in order to obtain the emotion score, the following steps are carried out on each label:
(1) deleting the special characters contained in the label;
(2) remove the proper noun in the tag. Proper nouns cannot accurately reflect emotions, so the proper nouns are removed when the emotion scores are calculated;
(3) the sentiment score of the tag is calculated. Calculating the emotion scores of the tags by using the emotion dictionary of the user, and if the tags exist in the emotion dictionary of the user, using the scores in the emotion dictionary as the emotion scores of the tags:
we(hu,i)＝EmotionScore(hu,i)
wherein EmotionScore (h)u,i) Is label h in emotion dictionaryu,iIf the label is contained in the emotion dictionary, the emotion score of the label is the emotion score in the emotion dictionary.
If a plurality of tags exist in the emotion dictionary of the user at the same time, calculating emotion scores according to the emotion values of the tags:
Figure BDA0002537414530000071
where set is a set composed of multiple tags, | set | is the number of all tags in the set, | set |, which is a set composed of multiple tagsemotionAnd | is the number of emotion labels in the set.
If the label does not exist in the emotion dictionary, the label has an emotion value of 0, and the user can add the label to the emotion dictionary and set an emotion score for the computation of the emotion value of the label later.
And (3) calculating the total weight fraction of the fashion:
weight(hu,i)＝(1-α)*w(hu,i)+α*we(hu,i)
where α is a parameter controlling the influence of the emotion label, the total weight is calculated using only the score-based fashion weight if the label has no emotion value, and the total weight is calculated using only the emotion-based fashion weight if the fashion has no score value.
Step 5, constructing a label total weight score matrix R of m × n of the user and the fashion based on the fashion total weight scores obtained in the step 4, wherein the label total weight score matrix R is [ w ═ wuj]，wujThe total weight score of the fashion item of the user u to the fashion item j is calculated in the step 4;
calculating the average total weight score mu of each user by using the total weight score of the labels of each user uu：
Figure BDA0002537414530000081
IuA set of serial numbers representing fashion items that have been evaluated by the user (row) u; pearson correlation coefficients may be used to measure the similarity Sim (u, v) of the scoring vector between user u and user v, and are defined as follows:
Figure BDA0002537414530000082
wherein Iu∩IvIs a label total weight score set known by both user u and user v;
step 6, calculating the fashion scores of the fashion products corresponding to the current day, then calculating to obtain the fashion product recommendation scores, sorting according to the size of the fashion product recommendation scores to obtain a final fashion product recommendation score list, and recommending the fashion products to other similar users from the fashion product recommendation score list according to the ranking list of the fashion product total weight scores of the similar users and the similar users obtained in the step 5;
the fashion recommendation score calculation method comprises the following steps:
RecScore＝FScore*weight(hu,i)
the present invention also provides a fashion recommendation system, comprising: the information acquisition module is used for acquiring the grading information of the fashion product and the emotion score of the added label from the user, acquiring an emotion dictionary of the user and acquiring the information of the fashion product;
the data processing module is used for calculating and obtaining the following information based on the grading information of the fashion product and the emotion score of the added label of the user, the emotion dictionary of the user and the information of the fashion product: the method comprises the following steps of (1) obtaining a fashion weight score based on scoring information, a fashion weight score based on emotion, a total fashion weight score, a fashion popularity score, a fashion recommendation score and a fashion recommendation score list; calculating to obtain similar users by utilizing a similarity principle based on the total weight score of the fashion products by the users;
and the recommending module is used for recommending the fashion products to the similar users based on the fashion product recommending scores, the fashion product recommending score list and the similar users.
A fashion recommendation device comprises a processor, a memory, an information acquisition device and a recommendation result output device, wherein the processor is connected with the memory through an I/O interface, the information acquisition device is connected with the input end of the processor, and the recommendation result output device is connected with the output end of the processor; the memory stores an executable computer program that, when executed, enables the processor to perform the fashion recommendation method of any one of claims 1-7 and present the recommendation via a recommendation output device.
