阅读说明：本技术 一种食谱推荐方法、装置、烹饪电器和计算机存储介质 (Recipe recommendation method and device, cooking appliance and computer storage medium ) 是由 黄源甲 龙永文 周宗旭 肖群虎 于 2018-06-19 设计创作，主要内容包括：本发明实施例公开了一种食谱推荐方法、装置、烹饪电器和计算机存储介质,该方法包括：获取N个用户的历史操作行为数据；根据所述N个用户的历史操作行为数据,得出所述N个用户对食谱的评分数据；采用ALS矩阵分解推荐模型,对所述N个用户对食谱的评分数据进行训练,得出推荐模型；采集任意一个用户对至少一个食谱的实时操作行为；根据所述实时操作行为以及所述推荐模型,得出食谱推荐结果。如此,能够提高食谱推荐的实时性和准确性。(The embodiment of the invention discloses a recipe recommendation method, a device, a cooking appliance and a computer storage medium, wherein the method comprises the following steps: acquiring historical operation behavior data of N users; obtaining score data of the N users for recipes according to the historical operation behavior data of the N users; adopting an ALS matrix decomposition recommendation model to train score data of the recipes of the N users to obtain a recommendation model; collecting real-time operation behaviors of any user on at least one recipe; and obtaining a recipe recommendation result according to the real-time operation behavior and the recommendation model. Therefore, the real-time performance and accuracy of recipe recommendation can be improved.)

1. A recipe recommendation method, characterized in that the method comprises:

acquiring historical operation behavior data of N users, wherein the historical operation behavior data of each user is used for representing: historical data of operational behavior of each user for at least one recipe; n is a natural number greater than 1;

obtaining score data of the N users for recipes according to the historical operation behavior data of the N users;

adopting an Alternating Least Square (ALS) matrix decomposition recommendation model to train score data of the recipes of the N users to obtain a recommendation model;

collecting real-time operation behaviors of any user on at least one recipe; and obtaining a recipe recommendation result according to the real-time operation behavior and the recommendation model.

2. The method according to claim 1, wherein the deriving score data of recipes for the N users from historical operational behavior data of the N users comprises: dividing the N users into a plurality of groups of users according to the predetermined attributes of the N users; obtaining score data of each group of users on the recipes according to the historical operation behavior data of each group of users;

the training of the scoring data of the recipes of the N users to obtain a recommendation model comprises the following steps: training the score data of the recipes of each group of users respectively to obtain a recommendation submodel corresponding to each group of users; merging the recommended sub-models corresponding to the groups of users to obtain a recommended model;

obtaining a recipe recommendation result according to the real-time operation behavior and the recommendation model, wherein the method comprises the following steps: determining a user group to which the any user belongs according to the predetermined attribute of the any user; and obtaining a recipe recommendation result according to the real-time operation behavior and the recommendation sub-model corresponding to the determined user group.

3. The method of claim 2, wherein the predetermined attribute comprises one of: age, gender, occupation, liveness.

4. The method according to claim 1, wherein the deriving score data of recipes for the N users from historical operational behavior data of the N users comprises:

setting a weight for each operational behavior for the recipe;

and according to the set weight, carrying out weighted summation operation on the historical operation behavior data of the N users to obtain the score data of the recipes of the N users.

5. The method of claim 1, wherein the historical operational behavior data of the N users comprises: historical operation behavior data collected by the cooking appliance and historical operation behavior data collected by an application program of the terminal.

6. The method according to claim 1, wherein the operation time corresponding to the historical operation behavior data of the N users is after a set time point, and a time interval between the set time point and the current time is less than a set threshold.

7. The method according to claim 1, wherein the deriving score data of recipes for the N users from historical operational behavior data of the N users comprises:

obtaining score data of the recipes by the N users by adopting an off-line calculation mode according to the historical operation behavior data of the N users;

the training of the scoring data of the recipes of the N users to obtain a recommendation model comprises the following steps:

and training the scoring data of the recipes of the N users in an off-line calculation mode to obtain a recommendation model.

