Preferences Of Individual Group Members Research Articles

An enhanced hybridized approach for group recommendation via reliable ratings

A group recommender system aim's to provide relevant information to all members of the group. To determine group preferences, the majority of existing studies use aggregation approaches. An aggregation method is a strategy for recommending products to a group by combining the individual preferences of group members. So far, a slew of different types of aggregation algorithms has been discovered. However, they only aggregate one component of the offered ratings (e.g., counts, rankings, high averages), which limits their ability to capture group members' proclivities. This study proposes a novel aggregation method called weighted count that aggregates ratings by providing weights equal to the number of users who provide ratings to an item (location). In addition, the study proposes combining additive utilitarian and weighted count approaches to highlight popular locations on which group members agreed. Experiments on a benchmark check-in dataset demonstrated that the proposed blended technique surpasses the existing methods significantly.

Predicting personalized grouping and consumption: A collaborative evolution model

With the prevalence of online social groups, the dynamic joint prediction of users’ grouping and consumption behaviors on social network platforms is critical for optimizing social link suggestions and product recommendations. The group influence theory indicates that group norms affect user preference and behavior; however, the individual preferences of group members can also alter group norms. Nevertheless, the problems of how to holistically model dynamic bidirectional influence, existing between individual preferences and group norms, and then, simultaneously predict the users’ grouping and consumption behaviors are still underexplored. In this study, we propose a collaborative evolution and prediction (CEP) model to address the above issues. We associate each social group with a latent group norm vector, and assign each user with a latent individual preference vector. The unobservable interplay between individual preferences and group norms is then modeled according to the underlying group influence theory. Based on these two latent vectors, we design a joint optimization function that incorporates the correlation between grouping and consumption behaviors, to enhance the prediction performance. Through extensive experiments and evolution analysis, we demonstrate the prediction effectiveness and the explanatory power of our CEP model.

An enhanced aggregation method considering deviations for a group recommendation

The goal of a group recommendation involves providing appropriate information for all members in a group. Most extant studies use aggregation methods to determine group preferences. An aggregation method is an approach that aggregates individual preferences of group members to recommend items to a group. Previous studies on aggregation methods only consider high averages, counts, and rankings to provide recommendations. However, the most important component of a group recommendation involves ensuring that majority of the members in a group are satisfied with the recommended results. Therefore, it is necessary to consider the deviation as an important element in aggregation methods. The present study involves proposing an upward leveling (UL) aggregation method that considers deviations for group recommendations. The UL recommends items with low deviations and high averages in conjunction with frequency of positive rating counts for group members. Furthermore, the effectiveness of the UL is validated to perform a comparative evaluation with existing aggregation methods by using the normalized discounted cumulative gain (NDCG) and diversity. The results indicate that the UL outperforms all the baselines and that the deviation plays an important role in the aggregation method.