Performance Analysis of Effective Conversational Recommender System
Implementation plan:
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Step 1: Initially, we load and Collect data from the LASTFM Dataset.
Step 2: Then, we Construct a static knowledge graph using TransE embedding from offline user-item-attribute interactions.
Step 3: Next, we Extract state representations using a Graph Convolutional Network (GCN) variant with dynamic edge weights.
Step 4: Next, we Implement a multi-channel transformer-based meta-reinforcement learning method to process historical feedback.
Step 5: Next, we Generate adaptive user preference policies using accepted/rejected attributes and rejected items channels.
Step 6: Next, we detect optimal actions using Deep Q-Learning with a prioritized replay method .
Step 7:Next, we Evaluate the CRS values using SR@t and average conversation turns on time-sorted interactions data.
Step 8: Finally, we plot performance for the following metrics:
8.1: Number of epochs vs. Accuracy (%)
8.2: Number of epochs vs. Precision (%)
8.3: Number of epochs vs. Recall (%)
8.4: 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/harshal19t/lastfm-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.
