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Full-Text Articles in Physical Sciences and Mathematics
A Common Approach For Consumer And Provider Fairness In Recommendations, Dimitris Sacharidis, Kyriakos Mouratidis, Dimitrios Kleftogiannis
A Common Approach For Consumer And Provider Fairness In Recommendations, Dimitris Sacharidis, Kyriakos Mouratidis, Dimitrios Kleftogiannis
Research Collection School Of Computing and Information Systems
We present a common approach for handling consumer and provider fairness in recommendations. Our solution requires defining two key components, a classification of items and a target distribution, which together define the case of perfect fairness. This formulation allows distinct fairness concepts to be specified in a common framework. We further propose a novel reranking algorithm that optimizes for a desired trade-off between utility and fairness of a recommendation list.
Mining Semantic Knowledge Graphs To Add Explainability To Black Box Recommender Systems, Mohammed Alshammari, Olfa Nasraoui, Scott Sanders
Mining Semantic Knowledge Graphs To Add Explainability To Black Box Recommender Systems, Mohammed Alshammari, Olfa Nasraoui, Scott Sanders
Faculty Scholarship
Recommender systems are being increasingly used to predict the preferences of users on online platforms and recommend relevant options that help them cope with information overload. In particular, modern model-based collaborative filtering algorithms, such as latent factor models, are considered state-of-the-art in recommendation systems. Unfortunately, these black box systems lack transparency, as they provide little information about the reasoning behind their predictions. White box systems, in contrast, can, by nature, easily generate explanations. However, their predictions are less accurate than sophisticated black box models. Recent research has demonstrated that explanations are an essential component in bringing the powerful predictions of …
Towards Personalized Data-Driven Bundle Design With Qos Constraint, Mustafa Misir, Hoong Chuin Lau
Towards Personalized Data-Driven Bundle Design With Qos Constraint, Mustafa Misir, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
In this paper, we study the bundle design problem for offering personalized bundles of services using historical consumer redemption data. The problem studied here is for an operator managing multiple service providers, each responsible for an attraction, in a leisure park. Given the specific structure of interactions between service providers, consumers and the operator, a bundle of services is beneficial for the operator when the bundle is underutilized by service consumers. Such revenue structure is commonly seen in the cable television and leisure industries, creating strong incentives for the operator to design bundles containing lots of not-so-popular services. However, as …
Cidf: A Clustering-Based Interaction-Driven Friending Algorithm For The Next-Generation Social Networks, Aadil Alshammari, Abdelmounaam Rezgui
Cidf: A Clustering-Based Interaction-Driven Friending Algorithm For The Next-Generation Social Networks, Aadil Alshammari, Abdelmounaam Rezgui
Faculty Publications - Information Technology
Online social networks, such as Facebook, have been massively growing over the past decade. Recommender algorithms are a key factor that contributes to the success of social networks. These algorithms, such as friendship recommendation algorithms, are used to suggest connections within social networks. Current friending algorithms are built to generate new friendship recommendations that are most likely to be accepted. Yet, most of them are weak connections as they do not lead to any interactions. Facebook is well known for its Friends-of-Friends approach which recommends familiar people. This approach has a higher acceptance rate but the strength of the connections, …
The Seven Layers Of Complexity Of Recommender Systems For Children In Educational Contexts, Emiliana Murgia, Monica Landoni, Theo Huibers, Jerry Alan Fails, Maria Soledad Pera
The Seven Layers Of Complexity Of Recommender Systems For Children In Educational Contexts, Emiliana Murgia, Monica Landoni, Theo Huibers, Jerry Alan Fails, Maria Soledad Pera
Computer Science Faculty Publications and Presentations
Recommender systems (RS) in their majority focus on an average target user: adults. We argue that for non-traditional populations in specific contexts, the task is not as straightforward–we must look beyond existing recommendation algorithms, premises for interface design, and standard evaluation metrics and frameworks. We explore the complexity of RS in an educational context for which young children are the target audience. The aim of this position paper is to spell out, label, and organize the specific layers of complexity observed in this context.
With A Little Help From My Friends: Use Of Recommendations At School, Maria Soledad Pera, Emiliana Murgia, Monica Landoni, Theo Huibers
With A Little Help From My Friends: Use Of Recommendations At School, Maria Soledad Pera, Emiliana Murgia, Monica Landoni, Theo Huibers
Computer Science Faculty Publications and Presentations
In this exploratory paper, we study the usage of recommendations by and for children (ages 9 to 11) in an educational setting. From our preliminary analysis, it becomes apparent that recommender systems (RS) could provide extra support to and help children successfully complete inquiry tasks. Nonetheless, children have difficulty in recognizing the role of RS, in terms of aiding information discovery for classroom assignments. Findings from our study set a foundation that can inform future design and development of RS for children that support classroom-related work.
An Explainable Autoencoder For Collaborative Filtering Recommendation, Pegah Sagheb Haghighi, Olurotimi Seton, Olfa Nasraoui
An Explainable Autoencoder For Collaborative Filtering Recommendation, Pegah Sagheb Haghighi, Olurotimi Seton, Olfa Nasraoui
Faculty Scholarship
Autoencoders are a common building block of Deep Learning architectures, where they are mainly used for representation learning. They have also been successfully used in Collaborative Filtering (CF) recommender systems to predict missing ratings. Unfortunately, like all black box machine learning models, they are unable to explain their outputs. Hence, while predictions from an Autoencoderbased recommender system might be accurate, it might not be clear to the user why a recommendation was generated. In this work, we design an explainable recommendation system using an Autoencoder model whose predictions can be explained using the neighborhood based explanation style. Our preliminary work …