Recommender systems (RSs) have become key components driving the success of e-commerce and other platforms where revenue and customer satisfaction is dependent on the user’s ability to discover desirable items in large catalogues. As the number of users and items on a platform grows, the computational complexity and the sparsity problem constitute important challenges for any recommendation algorithm. In addition, the most widely studied filtering-based RSs, while effective in providing suggestions for established users and items, are known for their poor performance for the new user and new item (cold-start) problems. Stereotypical modelling of users and items is a promising approach to solving these problems. A stereotype represents an aggregation of the characteristics of the items or users which can be used to create general user or item classes. We propose a set of methodologies for the automatic generation of stereotypes to address the cold-start problem. The novelty of the proposed approach rests on the findings that stereotypes built independently of the user-to-item ratings improve both recommendation metrics and computational performance during cold-start phases. The resulting RS can be used with any machine learning algorithm as a solver, and the improved performance gains due to rate-agnostic stereotypes are orthogonal to the gains obtained using more sophisticated solvers. The paper describes how such item-based stereotypes can be evaluated via a series of statistical tests prior to being used for recommendation. The proposed approach improves recommendation quality under a variety of metrics and significantly reduces the dimension of the recommendation model.
Consideration of multiple viewpoints on a contentious issue is critical for avoiding bias and assisting in the formulation of rational decisions. We observe that the current model imposes a constraint on diversity. This is because the conventional attention mechanism is biased toward a single semantic aspect of the claim, whereas the claim may contain multiple semantic aspects. Additionally, disregarding common-sense knowledge may result in generating perspectives that violate known facts about the world. The proposed approach is divided into two stages: the first stage considers multiple semantic aspects, which results in more diverse generated perspectives; the second stage improves the quality of generated perspectives by incorporating common-sense knowledge. We train the model on each stage using reinforcement learning and automated metric scores. The experimental results demonstrate the effectiveness of our proposed model in generating a broader range of perspectives on a contentious subject.
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