Beyond Personalization: Research Directions in Multistakeholder Recommendation

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 has become apparent that the single-minded focus on the user common to academic research has obscured other important aspects of recommendation outcomes. Properties such as fairness, balance, profitability, and reciprocity are not captured by typical metrics for recommender system evaluation. The concept of multistakeholder recommendation has emerged as a unifying framework for describing and understanding recommendation settings where the end user is not the sole focus. This article describes the origins of multistakeholder recommendation, and the landscape of system designs. It provides illustrative examples of current research, as well as outlining open questions and research directions for the field.Comment: 64 page

Culture and disaster risk management - stakeholder attitudes during Stakeholder Assembly in Lisbon, Portugal

This report provides a summary of the topics discussed and the results of the third CARISMAND Stakeholder Assembly conducted in Lisbon, Portugal on 27-28 February 2018. In order to promote cross-sectional knowledge transfer and gather a variety of attitudes and perceptions, as in the first and second CARISMAND Stakeholder Assemblies held in Romania and Italy in the previous years, the audience consisted of a wide range of practitioners who are typically involved in disaster management, e.g., civil protection, the emergency services, paramedics, nurses, environmental protection, Red Cross, firefighters, military, and the police. Further, these practitioners were from several regions in Portugal, including the island of Madeira. The 40 participants were recruited via invitations sent to various Portuguese organisations and institutions, and via direct contacts of the Civil Protection Department in Lisbon which is one of the partners in the CARISMAND consortium. The event consisted of a mix of presentations and discussion groups to combine dissemination with information gathering (for the detailed schedule/programme see Appendix 1). Furthermore, this third Stakeholder Assembly was organised and specifically designed to discuss and collect feedback on a comprehensive set of recommendations for disaster practitioners, which will form one of the core elements of the CARISMAND Work Package 9 ‘Toolkit’. These recommendations, which have all been formulated on the basis of Work Packages 2-10 results, were structured in four, main “sets”: 1. Approaches to ethnicity in disaster management; 2. Culturally aware disaster-related training activities; 3. Cultural factors in disaster communication, with the sub-sets: a. Cultural values and emotions; (cross-)cultural symbols; “physical” aides and methods; b. Involvement of cultural leaders; involvement of specific groups; usage of social media and mobile phone apps; and 4. Improving trust, improving disaster management. In an initial general assembly, the event started with presentations of the CARISMAND project and its main goals and concepts, including the concept of culture adopted by CARISMAND, and the planned CARISMAND Toolkit architecture and functionalities. These were followed by a detailed presentation of the first of the above mentioned sets of recommendations for practitioners. Then, participants of the Stakeholder Assembly were split into small groups in separate breakout rooms, where they discussed and provided feedback to the presented recommendations. Over the course of the 2-day event, this procedure was followed for all four sets of recommendations. To follow the cyclical design of CARISMAND events, and wherever meaningful and possible, the respective Toolkit recommendations for practitioners provided also the basis for a respective “shadow” recommendation for citizens which will be discussed accordingly in the last round of CARISMAND Citizen Summits (Citizen Summit 5 in Lisbon, and Citizen Summit 6 in Utrecht) in 2018. The location of the Third Stakeholder Assembly was selected to make use of the extensive local professional network of the Civil Protection Department in Lisbon, but also due to Portugal being a traditional “melting pot” where, over more than a millennium, people from different cultural backgrounds and local/ethnical origins (in particular Africa, South America, and Europe) have lived both alongside and together. All documents related to the Working Groups, i.e. discussion guidelines and consent forms, were translated into Portuguese. Accordingly, all presentations, as well as the group discussions were held in Portuguese, aiming to avoid any language/education-related access restrictions, and allowing participating practitioners to respond intuitively and discuss freely in their native language. For this purpose, simultaneous interpreters and professional local moderators were contracted via a local market research agency (EquaçãoLógica), which also provided the basic data analysis of all Working Group discussions and an independent qualitative evaluation of all recommendations presented in the event. The results of this analysis and evaluation will demonstrate that most recommendations were seen by the participating practitioners to be relevant and useful. In particular, those recommendations related to the use of cultural symbols and the potential of mobile phone apps and/or social media were perceived as stimulating and thought-provoking. Some recommendations were felt to be less relevant in the specific Portuguese context, but accepted as useful in other locations; a very small number was perceived to be better addressed to policy makers rather than practitioners. These and all other suggestions for improvement of the presented CARISMAND Toolkit recommendations for practitioners have been taken up and will be outlined in the final chapter of this report.The project was co-funded by the European Commission within the Horizon2020 Programme (2014-2020).peer-reviewe