As a preferred embodiment, the information acquisition device and the recommendation result output device both adopt touch displays, and the touch displays are connected with the processor through an I/O interface.
When the fashion recommendation device executes the step 1 through executing the computer program, the fashion purchased by the user is acquired through the information acquisition device, and the grading information of the fashion and the emotion score of the added label of the user are acquired; acquiring the emotion score of the added label for the fashion browsed by the user; the range of the emotion score is-1 to 1, the negative emotion score represents that the user is a negative emotion, and the closer to-1, the deeper the negative emotion degree is; if the emotion value is positive, the user is positive emotion, and the closer to 1, the deeper the positive emotion degree is; and is stored in a memory in such a manner that,
when the fashion recommendation device executes the step 2 by executing the computer program, the information acquisition device records the emotion labels and the emotion scores added by the user, can add and delete the emotion labels at any time and modify the emotion scores, and stores the information to the memory through the processor;
the memory is also stored with an instruction set for automatically acquiring information, the processor is connected with the network transmitter, the processor can execute the instruction set for automatically acquiring information to acquire the information of the step 1 and the step 2 of the invention from the network, and when the fashion recommendation device executes the step 1 and the step 2 by executing the computer program, the fashion recommendation device can be acquired by executing the instruction set for acquiring information by the processor or can be used for inputting information manually by a user.
When the fashion recommendation device executes the step 3 through executing the computer program, the fashion recommendation device acquires information of the fashion, and stores the information into a memory through a processor; meanwhile, the processor calculates the popularity score of the fashion product according to the life cycle of the fashion piece, and sends the popularity score to the memory;
when the fashion recommendation device executes the step 4 through executing the computer program, the processor calculates the fashion weight score, the fashion weight score and the fashion total weight score based on the grading information, and sends the fashion weight score, the fashion weight score and the fashion total weight score to the memory;
when the fashion recommendation device executes the step 5 by executing a computer program, based on the fashion total weight score of each user obtained in the step 4, calculating the average total weight score of each user by using the label total weight score of each user, calculating a correlation coefficient by using a similarity principle to obtain a similar user list, and sending the obtained result to a memory for storage;
when the fashion recommendation device executes the step 6 by executing the computer program, the processor reads the fashion score of the fashion obtained by executing the step 3 and the weight score result of the fashion of the user obtained by the step 4 from the memory, and calculates the recommendation score of the fashion according to the recommendation score of the fashion as the fashion score and the total weight score of the fashion and sorts the recommendation scores; simultaneously reading the data of the similar users obtained in the step 5, and recommending fashion products to the similar users from the fashion product recommendation score list; and displaying the recommendation result through a recommendation result output device.

Claims (9)

1. A fashion recommendation method based on emotion labels is characterized by comprising the following steps:
s1, acquiring the grading information of the fashion and the emotion score of the added label for the fashion purchased by the user; acquiring the emotion score of the added label for the fashion browsed by the user;
s2, establishing an emotion dictionary of the user;
s3, obtaining information of the fashion, wherein the information of the fashion at least comprises a predicted life cycle of the fashion, and calculating the popularity score of the fashion according to the life cycle of the fashion;
s4, calculating and sequencing the total weight scores of the fashion according to the grading information of the user to the fashion and the emotion scores of the labels at S1 to obtain a sequence list of the total weight scores of the fashion; the fashion total weight score calculation process is as follows:
calculating a fashion weight score based on the scoring information;
calculating a fashion weight score based on emotion;
a total fashion weight score ═ (1- α) — total fashion weight score based on the scoring information + α — + an emotional fashion weight score;
s5, based on the fashion total weight score of each user obtained in S4, the average total weight score mu of each user is calculated by using the label total weight score of each user uuThen, calculating a Pearson correlation coefficient by utilizing a similarity principle to obtain a ranking list of similar users v of the user u;
s6, calculating and sequencing the recommendation scores of the fashion according to the weight score result of the fashion of the user obtained in the S4 and the popularity score of the fashion obtained in the S3 to obtain a recommendation score sequencing list;
fashion recommendation score (popularity score) fashion total weight score
And recommending the fashion to the similar users from the fashion recommendation score list according to the similar user ranking list obtained in the step 5.
