To Share or Not to Share: Understanding and Modeling Individual Disclosure Preferences in Recommender Systems for the Workplace

Newly-formed teams often encounter the challenge of members coming together to collaborate on a project without prior knowledge of each other’s working and communication styles. This lack of familiarity can lead to conflicts and misunderstandings, hindering effective teamwork. Derived from research in social recommender systems, team...

User Modeling for Privacy-preserving Explanations in Group Recommendations

My thesis investigates what makes good explanations for group recommendations, considering the privacy concerns of group members. Let’s give an example. Have you ever been to lunch with other colleagues on a business trip? Do you recall how long it took you to pick a restaurant? In these situations, recommender systems could help people decide,...

Evaluating explainable social choice-based aggregation strategies for group recommendation

Social choice aggregation strategies have been proposed as an explainable way to generate recommendations to groups of users. However, it is not trivial to determine the best strategy to apply for a specific group. Previous work highlighted that the performance of a group recommender system is affected by the internal diversity of the group...

Exploring User Concerns about Disclosing Location and Emotion Information in Group Recommendations

Recent research has shown that explanations serve as an important means to increase transparency in group recommendations while also increasing users' privacy concerns. However, it is currently unclear what personal and contextual factors affect users' privacy concerns about various types of personal information. This paper studies the effect...

Factors influencing privacy concern for explanations of group recommendation

Explanations can help users to better understand why items have been recommended. Additionally, explanations for group recommender systems need to consider further goals than single-user recommender systems. For example, we need to balance group members' need for privacy with their need for transparency, since a transparent explanation might...

Generating Natural Language Explanations for Group Recommendations in High Divergence Scenarios

In some scenarios, like music or tourism, people often consume items in groups. However, reaching a consensus is difficult as different members of the group may have highly diverging tastes. To keep the rest of the group satisfied, an individual might need to be confronted occasionally with items they do not like. In this context, presenting an...

Someone really wanted that song but it was not me!: Evaluating Which Information to Disclose in Explanations for Group Recommendations

Explanations can be used to supply transparency in recommender systems (RSs). However, when presenting a shared explanation to a group, we need to balance users' need for privacy with their need for transparency. This is particularly challenging when group members have highly diverging tastes and individuals are confronted with items they do not...

You Do Not Decide for Me!: Evaluating Explainable Group Aggregation Strategies for Tourism

Most recommender systems propose items to individual users. However, in domains such as tourism, people often consume items in groups rather than individually. Different individual preferences in such a group can be difficult to resolve, and often compromises need to be made. Social choice strategies can be used to aggregate the preferences of...

Ensuring Fairness in Group Recommendations by Rank-Sensitive Balancing of Relevance

For group recommendations, one objective is to recommend an ordered set of items, a top-N , to a group such that each individual recommendation is relevant for everyone. A common way to do this is to select items on which the group can agree, using so-called ‘aggregation strategies’. One weakness of these aggregation strategies is that they...

Evaluating Explanations for different Relationship Strengths

People like to travel in groups to visit places. Group recommendation systems can be used to recommend an itinerary of "places of interests" (POIs) in an ordered sequence. The order of POIs in the sequence can be explained to group members to increase acceptance of the recommended items. There is a possibility that explanations which reveal...

Towards creating a non-synthetic group recommendation dataset

Recommender systems can be useful in group settings, e.g. when choosing a movie to watch with a group. However, while considerable research in group recommendation has been performed, we still lack truly ecological datasets on group recommendations in real life consumption scenarios. Much of the existing work considers hypothetical...

TourExplain: A Crowdsourcing Pipeline for Generating Explanations for Groups of Tourists

When a group is traveling together it is challenging to recommendan itinerary consisting of several points of interest (POIs). Thepreferences of individual group members often diverge, but it isimportant to keep everyone in the group satisfied during the entiretrip. We propose a method to consider the preferences of all thepeople in the group....

Generating Consensus Explanations for Group Recommendations: An exploratory study

In some scenarios, like music, people often consume items in groups. However, reaching a consensus is difficult, and often compromises need to be made. Such compromises can potentially help users expand their tastes. They can also lead to outright rejection of the recommended items. One way to avoid this is to explain recommendations that are...

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...