CN111652416A - Agricultural product user score prediction method - Google Patents
Agricultural product user score prediction method Download PDFInfo
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- CN111652416A CN111652416A CN202010449450.4A CN202010449450A CN111652416A CN 111652416 A CN111652416 A CN 111652416A CN 202010449450 A CN202010449450 A CN 202010449450A CN 111652416 A CN111652416 A CN 111652416A
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- 230000004927 fusion Effects 0.000 claims abstract description 22
- 239000011159 matrix material Substances 0.000 claims abstract description 16
- 239000013598 vector Substances 0.000 claims description 60
- 230000002452 interceptive effect Effects 0.000 claims description 6
- 238000004458 analytical method Methods 0.000 claims description 3
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- 238000013528 artificial neural network Methods 0.000 abstract description 2
- 238000004364 calculation method Methods 0.000 abstract description 2
- 238000005516 engineering process Methods 0.000 abstract description 2
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A40/00—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production
- Y02A40/10—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production in agriculture
Abstract
The invention discloses a prediction method for agricultural product user score, which relates to the technical field of agricultural products, in particular to a prediction method for agricultural product user score, and comprises S1, a target user online search module and S2, a module for commenting agricultural products by users; s3, a user scores the agricultural products and integrates the module, and the user scores the agricultural products and acquires the unit, the agricultural product scoring matrix forming unit and the scoring information collecting and sorting unit; s4, fusing a layer module; and S5, a user prediction scoring module is used for sorting the information obtained in the fusion layer module. The method and the device effectively solve the problem of recommendation accuracy caused by check-in data sparsity, and can well predict the user score of the agricultural product, so that the agricultural product can be accurately recommended to the user, the text content of the comment is mined and searched by combining a neural network by utilizing big data calculation, and the agricultural product recommendation technology has great advantages.
Description
Technical Field
The invention relates to the technical field of agricultural products, in particular to a user rating prediction method for agricultural products.
Background
Along with the steady increase of the total value of agricultural production, the operation level of Chinese agricultural production is steadily increased, the living standard of farmers is steadily improved, the information era is at present, but the informatization popularization in agricultural product users is small, no specific theoretical method is provided for helping agricultural product users to provide accurate information, in the current method, comment texts related to interest points are not fully researched due to technical reasons, agricultural products cannot be accurately recommended to the users, the user scores of the agricultural products cannot be predicted, and aiming at the problems, a method for predicting the user scores of the agricultural products is provided.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a method for predicting the user rating of an agricultural product, which solves the problems that comment texts related to interest points are not fully researched due to technical reasons, the agricultural product cannot be accurately recommended to users, and the user rating of the agricultural product cannot be predicted in the current method provided in the background art.
In order to achieve the purpose, the invention is realized by the following technical scheme: a method for predicting a rating of a user of an agricultural product, comprising:
s1, a target user online searching module comprises a target user information acquiring unit and a target user information sorting unit;
s2, a user comment on agricultural product module comprises a user comment text acquisition unit, an agricultural product comment text acquisition unit and a text data theme collection and arrangement unit;
s3, a user scores the agricultural products and integrates the module, and the user scores the agricultural products and acquires the unit, the agricultural product scoring matrix forming unit and the scoring information collecting and sorting unit;
s4, fusing a layer module;
and S5, a user prediction scoring module is used for sorting the information obtained in the fusion layer module.
Preferably, in step S1:
the target user information acquisition unit is used for acquiring agricultural product data information searched by a target user and acquiring basic information of the user;
the target user information sorting unit is used for sorting and storing the information of the target user.
Preferably, in step S2:
the user comment text acquisition unit is used for acquiring text data of user comments and topics in the multi-text data for extraction and analysis;
the agricultural product comment text acquisition unit is used for acquiring agricultural product comment text data and extracting and analyzing topics in the text data;
and the text data theme collection integral unit is used for collecting and sorting the themes extracted from the user comment text acquisition unit and the agricultural product comment text acquisition unit.
Preferably, in step S3:
the user grade obtaining unit is used for obtaining the grade data of the user on the agricultural products; the agricultural product scoring matrix forming unit is used for forming a scoring matrix from the scoring data of the agricultural products by the user;
the grading information collecting and sorting unit is used for collecting and sorting grading data of the agricultural products by the user.
Preferably, the fusion layer module comprises a search data vector unit, a user comment vector unit, an agricultural product comment vector unit, an implicit vector unit and a full connection layer unit.
Preferably, the search data vector unit in the fusion layer module is configured to obtain historical search data of the user, and form a search data vector for the agricultural product according to the number of times the user searches the agricultural product.
Preferably, the user comment vector unit in the fusion layer module is used for acquiring user comment text data, extracting according to the 'theme' preference of the agricultural product, and forming a comment vector of the user according to a corresponding proportion.
Preferably, the agricultural product comment vector unit in the fusion layer module is used for acquiring the agricultural product comment paper data of the user, extracting according to the 'theme' preference of the agricultural product, and forming a comment vector of the agricultural product according to the corresponding proportion.
