Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS’21)

Recommender systems were originally developed as interactive intelligent systems that can proactively guide users to items that match their preferences. Despite its origin on the crossroads of HCI and AI, the majority of research on recommender systems gradually focused on objective accuracy criteria paying less and less attention to how users interact with the system as well as the efficacy of interface designs from users’ perspectives. This trend is reversing with the increased volume of research that looks beyond algorithms, into users’ interactions, decision making processes, and overall experience. The series of workshops on Interfaces and Human Decision Making for Recommender Systems focuses on the "human side" of recommender systems. The goal of the research stream featured at the workshop is to improve users’ overall experience with recommender systems by integrating different theories of human decision making into the construction of recommender systems and exploring better interfaces for recommender systems. In this summary,we introduce the JointWorkshop on Interfaces and Human Decision Making for Recommender Systems at RecSys’21, review its history, and discuss most important topics considered at the workshop

From Rankings to Ratings: Rank Scoring Via Active Learning

In this paper we present RaScAL, an active learning approach to predicting real-valued scores for items given access to an oracle and knowledge of the overall item-ranking. In an experiment on six different datasets, we find that RaScAL consistently outperforms the state-of-the-art. The RaScAL algorithm represents one step within a proposed overall system of preference elicitations of scores via pairwise comparisons

Multi-agent blackboard architecture for supporting legal decision making

Our research objective is to design a system to support legal decision-making using the multi-agent blackboard architecture. Agents represent experts that may apply various knowledge processing algorithms and knowledge sources. Experts cooperate with each other using blackboard to store facts about current case. Knowledge is represented as a set of rules. Inference process is based on bottom-up control (forward chaining). The goal of our system is to find rationales for arguments supporting different decisions for a given case using precedents and statutory knowledge. Our system also uses top-down knowledge from statutes and precedents to interactively query the user for additional facts, when such facts could affect the judgment. The rationales for various judgments are presented to the user, who may choose the most appropriate one. We present two example scenarios in Polish traffic law to illustrate the features of our system. Based on these results, we argue that the blackboard architecture provides an effecive approach to model situations where a multitude of possibly conflicting factors must be taken into account in decision making. We briefly discuss two such scenarios: incorporating moral and ethical factors in decision making by autonomous systems (e.g. self-driven cars), and integrating eudaimonic (well-being) factors in modeling mobility patterns in a smart city

A genetic‐based pairwise trip planner recommender system

The massive growth of internet users nowadays can be a big opportunity for the busi- nesses to promote their services. This opportunity is not only for e-commerce, but also for other e-services, such as e-tourism. In this paper, we propose an approach of personalized recommender system with pairwise preference elicitation for the e-tourism domain area. We used a combination of Genetic Agorithm with pairwise user prefer- ence elicitation approach. The advantages of pairwise preference elicitation method, as opposed to the pointwise method, have been shown in many studies, including to reduce incosistency and confusion of a rating number. We also performed a user evaluation study by inviting 24 participants to examine the proposed system and publish the POIs dataset which contains 201 attractions used in this study

Annual Report 2019-2020

LETTER FROM THE DEAN As I write this letter wrapping up the 2019-20 academic year, we remain in a global pandemic that has profoundly altered our lives. While many things have changed, some stayed the same: our CDM community worked hard, showed up for one another, and continued to advance their respective fields. A year that began like many others changed swiftly on March 11th when the University announced that spring classes would run remotely. By March 28th, the first day of spring quarter, we had moved 500 CDM courses online thanks to the diligent work of our faculty, staff, and instructional designers. But CDM’s work went beyond the (virtual) classroom. We mobilized our makerspaces to assist in the production of personal protective equipment for Illinois healthcare workers, participated in COVID-19 research initiatives, and were inspired by the innovative ways our student groups learned to network. You can read more about our response to the COVID-19 pandemic on pgs. 17-19. Throughout the year, our students were nationally recognized for their skills and creative work while our faculty were published dozens of times and screened their films at prestigious film festivals. We added a new undergraduate Industrial Design program, opened a second makerspace on the Lincoln Park Campus, and created new opportunities for Chicago youth. I am pleased to share with you the College of Computing and Digital Media’s (CDM) 2019-20 annual report, highlighting our collective accomplishments. David MillerDeanhttps://via.library.depaul.edu/cdmannual/1003/thumbnail.jp

