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

Abstract

Context: User intent modeling is a crucial process in Natural Language Processing that aims to identify the underlying purpose behind a user’s request, enabling personalized responses. With a vast array of approaches introduced in the literature (over 13,000 papers in the last decade), understanding the related concepts and commonly used models in AI-based systems is essential. Method: We conducted a systematic literature review to gather data on models typically employed in designing conversational recommender systems. From the collected data, we developed a decision model to assist researchers in selecting the most suitable models for their systems. Additionally, we performed two case studies to evaluate the effectiveness of our proposed decision model. Results: Our study analyzed 59 distinct models and identified 74 commonly used features. We provided insights into potential model combinations, trends in model selection, quality concerns, evaluation measures, and frequently used datasets for training and evaluating these models. Contribution: Our study contributes practical insights and a comprehensive understanding of user intent modeling, empowering the development of more effective and personalized conversational recommender systems. With the Conversational Recommender System, researchers can perform a more systematic and efficient assessment of fitting intent modeling frameworks.

Analyzing Social Exchange Motives With Theory-Driven Data and Machine Learning

“…Machine learning approaches, such as collaborative filtering, matrix factorization, and Multilayer Perceptron have been widely adopted to predict the goals or objectives of humans in research areas such as recommendation systems. For example, collaborative filtering methods leverage user-item interactions to uncover similarities between users [12], while matrix factorization techniques capture user-item relationships to predict item users like [13]. Deep learning models, especially those that combine neural collaborative filtering and other algorithms, such as multilayer perceptrons, have demonstrated superior performance in predicting user preferences and goals [14].…”

“…Recent successes in goal prediction have primarily occurred in fields in which predictions are made from text or speech data. See [12] for a review. Although the proliferation of digital platforms has given rise to plenty of textual and speech data, it has also increased the availability of videos, images, and traces of interaction.…”