Project Background:
Conversational Recommender Systems (CRS) represent a significant advance in recommendation systems. These systems have emerged in response to the growing need for more personalized and engaging user experiences in various domains, such as e-commerce, content recommendation, and online services. The CRS stems from the desire to enhance user interactions with recommendation engines by facilitating natural and dynamic conversations. Traditional recommendation systems often provide static lists of items based on user preferences and historical data. In contrast, conversational recommenders leverage natural language processing and dialog management techniques to engage users in interactive conversations, allowing for a more context-aware and personalized recommendation process. These systems aim to bridge the gap between user needs and the vast pool of available options, ultimately improving user satisfaction and decision-making. This project focuses on developing and optimizing the algorithms, models, and user interfaces necessary to create effective conversational recommender systems, aiming to deliver more relevant and tailored user recommendations.
Problem Statement
- In this project, CRS revolves around improving the efficacy and user experience of recommendation systems by integrating natural language conversation into the recommendation process.
- Traditional recommendation systems cannot often understand the nuanced preferences, dynamic context, and evolving user needs.
- It aims to address some limitations by engaging users in interactive conversations to gather real-time feedback, clarify preferences, and provide personalized recommendations.
- The main objective is to create systems that can carry out meaningful and context-aware conversations with users, adapt to changing preferences, and offer recommendations with the users current intent and context.
Aim and Objectives
- Improve recommendation accuracy, personalization, and user engagement by integrating natural language conversations into the recommendation process.
- Enhance recommendation relevance by gathering real-time user feedback and adapting to evolving preferences.
- Improve user satisfaction and decision-making by providing context-aware and personalized recommendations.
- Develop effective natural language understanding and dialog management techniques for interactive conversations.
- Optimize recommendation algorithms to work seamlessly within conversational interfaces.
- Create user-friendly interfaces that facilitate dynamic and engaging interactions with the recommendation system.
Contributions to Conversational Recommender Systems
- Development of conversational agents can engage in natural and dynamic conversations with users, providing recommendations and gathering feedback effectively.
- Incorporation of contextual information such as location, time, and user history utilized to provide context-aware recommendations.
- Integration of reinforcement learning to optimize recommendations and engage users to maximize long-term satisfaction and user engagement.
- Utilization of multiple data modalities like text, images, and audio provides more diverse and engaging recommendations.
- Development of specific evaluation metrics for conversational recommender systems that go beyond traditional recommendation accuracy measures.
Deep Learning Algorithms for Conversational Recommender Systems
- Recurrent Neural Networks (RNNs)
- Long Short-Term Memory (LSTM) networks
- Gated Recurrent Unit (GRU)
- Transformer models
- BERT (Bidirectional Encoder Representations from Transformers)
- GPT (Generative Pre-trained Transformer)
- Seq2Seq (Sequence-to-Sequence) models
- Attention mechanisms
- Reinforcement Learning for recommendation
- Variational Autoencoders (VAE)
- Deep Q-Networks (DQN)
- Memory-augmented neural networks
Datasets for Conversational Recommender Systems
- DSTC Datasets
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ReDial Dataset
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Persona-Chat Dataset
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ConvAI Dataset
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OpenDialKG Dataset
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CoNaLa Dataset
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MWOZ Dataset
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CamRest676 Dataset
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DailyDialog Dataset
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Yelp Dataset Challenge
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MovieLens Dataset
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Reddit Conversational AI Dataset
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E-Commerce Conversational Dataset
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Amazon Product Review Dataset
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Twitter Conversational Dataset
Performance Metrics
- Recall
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Precision
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F1-Score
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Mean Reciprocal Rank
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Hit Rate
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Coverage
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Response Time
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Response Quality
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Conversational Depth
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Conversational Length
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Turn-Level Metrics
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Normalized Discounted Cumulative Gain
Software Tools and Technologies:
Operating System: Ubuntu 18.04 LTS 64bit / Windows 10
Development Tools: Anaconda3, Spyder 5.0, Jupyter Notebook
Language Version: Python 3.9
Python Libraries:
1. Python ML Libraries:
- Scikit-Learn
- Numpy
- Pandas
- Matplotlib
- Seaborn
- Docker
- MLflow
2. Deep Learning Frameworks:
- Keras
- TensorFlow
- PyTorch
