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Recommender systems aim to enhance the overall user experience by providing
tailored recommendations for a variety of products and services. These systems
help users make more informed decisions, leading to greater user satisfaction
with the platform. However, the implementation of these systems largely depends
on the context, which can vary from recommending an item or package to a user
or a group. This requires careful exploration of several models during the
deployment, as there is no comprehensive and unified approach that deals with
recommendations at different levels. Furthermore, these individual models must
be closely attuned to their generated recommendations depending on the context
to prevent significant variation in their generated recommendations. In this
paper, we propose a novel unified recommendation framework that addresses all
four recommendation tasks, namely personalized, group, package, or
package-to-group recommendation, filling the gap in the current research
landscape. The proposed framework can be integrated with most of the
traditional matrix factorization-based collaborative filtering models. The idea
is to enhance the formulation of the existing approaches by incorporating
components focusing on the exploitation of the group and package latent
factors. These components also help in exploiting a rich latent representation
of the user/item by enforcing them to align closely with their corresponding
group/package representation. We consider two prominent CF techniques,
Regularized Matrix Factorization and Maximum Margin Matrix factorization, as
the baseline models and demonstrate their customization to various
recommendation tasks. Experiment results on two publicly available datasets are
reported, comparing them to other baseline approaches that consider individual
rating feedback for group or package recommendations.

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