8. The method of claim 1, further comprising:

and after the real-time operation behavior of any user on at least one recipe is collected, when the any user does not belong to the N users, a default recipe recommendation algorithm is adopted to obtain a recommendation result aiming at the any user.

9. A recipe recommendation apparatus, characterized in that the apparatus comprises a processor and a memory for storing a computer program executable on the processor; wherein the content of the first and second substances,

the processor is adapted to perform the steps of the method of any one of claims 1 to 8 when running the computer program.

10. A cooking appliance characterized in that it comprises a recipe recommendation device according to claim 9.

11. A computer storage medium on which a computer program is stored, characterized in that the computer program realizes the steps of the method of any one of claims 1 to 8 when executed by a processor.

Technical Field

The invention relates to a data mining technology, and relates to a recipe recommendation method and device, a cooking appliance and a computer storage medium.

Background

At present, with the rapid development of economy, the living standard of people is continuously improved, and more attention is paid to the health problem of food, however, most people are facing a large amount of gourmets when having a dinner at home or going out, and it is unclear which kind is suitable for oneself. In the prior art, the recipe recommendation can be performed according to the physical condition and the daily eating habit of the user, but the user cannot acquire the recipe in real time during cooking.

Disclosure of Invention

In order to solve the technical problem, embodiments of the present invention desirably provide a recipe recommendation method, an apparatus, a cooking appliance, and a computer storage medium, and aim to solve the problem that a user cannot obtain a recipe in real time during cooking.

The embodiment of the invention provides a recipe recommendation method, which comprises the following steps:

acquiring historical operation behavior data of N users, wherein the historical operation behavior data of each user is used for representing: historical data of operational behavior of each user for at least one recipe; n is a natural number greater than 1;

obtaining score data of the N users for recipes according to the historical operation behavior data of the N users;

decomposing a recommendation model by adopting an Alternating Least Square (ALS) matrix, and training score data of the recipes of the N users to obtain the recommendation model;

collecting real-time operation behaviors of any user on at least one recipe; and obtaining a recipe recommendation result according to the real-time operation behavior and the recommendation model.

In the above scheme, the obtaining score data of the recipes by the N users according to the historical operation behavior data of the N users includes: dividing the N users into a plurality of groups of users according to the predetermined attributes of the N users; obtaining score data of each group of users on the recipes according to the historical operation behavior data of each group of users;

the training of the scoring data of the recipes of the N users to obtain a recommendation model comprises the following steps: training the score data of the recipes of each group of users respectively to obtain a recommendation submodel corresponding to each group of users; merging the recommended sub-models corresponding to the groups of users to obtain a recommended model;

obtaining a recipe recommendation result according to the real-time operation behavior and the recommendation model, wherein the method comprises the following steps: determining a user group to which the any user belongs according to the predetermined attribute of the any user; and obtaining a recipe recommendation result according to the real-time operation behavior and the recommendation sub-model corresponding to the determined user group.

In the foregoing solution, the predetermined attribute includes one of: age, gender, occupation, liveness.

In the above scheme, the obtaining score data of the recipes by the N users according to the historical operation behavior data of the N users includes:

setting a weight for each operational behavior for the recipe;

and according to the set weight, carrying out weighted summation operation on the historical operation behavior data of the N users to obtain the score data of the recipes of the N users.

In the foregoing solution, the historical operation behavior data of the N users includes: historical operation behavior data collected by the cooking appliance and historical operation behavior data collected by an application program of the terminal.

In the above scheme, the operation time corresponding to the historical operation behavior data of the N users is after a set time point, and the time interval between the set time point and the current time is less than a set threshold.

In the above scheme, the obtaining score data of the recipes by the N users according to the historical operation behavior data of the N users includes:

obtaining score data of the recipes by the N users by adopting an off-line calculation mode according to the historical operation behavior data of the N users;

the training of the scoring data of the recipes of the N users to obtain a recommendation model comprises the following steps:

and training the scoring data of the recipes of the N users in an off-line calculation mode to obtain a recommendation model.

