Thuy Ngoc Nguyen

Observing Group Decision Making Processes

Most research on group recommender systems relies on the assumption that individuals have conflicting preferences; in order to generate group recommendations the system should identify a fair way of aggregating these preferences. Both empirical studies and theoretical frameworks have tried to identify the most effective preference aggregation techniques without coming to definite conclusions. In this paper, we propose to approach group recommendation from the group dynamics perspective and analyze the group decision making process for a particular task (in the travel domain). We observe several individual and group properties and correlate them to choice satisfaction. Supported by these initial results we therefore advocate for the development of new group recommendation techniques that consider group dynamics and support the full group decision making process.

A Chat-Based Group Recommender System for Tourism

Group recommender systems aim at supporting a group of users in making decisions when considering a set of alternatives. State of the art solutions aggregate individual preferences acquired before the actual decision making process and suggest items that fit the aggregated model. In this paper, we illustrate a different approach, which is implemented in a system that records and uses the users’ preferences expressed while the group discusses options. The system monitors users’ interactions and offers appropriate directions and recommendations. The system runs on a smartphone and acts as a facilitator to guide and help the group members in coming up with an agreement and a final decision. In order to measure the effectiveness of the proposed technologies we have focussed on usability and perceived recommendation quality. In a controlled live user study, we have measured a high usability score, good user-perceived recommendation quality and choice satisfaction.

An observational user study for group recommender systems in the tourism domain

In this article we argue and give evidence that the research on group recommender systems must look more carefully at the dynamics of group decision-making in order to produce technologies that will be truly beneficial for groups. We illustrate the adopted research method and the results of a user study aimed at observing and measuring the evolution of user preferences and interaction in a tourism decision-making task: finding a destination to visit together as a group. We discuss the benefits and caveats of such an observational study method and we present the implications that the derived data and findings have on the design of interactive group recommender systems.

Algorithms Aside: Recommendation As The Lens Of Life

In this position paper, we take the experimental approach of putting algorithms aside, and reflect on what recommenders would be for people if they were not tied to technology. By looking at some of the shortcomings that current recommenders have fallen into and discussing their limitations from a human point of view, we ask the question: if freed from all limitations, what should, and what could, RecSys be? We then turn to the idea that life itself is the best recommender system, and that people themselves are the query. By looking at how life brings people in contact with options that suit their needs or match their preferences, we hope to shed further light on what current RecSys could be doing better. Finally, we look at the forms that RecSys could take in the future. By formulating our vision beyond the reach of usual considerations and current limitations, including business models, algorithms, data sets, and evaluation methodologies, we attempt to arrive at fresh conclusions that may inspire the next steps taken by the community of researchers working on RecSys.