Recommender Systems

The ongoing rapid expansion of the Internet greatly increases the necessity of effective recommender systems for filtering the abundant information. Extensive research for recommender systems is conducted by a broad range of communities including social and computer scientists, physicists, and interdisciplinary researchers. Despite substantial theoretical and practical achievements, unification and comparison of different approaches are lacking, which impedes further advances. In this article, we review recent developments in recommender systems and discuss the major challenges. We compare and evaluate available algorithms and examine their roles in the future developments. In addition to algorithms, physical aspects are described to illustrate macroscopic behavior of recommender systems. Potential impacts and future directions are discussed. We emphasize that recommendation has a great scientific depth and combines diverse research fields which makes it of interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports

Toward a Robust Diversity-Based Model to Detect Changes of Context

Being able to automatically and quickly understand the user context during a session is a main issue for recommender systems. As a first step toward achieving that goal, we propose a model that observes in real time the diversity brought by each item relatively to a short sequence of consultations, corresponding to the recent user history. Our model has a complexity in constant time, and is generic since it can apply to any type of items within an online service (e.g. profiles, products, music tracks) and any application domain (e-commerce, social network, music streaming), as long as we have partial item descriptions. The observation of the diversity level over time allows us to detect implicit changes. In the long term, we plan to characterize the context, i.e. to find common features among a contiguous sub-sequence of items between two changes of context determined by our model. This will allow us to make context-aware and privacy-preserving recommendations, to explain them to users. As this is an ongoing research, the first step consists here in studying the robustness of our model while detecting changes of context. In order to do so, we use a music corpus of 100 users and more than 210,000 consultations (number of songs played in the global history). We validate the relevancy of our detections by finding connections between changes of context and events, such as ends of session. Of course, these events are a subset of the possible changes of context, since there might be several contexts within a session. We altered the quality of our corpus in several manners, so as to test the performances of our model when confronted with sparsity and different types of items. The results show that our model is robust and constitutes a promising approach.Comment: 27th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2015), Nov 2015, Vietri sul Mare, Ital

Understanding and Mitigating Multi-sided Exposure Bias in Recommender Systems

Fairness is a critical system-level objective in recommender systems that has been the subject of extensive recent research. It is especially important in multi-sided recommendation platforms where it may be crucial to optimize utilities not just for the end user, but also for other actors such as item sellers or producers who desire a fair representation of their items. Existing solutions do not properly address various aspects of multi-sided fairness in recommendations as they may either solely have one-sided view (i.e. improving the fairness only for one side), or do not appropriately measure the fairness for each actor involved in the system. In this thesis, I aim at first investigating the impact of unfair recommendations on the system and how these unfair recommendations can negatively affect major actors in the system. Then, I seek to propose solutions to tackle the unfairness of recommendations. I propose a rating transformation technique that works as a pre-processing step before building the recommendation model to alleviate the inherent popularity bias in the input data and consequently to mitigate the exposure unfairness for items and suppliers in the recommendation lists. Also, as another solution, I propose a general graph-based solution that works as a post-processing approach after recommendation generation for mitigating the multi-sided exposure bias in the recommendation results. For evaluation, I introduce several metrics for measuring the exposure fairness for items and suppliers, and show that these metrics better capture the fairness properties in the recommendation results. I perform extensive experiments to evaluate the effectiveness of the proposed solutions. The experiments on different publicly-available datasets and comparison with various baselines confirm the superiority of the proposed solutions in improving the exposure fairness for items and suppliers.Comment: Doctoral thesi