In the above scheme, the method further comprises: and after the real-time operation behavior of any user on at least one recipe is collected, when the any user does not belong to the N users, a default recipe recommendation algorithm is adopted to obtain a recommendation result aiming at the any user.

An embodiment of the present invention further provides a recipe recommendation apparatus, where the apparatus includes a processor and a memory for storing a computer program capable of running on the processor; wherein the content of the first and second substances,

the processor is configured to execute the steps of any one of the recipe recommendation methods described above when running the computer program.

The embodiment of the invention also provides a cooking appliance, which comprises any one of the recipe recommending devices.

Embodiments of the present invention further provide a computer storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of any one of the recipe recommendation methods described above.

In the embodiment of the present invention, first, historical operation behavior data of N users is obtained, where the historical operation behavior data of each user is used to represent: historical data of operational behavior of each user for at least one recipe; n is a natural number greater than 1; then, obtaining score data of the N users for the recipes according to the historical operation behavior data of the N users; adopting an Alternating Least Square (ALS) matrix decomposition recommendation model to train score data of the recipes of the N users to obtain a recommendation model; finally, collecting the real-time operation behavior of any user on at least one recipe; and obtaining a recipe recommendation result according to the real-time operation behavior and the recommendation model. Therefore, the recipe recommendation result is obtained according to the real-time operation behavior, so that the recipe recommendation has real-time performance, and in addition, the historical operation behavior data of the user needs to be considered when the recipe recommendation is carried out, so that the recipe recommendation has the characteristic of high accuracy.

Drawings

Fig. 1 is a schematic diagram of a recipe recommendation apparatus according to an embodiment of the present invention;

FIG. 2 is a first flowchart of a recipe recommendation method according to an embodiment of the present invention;

FIG. 3 is a flowchart II of a recipe recommendation method according to an embodiment of the present invention;

fig. 4 is another schematic diagram of a recipe recommendation apparatus according to an embodiment of the present invention.

Detailed Description

The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

In the embodiment of the invention, the recipe recommendation can be realized by using a recipe recommendation device, and optionally, the recipe recommendation device can be a cooking appliance or a part of the cooking appliance; exemplary cooking appliances include, but are not limited to, pressure cookers, electric cookers, soymilk makers, bread makers, and the like.

Fig. 1 is a schematic diagram of a recipe recommendation apparatus according to an embodiment of the present invention, as shown in fig. 1, here, the recipe recommendation apparatus 10 described above may include a data acquisition module 101.

The data acquisition module 101 is configured to perform data acquisition of user operation behaviors, for example, the user operation behaviors acquired by the data acquisition module represent historical data of operation behaviors of a user with respect to at least one recipe, and for example, the operation behaviors of the user with respect to the at least one recipe may include at least one of the following: browsing, collecting, praise, canceling collecting, commenting and searching. In a specific implementation, the data acquisition module 101 may be a data acquisition device embedded in the cooking appliance.

Optionally, the recipe recommendation apparatus 10 may further include a data caching module 102; the data caching module 102 may cache data collected by the data collection module 101; in particular implementation, the data caching module 102 may be a memory embedded in the cooking appliance.

Optionally, the recipe recommendation device 10 may further include a communication module 103; the communication module 103 is used for realizing communication and data interaction between the recipe recommendation device 10 and external equipment; illustratively, the communication module 103 may enable the recipe recommendation device 10 to connect to the internet, thereby enabling the recipe recommendation device 10 to interact with the server; in practical implementation, the communication module 103 may be implemented by using an antenna, a baseband chip, or the like.

Optionally, the recipe recommendation device 10 may further include a display module 104; the display module 104 is used for displaying the recommended recipes; optionally, the display module 104 may also perform positive and negative feedback on the currently recommended recipe; in practical implementation, the display module 104 may be implemented by a display panel or the like.

Based on the recipe recommendation apparatus described above, the following embodiments are proposed.