UniRecSys: A Unified Framework for Personalized, Group, Package, and Package-to-Group Recommendations

Recommender systems aim to enhance the overall user experience by providing tailored recommendations for a variety of products and services. These systems help users make more informed decisions, leading to greater user satisfaction with the platform. However, the implementation of these systems largely depends on the context, which can vary from recommending an item or package to a user or a group. This requires careful exploration of several models during the deployment, as there is no comprehensive and unified approach that deals with recommendations at different levels. Furthermore, these individual models must be closely attuned to their generated recommendations depending on the context to prevent significant variation in their generated recommendations. In this paper, we propose a novel unified recommendation framework that addresses all four recommendation tasks, namely personalized, group, package, or package-to-group recommendation, filling the gap in the current research landscape. The proposed framework can be integrated with most of the traditional matrix factorization-based collaborative filtering models. The idea is to enhance the formulation of the existing approaches by incorporating components focusing on the exploitation of the group and package latent factors. These components also help in exploiting a rich latent representation of the user/item by enforcing them to align closely with their corresponding group/package representation. We consider two prominent CF techniques, Regularized Matrix Factorization and Maximum Margin Matrix factorization, as the baseline models and demonstrate their customization to various recommendation tasks. Experiment results on two publicly available datasets are reported, comparing them to other baseline approaches that consider individual rating feedback for group or package recommendations.Comment: 25 page

Probe: Learning Users' Personalized Projection Bias in Intertemporal Bundle Choices

Intertemporal choices involve making decisions that require weighing the costs in the present against the benefits in the future. One specific type of intertemporal choice is the decision between purchasing an individual item or opting for a bundle that includes that item. Previous research assumes that individuals have accurate expectations of the factors involved in these choices. However, in reality, users' perceptions of these factors are often biased, leading to irrational and suboptimal decision-making. In this work, we specifically focus on two commonly observed biases: projection bias and the reference-point effect. To address these biases, we propose a novel bias-embedded preference model called Probe. The Probe incorporates a weight function to capture users' projection bias and a value function to account for the reference-point effect, and introduce prospect theory from behavioral economics to combine the weight and value functions. This allows us to determine the probability of users selecting the bundle or a single item. We provide a thorough theoretical analysis to demonstrate the impact of projection bias on the design of bundle sales strategies. Through experimental results, we show that the proposed Probe model outperforms existing methods and contributes to a better understanding of users' irrational behaviors in bundle purchases. This investigation can facilitate a deeper comprehension of users' decision-making mechanisms, enable the provision of personalized services, and assist users in making more rational and optimal decisions

Estimating Error and Bias of Offline Recommender System Evaluation Results

Recommender systems are software applications deployed on the Internet to help people find useful items (e.g. movies, books, music, products) by providing recommendation lists. Before deploying recommender systems online, researchers and practitioners generally conduct offline evaluations to compare the accuracy of top- recommendation lists among candidate algorithms using users’ history consumption data. These offline evaluations typically use metrics and methodologies borrowed from machine learning and information retrieval and have several well-known biases that affect the validity of their results, including popularity bias and other biases arising from the missing-not-at-random nature of the data used. The existence of these biases is well-established, but their extent and impact are not as well-studied. In this work, we employ controlled simulations with varying assumptions about the distribution and structure of users’ preferences and the rating process to estimate the distributions of the errors in recommender experiment outcomes as a result of these biases. We calibrate our simulated datasets to mimic key statistics of existing public datasets in different domains and use the simulated data to assess the error in estimating true accuracy with observable rating data. We find inconsistency of the evaluation metric scores and the order in which they rank recommendation algorithms in the synthetic true preference and the observation dataset. Simulation results show that offline evaluations are sometimes fooled by intrinsic effects in the data generation process into mistakenly ranking algorithms. The extent of this effect is sensitive to assumptions

