A Package-to-Group Recommendation Framework

Recommender systems are important information filtering techniques that retrieve interesting and personalized items for users based on their profiles and past activities. The goal of most recommender systems is to identify a ranked list of items that are likely to be of interest to users. However, there are several applications such as trip planning, where the items to be selected are not intended for single users but for a group of users, and where the group members are interested in package recommendations as collections of items. Recent research on recommender systems has generalized recommendations to suggest packages of items to single users (Package recommendations), and single items to groups of users (Group recommendations). However, the package-to-group recommendation task has not gained much attention. In this paper, we focus on the task of recommending packages of items to groups of users. This is a task with several real life scenarios, such as recommending a set of Points of Interest packages to tourist groups. We formally define the problem of top-k package-to-group recommendations and propose two models for estimating the preference of a group for a package, incorporating features such as package constraint, user impact and package viability. We design ranking algorithms for finding the top-k package-to-group recommendations and we compare our proposed models with baseline approaches stemming from related works. The experimental evaluation of our proposals, using the Yelp dataset demonstrates that our models find packages of high quality considering important features of package-to-group recommendations.