Recommending Packages of Multi-Criteria Items to Groups
Edgar Ceh-Varela, Huiping Cao · 2019
Most recommender services help individual users by recommending items within a single category based on the items' overall ratings. However, this may not be sufficient for group activities. For example, a group of friends using an online travel website look for a weekend getaway package (with hotel and restaurant), where the group members have different preferences over the characteristics (e.g., price, service, ambient) of these items. We call items with multiple characteristics as multi-criteria items. The items may come from different categories (e.g., hotel, restaurant). This paper proposes a novel problem of recommending packages of multi-criteria items to a group of users by leveraging users' preferences over categories. As far as we know, our work is the first paper studying this problem. We propose two models to measure the preference of a group to a package. The first model utilizes users' preferences for all the items and all categories, while the second model further leverages the influence of different group members to user preferences. We further introduce a new metric, to measure the fairness of the recommendations to different group members. We present an approach that utilizes co-clustering to incorporate items' characteristics in the calculation of user preferences and creating recommendations. Finally, we conduct extensive experiments with three real datasets. The experiments show that the second model can find packages that balance better the preferences of all the group members.