Deep Learning Techniques on Recommender Systems

Ένα σύστημα συστάσεων είναι ένα εργαλείο που φιλτράρει πληροφορίες και προτείνει στους χρήστες περιεχόμενο που σχετίζεται με τα ενδιαφέροντά τους. Έχει παρατηρηθεί αύξηση στην χρήση των συστημάτων συστάσεων τα τελευταία χρόνια ως αποτέλεσμα της αυξανόμενης χρήσης του διαδικτύου η οποία παρέχει στους ερευνητές τεράστιες πο­ σότητες δεδομένων για τους χρήστες. Ο σκοπός αυτής της πτυχιακής εργασίας είναι να μελετήσει τις διάφορες τεχνικές που εφαρμόζονται στα συστήματα συστάσεων καθώς και τα μοντέλα βαθιάς μάθησης που χρησιμοποιούνται για την ενίσχυση αυτών των συστη­ μάτων. Επιπλέον, οι μέθοδοι αξιολόγησης των συστημάτων συστάσεων περιγράφονται μαζί με τις προκλήσεις που αυτά αντιμετωπίζουν. Στην συνέχεια αυτής της μελέτης, περι­ γράφεται η υλοποίηση ενός συστήματος συστάσεων για βιντεοπαιχνίδια που χρησιμοποιεί αλγόριθμους βαθιάς μάθησης, ακολουθούμενη από την ερμηνεία των αποτελεσμάτων της. Στο τέλος παρουσιάζονται μερικά προβλήματα και προτάσεις που σχετίζονται με το μέλλον του χώρου των συστάσεων.A recommender system is a tool that filters information and suggests content to users which is relevant to their interests. Recommender systems have seen a rise in their use in the recent years as a result of the increasing internet use which provides researchers with huge amounts of user data. The purpose of this thesis is to study the various techniques that are applied to recommender systems as well as the deep learning models that are used to enhance those systems. Moreover, the evaluation methods of the recommender systems are described along with the challenges they face. Followingly, an implementa­ tion of a recommender system for video games which employs deep learning algorithms is provided followed by the interpretation of the results. At the end, some concerns and suggestions about the future in the field of recommendations are mentioned

Correlation-sensitive next-basket recommendation

Ministry of Education, Singapore under its Academic Research Funding Tier

Sistema de recomendação de videojogos

Esta dissertação, foca-se no estudo e comparação do desempenho de algoritmos de recomendação baseados em filtragem colaborativa, com o objetivo de propor um sistema de recomendação de videojogos. Esse sistema utiliza informações provenientes da plataforma Steam, que podem ser descritos como dados implícitos, e que posteriormente foram transformados em classificações explícitas para serem usadas nos algoritmos. Os algoritmos foram implementados com recurso à biblioteca Surprise, que permite criar e avaliar sistemas de recomendação baseados em dados explícitos. O trabalho foca-se em abordagens computacionalmente menos exigentes, demostrando que as mesmas podem obter bons resultados. Os algoritmos são avaliados e comparados entre si usando métricas como RSME, MAE, Precision@k, Recall@k e [email protected] dissertation focuses on the study and compare of the performance of collaborative filtering algorithms, with the intent of proposing a videogame-oriented recommendation system. This system uses information from the video game platform “Steam”, which can be described as implicit feedback, and that were later transformed into explicit feedback. These algorithms were implemented using Python’s Surprise library, that allows to create and evaluate recommender systems that deal with explicit data. The work focuses on computationally fewer demanding approaches, demonstrating that they can obtain good results. The algorithms are evaluated and compared with each other using metrics such as RSME, MAE, Precision@k, Recall@k and F1@k

Regulating competition in the digital network industry: A proposal for progressive ecosystem regulation