Preferably, the hidden vector unit in the fusion layer module is used for acquiring rating data of the user on the agricultural product to form a corresponding rating matrix, and decomposing the rating matrix to obtain a hidden vector U0And V0。
Preferably, the full connection layer unit in the fusion layer module is used for searching a data vector, a comment vector of a user, and U0Operation of additionTo obtain U1；Search data vector, comment vector of user, U0Performing interactive operation to obtain U2(ii) a Search data vector, agricultural product comment vector, V0Performing an addition operation to obtain V1(ii) a Search data vector, comment vector of user, V0Performing interactive operation to obtain V2(ii) a Will U1And U2Carrying out splicing operation to obtain U; will V1And V2Performing splicing operation to obtain V; and carrying out linear combination on the U and the V through a full connection layer to obtain a final prediction score.
The invention provides a method for predicting user scores of agricultural products, which has the following beneficial effects: the method and the device effectively solve the problem of recommendation accuracy caused by check-in data sparsity, and can well predict the user score of the agricultural product, so that the agricultural product can be accurately recommended to the user, the text content of the comment is mined and searched by combining a neural network by utilizing big data calculation, and the agricultural product recommendation technology has great advantages.
Drawings
FIG. 1 is a schematic flow chart of the system of the present invention;
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
In the description of the present invention, "a plurality" means two or more unless otherwise specified; the terms "upper", "lower", "left", "right", "inner", "outer", "front", "rear", "head", "tail", and the like, indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, are only for convenience in describing and simplifying the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed in a particular orientation, and be operated, and thus, should not be construed as limiting the invention. Furthermore, the terms "first," "second," "third," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it is to be noted that, unless otherwise explicitly specified or limited, the terms "connected" and "connected" are to be interpreted broadly, e.g., as being fixed or detachable or integrally connected; can be mechanically or electrically connected; may be directly connected or indirectly connected through an intermediate. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Referring to fig. 1, the present invention provides a technical solution: a method for predicting a rating of a user of an agricultural product, comprising:
s1, a target user online searching module comprises a target user information acquiring unit and a target user information sorting unit;
s2, a user comment on agricultural product module comprises a user comment text acquisition unit, an agricultural product comment text acquisition unit and a text data theme collection and arrangement unit;
s3, a user scores the agricultural products and integrates the module, and the user scores the agricultural products and acquires the unit, the agricultural product scoring matrix forming unit and the scoring information collecting and sorting unit;
s4, fusing a layer module;
and S5, a user prediction scoring module is used for sorting the information obtained in the fusion layer module.
In step S1:
the target user information acquisition unit is used for acquiring agricultural product data information searched by a target user and acquiring basic information of the user;
the target user information sorting unit is used for sorting and storing the information of the target user.
In step S2:
the user comment text acquisition unit is used for acquiring text data of user comments and topics in the multi-text data for extraction and analysis;
the agricultural product comment text acquisition unit is used for acquiring agricultural product comment text data and extracting and analyzing topics in the text data;
and the text data theme collection integral unit is used for collecting and sorting the themes extracted from the user comment text acquisition unit and the agricultural product comment text acquisition unit.
In step S3:
the user grade obtaining unit is used for obtaining the grade data of the user on the agricultural products; the agricultural product scoring matrix forming unit is used for forming a scoring matrix from the scoring data of the agricultural products by the user;
the grading information collecting and sorting unit is used for collecting and sorting grading data of the agricultural products by the user.
The fusion layer module comprises a search data vector unit, a user comment vector unit, an agricultural product comment vector unit, an implicit vector unit and a full connection layer unit.
And the search data vector unit in the fusion layer module is used for acquiring historical search data of a user and forming a search data vector for agricultural products according to the number of times of searching the agricultural products by the user.
And the user comment vector unit in the fusion layer module is used for acquiring the user comment text data, extracting according to the 'theme' preference of the agricultural product, and forming the comment vector of the user according to the corresponding proportion.
And the agricultural product comment vector unit in the fusion layer module is used for acquiring the agricultural product comment paper data of the user, extracting according to the 'theme' preference of the agricultural product, and forming a comment vector of the agricultural product according to a corresponding proportion.
The hidden vector unit in the fusion layer module is used for acquiring the rating data of the user on the agricultural products to form a corresponding rating matrix, and decomposing the rating matrix to obtain a hidden vector U0And V0。
The full connection layer unit in the fusion layer module is used for searching data vectors, comment vectors of users and U0Performing addition operation to obtain U1；Search data vector, comment vector of user, U0Performing interactive operation to obtain U2(ii) a Will be provided withSearch data vector, agricultural product review vector, V0Performing an addition operation to obtain V1(ii) a Search data vector, comment vector of user, V0Performing interactive operation to obtain V2(ii) a Will U1And U2Carrying out splicing operation to obtain U; will V1And V2Performing splicing operation to obtain V; and carrying out linear combination on the U and the V through a full connection layer to obtain a final prediction score.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be able to cover the technical scope of the present invention and the equivalent alternatives or modifications according to the technical solution and the inventive concept of the present invention within the technical scope of the present invention.