2. The method for recommending fashion goods based on emotion label as recited in claim 1, wherein in S2, the emotion dictionary of the user records emotion labels added by the user and emotion scores, and the emotion labels are added and deleted by the user at any time, and the emotion scores are modified.
3. The emotion tag-based fashion recommendation method of claim 1, wherein in S3, the fashion information further includes a release date, a brand and a price.
4. The emotion tag-based fashion recommendation method of claim 1, wherein in S3, the fashion popularity score:
Figure FDA0002537414520000021
wherein: t is the predicted life cycle of the fashion product, T is the number of days after the fashion product is issued, the fashion product popularity score is lower along with the increase of the number of issued days, and FScoreThe minimum value is 0, and the popularity score of the fashion product of the classic style is always 1.
5. The emotion tag-based fashion recommendation method of claim 1, wherein in S4, the fashion weight score based on the scoring information is calculated by:
Figure FDA0002537414520000022
wherein r isu,iIs the value of the user's score for fashion, ru,i(h) Vectorizing the score of each user for the normalized score value of the label h added by the user for the fashion item i, and then normalizing the score to be a unit vector:
Figure FDA0002537414520000023
i is the total number of all fashion items scored by the user.
6. The emotion tag-based fashion recommendation method of claim 1, wherein the emotion-based fashion weight score is calculated as follows in S4:
(1) deleting the special characters contained in the label;
(2) removing proper nouns in the tag;
(3) calculating the emotion scores of the tags, calculating the emotion scores of the tags by using an emotion dictionary of the user, and if the tags exist in the emotion dictionary of the user, using the scores in the emotion dictionary as the emotion scores of the tags:
we(hu,i)＝EmotionScore(hu,i)
wherein EmotionScore (h)u,i) Is label h in emotion dictionaryu,iIf the label is contained in the emotion dictionary, the emotion score of the label is the emotion score in the emotion dictionary;
if a plurality of tags exist in the emotion dictionary of the user at the same time, calculating emotion scores according to the emotion values of the tags:
Figure FDA0002537414520000031
where set is a set composed of multiple tags, | set | is the number of all tags in the set, | set |, which is a set composed of multiple tagsemotionAnd | is the number of emotion labels in the set.
7. The method of claim 1, wherein in step S5, the average total weight score μ for each user is calculated using the total weight score of the tags for each user uu：
Figure FDA0002537414520000032
IuA set of numbers representing fashion items that have been evaluated by user u; pearson correlation coefficients may be used to measure the similarity Sim (u, v) of the scoring vector between user u and user v, and are defined as follows:
Figure FDA0002537414520000033
wherein Iu∩IvIs a set of label total weight scores known to both user u and user v.
8. A fashion recommendation system, comprising: the information acquisition module is used for acquiring the grading information of the fashion product and the emotion score of the added label from the user, acquiring an emotion dictionary of the user and acquiring the information of the fashion product;
the data processing module is used for calculating and obtaining the following information based on the grading information of the fashion product and the emotion score of the added label of the user, the emotion dictionary of the user and the information of the fashion product: the method comprises the following steps of (1) obtaining a fashion weight score based on scoring information, a fashion weight score based on emotion, a total fashion weight score, a fashion popularity score, a fashion recommendation score and a fashion recommendation score list; calculating to obtain similar users by utilizing a similarity principle based on the total weight score of the fashion products by the users;
and the recommending module is used for recommending the fashion products to the similar users based on the fashion product recommending scores, the fashion product recommending score list and the similar users.
9. The fashion recommendation device is characterized by comprising a processor, a memory, an information acquisition device and a recommendation result output device, wherein the processor is connected with the memory through an I/O interface; the memory stores an executable computer program that, when executed, enables the processor to perform the fashion recommendation method of any one of claims 1-7 and present the recommendation via a recommendation output device.
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