The digital sector is a cornerstone of the modern economy, and regulating digital enterprises can be considered the new frontier for regulators and competition authorities. To capture and address the competitive dynamics of digital markets we need to rethink our (competition) laws and regulatory strategies. The thesis develops new approaches to regulating digital markets by viewing them as part of a network industry. By combining insights from our experiences with existing regulation in telecommunications with insights from economics literature and management theory, the thesis concludes by proposing a new regulatory framework called ‘progressive ecosystem regulation’. The thesis is divided in three parts and has three key findings or contributions. The first part explains why digital platforms such as Google’s search engine, Meta’s social media platforms and Amazon’s Marketplace are prone to monopolization. Here, the thesis develops a theory of ‘digital natural monopoly’, which explains why competition in digital platform markets is likely to lead to concentration by its very nature.The second part of the thesis puts forward that competition in digital markets persists, even if there is monopoly in a market. Here, the thesis develops a conceptual framework for competition between digital ecosystems, which consists of group of actors and products. Digital enterprises compete to carve out a part of the digital network industry where they can exert control, and their strong position in a platform market can be used offensively or defensively to steer competition between ecosystems. The thesis then sets out four phases of ecosystem competition, which helps to explain when competition in the digital network industry is healthy and when it is likely to become problematic.The third and final part of the thesis brings together these findings and draws lessons from our experiences of regulating the network industry for telecommunications. Based on the insights developed in the thesis it puts forward a proposal for ‘progressive ecosystem regulation’. The purpose of this regulation is to protect and empower entrants from large digital ecosystems so that they can develop new products and innovate disruptively. This regulatory framework would create three regulatory pools: a heavily regulated, lightly regulated and entrant pool. The layered regulatory framework allows regulators to adjust who receives protection under the regulation and who faces the burdens relatively quickly, so that the regulatory framework reflects the fast pace of innovation and changing nature of digital markets. With this proposal, the thesis challenges and enriches our existing notions on regulation and specifically how we should regulate digital markets

Regulating competition in the digital network industry: A proposal for progressive ecosystem regulation

The digital sector is a cornerstone of the modern economy, and regulating digital enterprises can be considered the new frontier for regulators and competition authorities. To capture and address the competitive dynamics of digital markets we need to rethink our (competition) laws and regulatory strategies. The thesis develops new approaches to regulating digital markets by viewing them as part of a network industry. By combining insights from our experiences with existing regulation in telecommunications with insights from economics literature and management theory, the thesis concludes by proposing a new regulatory framework called ‘progressive ecosystem regulation’. The thesis is divided in three parts and has three key findings or contributions. The first part explains why digital platforms such as Google’s search engine, Meta’s social media platforms and Amazon’s Marketplace are prone to monopolization. Here, the thesis develops a theory of ‘digital natural monopoly’, which explains why competition in digital platform markets is likely to lead to concentration by its very nature.The second part of the thesis puts forward that competition in digital markets persists, even if there is monopoly in a market. Here, the thesis develops a conceptual framework for competition between digital ecosystems, which consists of group of actors and products. Digital enterprises compete to carve out a part of the digital network industry where they can exert control, and their strong position in a platform market can be used offensively or defensively to steer competition between ecosystems. The thesis then sets out four phases of ecosystem competition, which helps to explain when competition in the digital network industry is healthy and when it is likely to become problematic.The third and final part of the thesis brings together these findings and draws lessons from our experiences of regulating the network industry for telecommunications. Based on the insights developed in the thesis it puts forward a proposal for ‘progressive ecosystem regulation’. The purpose of this regulation is to protect and empower entrants from large digital ecosystems so that they can develop new products and innovate disruptively. This regulatory framework would create three regulatory pools: a heavily regulated, lightly regulated and entrant pool. The layered regulatory framework allows regulators to adjust who receives protection under the regulation and who faces the burdens relatively quickly, so that the regulatory framework reflects the fast pace of innovation and changing nature of digital markets. With this proposal, the thesis challenges and enriches our existing notions on regulation and specifically how we should regulate digital markets

18th Annual Symposium of the School of Science, Engineering and Health

Message from the Dean We in the School of Science, Engineering and Health welcome you to this 18th Annual Symposium, and our first as Messiah University. Here you will see our students, faculty and staff showcase innovation, creativity, teamwork and professionalism in our academic departments. Basic and applied research in science and health fields stem from curiosity, acquired skill, and a desire to test and improve processes from foundational principles. The outcomes of scientific research expand intellectual understanding and have tremendous impact on quality of life, environmental health, and human flourishing. We miss having you as guests on our campus but warmly welcome you to enjoy this day virtually. Angela Hare Dean School of Science, Engineering and Health, Messiah Universit