Joint Group Profiling and Recommendation via Deep Neural Network-based Multi-Task Learning
This work addresses challenges in group recommender systems for applications like social or collaborative settings, but it is incremental as it builds on existing multi-task learning and attention mechanisms.
The paper tackles the problem of improving group recommendation accuracy by jointly learning group profiling and recommendation tasks within a deep neural network-based multi-task learning framework, resulting in consistent outperformance of baseline models in experiments on real-world datasets.
Group recommender systems aim to generate recommendations that align with the collective preferences of a group, introducing challenges that differ significantly from those in individual recommendation scenarios. This paper presents Joint Group Profiling and Recommendation via Deep Neural Network-based Multi-Task Learning, a framework that unifies group profiling and recommendation tasks within a single model. By jointly learning these tasks, the model develops a deeper understanding of group dynamics, leading to improved recommendation accuracy. The shared representations between the two tasks facilitate the discovery of latent features essential to both, resulting in richer and more informative group embeddings. To further enhance performance, an attention mechanism is integrated to dynamically evaluate the relevance of different group features and item attributes, ensuring the model prioritizes the most impactful information. Experiments and evaluations on real-world datasets demonstrate that our multi-task learning approach consistently outperforms baseline models in terms of accuracy, validating its effectiveness and robustness.