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  1. Recommender systems traditionally find the most relevant products or services for users tailored to their needs or interests but they ignore the interests of the other sides of the market (aka stakeholders). In this paper, we propose to use a Ranked Bandit approach for an online multi-stakeholder recommender system that sequentially selects top 𝑘 items according to the relevance and priority of all the involved stakeholders. We presented three different criteria to consider the priority of each stakeholder when evaluating our approach. Our extensive experimental results on a movie dataset showed that the contextual multi-armed bandits with a relevance function make a higher level of satisfaction for all involved stakeholders in the long term. Keywords: Multi-stakeholder Recommender Systems; Multi-armed Bandits; Ranked Bandit; 
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  2. Traditional recommender systems help users find the most relevant products or services to match their needs and preferences. However, they overlook the preferences of other sides of the market (aka stakeholders) involved in the system. In this paper, we propose to use contextual bandit algorithms in multi-stakeholder platforms where a multi-sided relevance function with adjusting weights is modeled to consider the preferences of all involved stakeholders. This algorithm sequentially recommends the items based on the contextual features of users along with the priority of the stakeholders and their relevance to the items.Our extensive experimental results on a dataset consisting of MovieLens (1m), IMDB (81k+), and a synthetic dataset show that our proposed approach outperforms the baseline methods and provides a good trade-off between the satisfaction of different stakeholders over time. 
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  3. null (Ed.)
    Recommender systems are widely used to help customers find the most relevant and personalized products or services tailored to their preferences. However, traditional systems ignore the preferences of the other side of the market, e.g., “product suppliers” or “service providers”, towards their customers. In this paper, we present 2SRS a Two-Sided Recommender System that recommends coupons, supplied by local businesses, to passerby while considering the preferences of both sides towards each other. For example, some passerby may only be interested in coffee shops whereas certain businesses may only be interested in sending coupons to new customers only. Our experimental results show that 2SRS delivers higher satisfaction when considering both sides of the market compared to the baseline methods. 
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  4. null (Ed.)
    Public transit is one of the first things that come to mind when someone talks about “smart cities.” As a result, many technologies, applications, and infrastructure have already been deployed to bring the promise of the smart city to public transportation. Most of these have focused on answering the question, “When will my bus arrive?”; little has been done to answer the question, “How full will my next bus be?” which also dramatically affects commuters’ quality of life. In this article, we consider the bus fullness problem. In particular, we propose two different formulations of the problem, develop multiple predictive models, and evaluate their accuracy using data from the Pittsburgh region. Our predictive models consistently outperform the baselines (by up to 8 times). 
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