Claims (10)
1. A method for predicting a rating of a user of an agricultural product, comprising:
s1, a target user online searching module comprises a target user information acquiring unit and a target user information sorting unit;
s2, a user comment on agricultural product module comprises a user comment text acquisition unit, an agricultural product comment text acquisition unit and a text data theme collection and arrangement unit;
s3, a user scores the agricultural products and integrates the module, and the user scores the agricultural products and acquires the unit, the agricultural product scoring matrix forming unit and the scoring information collecting and sorting unit;
s4, fusing a layer module;
and S5, a user prediction scoring module is used for sorting the information obtained in the fusion layer module.
2. The agricultural product user score prediction method of claim 1, wherein: in the step S1:
the target user information acquisition unit is used for acquiring agricultural product data information searched by a target user and acquiring basic information of the user;
the target user information sorting unit is used for sorting and storing the information of the target user.
3. The agricultural product user score prediction method of claim 1, wherein: in the step S2:
the user comment text acquisition unit is used for acquiring text data of user comments and topics in the multi-text data for extraction and analysis;
the agricultural product comment text acquisition unit is used for acquiring agricultural product comment text data and extracting and analyzing topics in the text data;
and the text data theme collection integral unit is used for collecting and sorting the themes extracted from the user comment text acquisition unit and the agricultural product comment text acquisition unit.
4. The agricultural product user score prediction method of claim 1, wherein: in the step S3:
the user grade obtaining unit is used for obtaining the grade data of the user on the agricultural products; the agricultural product scoring matrix forming unit is used for forming a scoring matrix from the scoring data of the agricultural products by the user;
the grading information collecting and sorting unit is used for collecting and sorting grading data of the agricultural products by the user.
5. The agricultural product user score prediction method of claim 1, wherein: the fusion layer module comprises a search data vector unit, a user comment vector unit, an agricultural product comment vector unit, an implicit vector unit and a full connection layer unit.
6. The agricultural product user score prediction method of claim 5, wherein: and the search data vector unit in the fusion layer module is used for acquiring historical search data of a user and forming a search data vector for agricultural products according to the number of times of searching the agricultural products by the user.
7. The agricultural product user score prediction method of claim 5, wherein: and the user comment vector unit in the fusion layer module is used for acquiring the user comment text data, extracting according to the 'theme' preference of the agricultural product, and forming the comment vector of the user according to the corresponding proportion.
8. The agricultural product user score prediction method of claim 5, wherein: and the agricultural product comment vector unit in the fusion layer module is used for acquiring the agricultural product comment paper data of the user, extracting according to the 'theme' preference of the agricultural product, and forming a comment vector of the agricultural product according to a corresponding proportion.
9. The agricultural product user score prediction method of claim 5, wherein: the hidden vector unit in the fusion layer module is used for acquiring the rating data of the user on the agricultural products to form a corresponding rating matrix, and decomposing the rating matrix to obtain a hidden vector U0And V0.。
10. The agricultural product user score prediction method of claim 5, wherein: the full connection layer unit in the fusion layer module is used for searching data vectors, comment vectors of users and U0Performing addition operation to obtain U1；Search data vector, comment vector of user, U0Performing interactive operation to obtain U2(ii) a Search data vector, agricultural product comment vector, V0Performing an addition operation to obtain V1(ii) a Search data vector, comment vector of user, V0Performing interactive operation to obtain V2(ii) a Will U1And U2Carrying out splicing operation to obtain U; will V1And V2Performing splicing operation to obtain V; and carrying out linear combination on the U and the V through a full connection layer to obtain a final prediction score.
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Citations (5)
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|---|---|---|---|---|
| US20150248720A1 (en) * | 2014-03-03 | 2015-09-03 | Invent.ly LLC | Recommendation engine |
| CN105550211A (en) * | 2015-12-03 | 2016-05-04 | 云南大学 | Social network and item content integrated collaborative recommendation system |
| CN106202519A (en) * | 2016-07-22 | 2016-12-07 | 桂林电子科技大学 | A kind of combination user comment content and the item recommendation method of scoring |
| US20190378193A1 (en) * | 2017-02-16 | 2019-12-12 | The University Of Tulsa | System and method for providing recommendations to a target user based upon review and ratings data |
| CN110648163A (en) * | 2019-08-08 | 2020-01-03 | 中山大学 | Recommendation algorithm based on user comments |
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Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150248720A1 (en) * | 2014-03-03 | 2015-09-03 | Invent.ly LLC | Recommendation engine |
| CN105550211A (en) * | 2015-12-03 | 2016-05-04 | 云南大学 | Social network and item content integrated collaborative recommendation system |
| CN106202519A (en) * | 2016-07-22 | 2016-12-07 | 桂林电子科技大学 | A kind of combination user comment content and the item recommendation method of scoring |
| US20190378193A1 (en) * | 2017-02-16 | 2019-12-12 | The University Of Tulsa | System and method for providing recommendations to a target user based upon review and ratings data |
| CN110648163A (en) * | 2019-08-08 | 2020-01-03 | 中山大学 | Recommendation algorithm based on user comments |
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