A scalable recommender system : using latent topics and alternating least squares techniques

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsA recommender system is one of the major techniques that handles information overload problem of Information Retrieval. Improves access and proactively recommends relevant information to each user, based on preferences and objectives. During the implementation and planning phases, designers have to cope with several issues and challenges that need proper attention. This thesis aims to show the issues and challenges in developing high-quality recommender systems. A paper solves a current research problem in the field of job recommendations using a distributed algorithmic framework built on top of Spark for parallel computation which allows the algorithm to scale linearly with the growing number of users. The final solution consists of two different recommenders which could be utilised for different purposes. The first method is mainly driven by latent topics among users, meanwhile the second technique utilises a latent factor algorithm that directly addresses the preference-confidence paradigm

Plataforma web para modelar comportamiento server-side en augmentaciones

La aumentación web es un conjunto de técnicas que permiten a los usuarios definir y ejecutar software que depende de la capa de presentación de una página web concreta. De esta manera, a través del uso de artefactos de aumentación web, los usuarios finales pueden satisfacer varios tipos de requisitos que no fueron considerados por los analistas, desarrolladores y otras partes interesadas que construyeron la aplicación. Aunque hay algunos enfoques de aumentación que contemplan una contraparte de servidor (para soportar aspectos tales como colaboración, gestión de sesión de explorador cruzado, etc.), los artefactos de aumentación suelen ser puramente del lado del cliente. Este soporte del lado del servidor mejora las capacidades de las aumentaciones, ya que puede permitir compartir información entre usuarios e incluso entre las mismas aplicaciones. Hasta ahora, este apoyo se define a menudo y se desarrolla de una manera ad-hoc. Aunque está claro que el soporte del servidor aporta nuevas posibilidades, también es cierto que el desarrollo y despliegue de aplicaciones web del lado del servidor es una tarea compleja que los usuarios finales difícilmente pueden manejar. Este trabajo presenta una herramienta CASE Web en fácil aprendizaje y uso, reemplazando las actuales herramientas desktop que se utilizan en estos casos para desarrollar el comportamiento del lado del servidor mediante el modelado conceptual y navegacional, brindando los elementos para el desarrollo de la interfaz de usuario y la persistencia de los modelos.Facultad de Informátic

Aumentación de sitios web combinando enfoques MDWE y técnicas de separación de concerns

La aumentación web es un conjunto de técnicas que permiten a los usuarios definir y ejecutar software que depende de la capa de presentación de una página web concreta. De esta manera, a través del uso de artefactos de aumentación web, los usuarios finales pueden satisfacer varios tipos de requisitos que no fueron considerados por los analistas, desarrolladores e interesados que construyeron la aplicación. Aunque hay algunos enfoques de aumento que contemplan una contraparte en el servidor (para soportar aspectos tales como colaboración, gestión de sesión de explorador cruzado, etc.), los artefactos aumentación suelen ser puramente del lado del cliente. Este soporte del lado del servidor aumenta las capacidades de las ampliaciones, ya que puede permitir compartir información entre usuarios y dispositivos. Este trabajo presenta un nuevo enfoque para el diseño de aplicaciones de aumentaciones web basado en el lado del cliente y componentes del lado del servidor. Se propone un enfoque basado en el modelo que eleva el nivel de abstracción para el desarrollo del servidor. El enfoque utiliza principios avanzados de separación de conceptos, por lo que se proporcionan un conjunto de herramientas para diseñar la composición de la aplicación del núcleo y el aumento. Las ideas y enfoque se ilustran con varios ejemplos corrientes que muestran el potencial del enfoque.Facultad de Informátic