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Data sparsity, that is a common problem in neighbor-based collaborative filtering domain, usually complicates the process of item recommendation. This problem is more serious in collaborative ranking domain, in which calculating the users…

Data sharing is the fuel of the galloping artificial intelligence economy, providing diverse datasets for training robust models. Trust between data providers and data consumers is widely considered one of the most important factors for…

Recommendation systems have received considerable attention recently. However, most research has been focused on improving the performance of collaborative filtering (CF) techniques. Social networks, indispensably, provide us extra…

Increasing data volumes requires additional rating techniques. Reputation systems are the subject of much research. There are various techniques to rate content that facilitate the search of quality content. Page rank, citation index and…

This paper explores recommender systems in social networks which leverage information such as item rating, intra-item similarities, and trust graph. We demonstrate that item-rating information is more influential than other information…

The emergence of online enterprises spread across continents have given rise to the need for expert identification in this domain. Scenarios that includes the intention of the employer to find tacit expertise and knowledge of an employee…

How do the ratings of critics and amateurs compare and how should they be combined? Previous research has produced mixed results about the first question, while the second remains unanswered. We have created a new, unique dataset, with wine…

Recommender systems are personalized information access applications; they are ubiquitous in today's online environment, and effective at finding items that meet user needs and tastes. As the reach of recommender systems has extended, it…

Bipartite ranking is a fundamental machine learning and data mining problem. It commonly concerns the maximization of the AUC metric. Recently, a number of studies have proposed online bipartite ranking algorithms to learn from massive…

Thick two-sided matching platforms, such as the room-rental market, face the challenge of showing relevant objects to users to reduce search costs. Many platforms use ranking algorithms to determine the order in which alternatives are shown…

Machine Learning models have many potentially beneficial applications in education settings, but a key barrier to their development is securing enough data to train these models. Labelling educational data has traditionally relied on highly…

Analyzing social media trends can create a win-win situation for both creators and consumers. Creators can receive fair compensation, while consumers gain access to engaging, relevant, and personalized content. This paper proposes a new…

This paper studies ranking policies in a stylized trial-offer marketplace model, in which a single firm offers products and has consumers with heterogeneous preferences. Consumer trials are influenced by past purchases and the ranking of…

Recommender systems learn from historical users' feedback that is often non-uniformly distributed across items. As a consequence, these systems may end up suggesting popular items more than niche items progressively, even when the latter…

Nowadays, several crowdsourcing projects exploit social choice methods for computing an aggregate ranking of alternatives given individual rankings provided by workers. Motivated by such systems, we consider a setting where each worker is…

With the popularity of smart terminals, such as the Internet of Things, crowdsensing is an emerging data aggregation paradigm, which plays a pivotal role in data-driven applications. There are some key issues in the development of…

Understanding the way individuals are interconnected in social networks is of prime significance to predict their collective outcomes. Leveraging a large-scale dataset from a knowledge-sharing website, this paper presents an exploratory…

In this work we analyze how reputation-based interactions influence the emergence of innovations. To do so, we make use of a dynamic model that mimics the discovery process by which, at each time step, a pair of individuals meet and merge…

Socially-based recommendation systems have recently attracted significant interest, and a number of studies have shown that social information can dramatically improve a system's predictions of user interests. Meanwhile, there are now many…

Interpreting the performance results of models that attempt to realize user behavior in platforms that employ recommenders is a big challenge that researchers and practitioners continue to face. Although current evaluation tools possess the…