Publications

Abstract : A commercially viable multi-stakeholder recommendation system maximizes the utility gain by learning the personalized preferences of multiple stakeholders, such as consumers and producers. Existing multi-stakeholder studies rely on a consumer-item interaction matrix to evaluate the producers' preferences and utility gain. However, these methods result in a negligible boost in producers' utility, as consumer-item interaction provides only a limited insight into producers' preferences. Instead, an independent producer-item interaction matrix may better represent the needs and interests of producers. The deep neural networks have recently achieved encouraging results in a recommendation by estimating user preferences and learning user-item non-linear features. The multi-stakeholder recommendation system may employ this strength of the deep neural network to combine consumer-producer preferences and generate the optimal estimate of their common interest. Hence this study proposes a deep neural network-based multi-stakeholder recommendation system model for aggregating consumer and producer preferences. Next, a multi-criteria rating-based interaction matrix is proposed to learn the producers' preference over an item. Further, we perform deep neural network-based model training to generate the cumulative preference matrix by learning and aggregating the preferences of consumer and producer stakeholders. This work performs extensive experiments over Movie Lens-100 K and 1 M datasets with numerous activation functions, hidden layer configuration, and optimizers. The prediction accuracy, ranking, and utility gain-based evaluation results validate the success of the proposed model in developing a multi-criteria matrix for producers' and deep neural network-based multi-stakeholder preference aggregation over the baseline models.