Performance Analysis of Demographic Aware Conversational Recommender System
Implementation Plan
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Scenario 1; (With MetaCRS Evaluation)
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Step 1: Initially, we load and collect data from the Movielens Dataset
Step 2: Next, we pre-train TransE embeddings for users and their attributes to create demographic-aware representations.
Step 3: Next, we perform human-like user conversations using a fine-tuned model trained on PersonaChat and DailyDialog.
Step 4: Next, we apply RoBERTa classifier to infer user gender, and emotion from each sentence-level response.
Step 5: Next, we classify user intent using a BERT model and compute semantic similarity using Sentence-BERT for fuzzy matching.
Step 6: Next, we evaluate MetaCRS dynamic demographic vectors and adaptive profiling.
Step 7: Finally, we plot performance for the following metrics:
7.1: Number of epochs vs. Accuracy (%)
7.2: Number of epochs vs. Precision (%)
7.3: Number of epochs vs. Recall (%)
7.4: Number of epochs vs. F1-score (%)
Scenario 2; (Without MetaCRS Evaluation)
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Step 1: Initially, we load and collect data from the Movielens Dataset
Step 2: Next, we pre-train TransE embeddings for users and their attributes to create demographic-aware representations.
Step 3: Next, we perform human-like user conversations using a fine-tuned model trained on PersonaChat and DailyDialog.
Step 4: Next, we apply RoBERTa classifier to infer user gender, and emotion from each sentence-level response.
Step 5: Next, we classify user intent using a BERT model and compute semantic similarity using Sentence-BERT for fuzzy matching.
Step 6: Finally, we plot performance for the following metrics:
6.1: Number of epochs vs. Accuracy (%)
6.2: Number of epochs vs. Precision (%)
6.3: Number of epochs vs. Recall (%)
6.4: Number of epochs vs. F1-score (%)
Software requirement:
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1. Development Tool: Python 3.11.4 or above
2. Operating System: Windows 10 (64-bit) or above
Dataset:
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Link :- https://www.kaggle.com/datasets/grouplens/movielens-20m-dataset
Note
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1) If the plan does not meet your requirements, provide detailed steps, parameters, models, or expected results in advance. Once implemented, changes won’t be possible without prior input; otherwise, we’ll proceed as per our implementation plan.
2) If the plan satisfies your requirement, Please confirm with us.
3) Project based on Simulation only, not a real time project.
4) If you have any changes in the Dataset , kindly provide before implementation.Our work is completely based on dataset values.
5) Please understand that any modifications made to the confirmed implementation plan will not be made after the project development.
