Method and system for dataset classification and recommendation

1 . A system for dataset classification and recommendation, wherein the system comprises:

at least a processor; and

a memory, wherein the memory contains instructions configuring the at least a processor to:

receive and process multimodal user data, wherein receiving and processing multimodal user data comprises:

receiving a first user dataset from a first user, comprising a first plurality of historical interaction data, wherein the first plurality of historical interaction data comprises at least unstructured textual data;

structuring the at least unstructured textual data into structured textual data using natural language processing;

retrieving a set of structured data related to the first user; and

combining the set of structured data and the structured textual data to form a first structured user data set;

determine at least one connection recommendation, wherein determining at least one connection recommendation comprises:

classifying, by an implementation criteria machine-learning model, the first structured user data set to a first plurality of implementation scores;

retrieving a second plurality of implementation scores for a second user;

calculating an implementation delta across the first plurality of implementation scores for the first user and the second plurality of implementation scores for the second user; and

determining the at least one connection recommendation for the first user by applying a rules engine to the implementation delta, wherein the rules engine is configured to:

identify, as a function of the implementation delta, at least one relative implementation weakness of the first user;

identify, for the second user, a corresponding implementation strength associated with the relative implementation weakness; and

generate an ordered list, comprising the at least one connection recommendation, as a function of prioritizing the second user as a function of the corresponding implementation strength selected to address the relative implementation weakness; and

display, through a graphical user interface, the at least one connection recommendation, wherein the graphical user interface:

comprises a plurality of panels; and

is configured to display the at least one connection recommendation ranked according to the ordered list.

2 . The system of claim 1 , wherein structuring the at least unstructured textual data into structured textual data using natural language processing comprises applying a natural language processing module, wherein the natural language processing module comprises name entity recognition tool.

3 . The system of claim 2 , wherein the name entity recognition tool is configured to identify one or more artificial intelligence tools and one or more tasks in at least unstructured textual data.

4 . The system of claim 1 , further comprising a feedback module, wherein the feedback module is configured to:

receive feedback from the first user as a function of a quality of at least one connection recommendation; and

retrain the implementation criteria machine-learning model as a function of the feedback.

5 . The system of claim 1 , further comprising a learning module, wherein the learning module is configured to:

receive feedback from the feedback module; and

modify, by a reinforcement learning model, the rules engine wherein the reinforcement learning model is configure to update a rule logic of the rules engine as a function of the feedback and a quality of at least one connection recommendation.

6 . The system of claim 1 , wherein the memory contains instructions further configuring the at least a processor to generate, using an explanatory output as a function of the at least one connection recommendation.

7 . The system of claim 1 , further comprising an explainability module, wherein the explainability module is configured to:

record one or more results of a plurality of rules applied by the rules engine;

input the one or more results into a large language model (LLM); and

generate one or more natural language explanations as a function of the one or more results, using the LLM.

8 . The system of claim 1 , wherein the rules engine is further configured to:

determine an implementation delta for each of a plurality of implementation score categories as a function of a strength in an area corresponding to a relative weakness in the first plurality of implementation scores for the first user;

identify a second user from a set of candidate users as a function of the second plurality of the implementation delta; and

prioritize the ordered list of the at least one connection recommendation as a function of an aggregation of determined implementation deltas across the plurality of implementation score categories.

9 . The system of claim 1 , wherein retrieving a set of structured data related to the first user comprises using a web crawler to collect public data associated with the first user.

10 . A method for dataset classification and recommendation, the method comprises:

receiving and processing, using at least a processor, multimodal user data, wherein receiving and processing multimodal user data comprises:

receiving a first user dataset from a first user, comprising a first plurality of historical interaction data, wherein the first plurality of historical interaction data comprises at least unstructured textual data;

structuring the at least unstructured textual data into structured textual data using natural language processing;

retrieving a set of structured data related to the first user; and

combining the set of structured data and the structured textual data to form a first structured user data set; and

determining, using the at least a processor, at least one connection recommendation, wherein determining at least one connection recommendation comprises:

classifying, by an implementation criteria machine-learning model, the first structured user data set to a first plurality of implementation scores;

retrieving a second plurality of implementation scores for a second user;

calculating an implementation delta across the first plurality of implementation scores for the first user and the second plurality of implementation scores for the second user; and

determining the at least one connection recommendation for the first user by applying a rules engine to the implementation delta, wherein the rules engine is configured to:

identify, as a function of the implementation delta, at least one relative implementation weakness of the first user;

identify, for the second user, a corresponding implementation strength associated with the relative implementation weakness; and

generate an ordered list, comprising the at least one connection recommendation, as a function of: prioritizing the second user as a function of the corresponding implementation strength selected to address the relative implementation weakness; and

displaying, using the at least a processor and a graphical user interface, the at least one connection recommendation, wherein the graphical user interface:

comprises a plurality of panels; and

is configured to display the at least one connection recommendation ranked according to the ordered list.

11 . The method of claim 10 , wherein structuring the at least unstructured textual data into structured textual data using natural language processing comprises applying a natural language processing module, wherein the natural language processing module comprises name entity recognition tool.

12 . The method of claim 11 , wherein the name entity recognition tool is configured to identify one or more artificial intelligence tools and one or more tasks in at least unstructured textual data.

13 . The method of claim 10 , further comprising receiving, by a feedback module, feedback from the first user as a function of a quality of at least one connection recommendation.

14 . The method of claim 10 , further comprising:

receiving, by a learning module, feedback from the feedback module;

modifying, by a reinforcement learning model, the rules engine wherein the reinforcement learning model is configured to update a rule logic of the rules engine as a function of the feedback and a quality of at least one connection recommendation.

15 . The method of claim 10 , further comprising generating, using an explanatory output as a function of the at least one connection recommendation.

16 . The method of claim 10 , further comprising:

recording, by explainability module, one or more results of a plurality of rules applied by the rules engine;

inputting the one or more results into a large language model (LLM); and

generating one or more natural language explanations as a function of the one or more results, using the LLM.

17 . The method of claim 10 , wherein the rules engine is further configured to:

determine an implementation delta for each of a plurality of implementation score categories as a function of a strength in an area corresponding to a relative weakness in the first plurality of implementation scores for the first user;

identify a second user from a set of candidate users as a function of the second plurality of the implementation delta; and

prioritize the ordered list of the at least one connection recommendation as a function of an aggregation of determined implementation deltas across the plurality of implementation score categories.

18 . The method of claim 10 , wherein retrieving a set of structured data related to the first user comprises using a web crawler to collect public data associated with the first user.