Understanding User Intent Modeling for Conversational Recommender Systems: A Systematic Literature Review

This dataset compiles the results of a systematic literature review on user intent modeling in Natural Language Processing (NLP), with a focus on its application in conversational recommender systems. Over 13,000 papers from the past decade have been analyzed to provide a thorough understanding of the prevalent AI models used in this area. The dataset includes detailed examinations of various machine learning models such as SVM, LDA, Naive Bayes, BERT, Word2vec, and MLP, highlighting their advantages, limitations, and suitability for different scenarios in recommender systems.

Additionally, the dataset encompasses a wide range of applications of user intent modeling across sectors such as e-commerce, healthcare, education, social media, and virtual assistants. It sheds light on how these models aid in delivering personalized recommendations, detecting fake reviews, providing health interventions, tailoring educational content, and enhancing user experience on social media.

A key component of the dataset is a decision model, derived from the literature review, designed to assist researchers and developers in selecting the most appropriate machine learning model for specific user intent modeling tasks in recommender systems. This model addresses the challenge posed by the variety of available models and the lack of a clear classification scheme.

Furthermore, the dataset includes the outcomes of two academic case studies conducted to assess the utility of the decision model. These case studies follow Yin's guidelines and provide practical insights into the application of the decision model in real-world scenarios.

Researchers, developers, and practitioners in the field of NLP, AI, and recommender systems will find this dataset invaluable for navigating the complex landscape of user intent modeling. It not only synthesizes scattered research but also provides a practical tool for model selection, thereby contributing significantly to the advancement of personalized user experiences in various domains.

Keywords: User Intent Modeling, NLP, Conversational Recommender Systems, Machine Learning, Systematic Literature Review, Decision Model