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.

翻译：推荐系统旨在通过为各种产品和服务提供量身定制的推荐来增强整体用户体验。这些系统帮助用户做出更明智的决策，从而提高用户对平台的满意度。然而，这些系统的实现很大程度上依赖于具体上下文，推荐场景可能涉及向单个用户或群体推荐单个物品或套餐。由于目前缺乏能够处理不同层级推荐任务的全面统一方法，在部署过程中需要仔细探索多种模型。此外，这些独立模型必须根据上下文与其生成的推荐结果紧密协调，以防止推荐结果出现显著差异。本文提出了一种新颖的统一推荐框架，该框架能够处理个性化推荐、群体推荐、套餐推荐及套餐到群体推荐这四类推荐任务，填补了当前研究领域的空白。所提出的框架可与大多数基于传统矩阵分解的协同过滤模型集成。其核心思想是通过引入专注于利用群体和套餐潜在因子的组件来增强现有方法的公式化表达。这些组件还通过强制用户/物品的表示与其对应的群体/套餐表示紧密对齐，帮助挖掘用户/物品的丰富潜在表征。我们选取两种主流的协同过滤技术——正则化矩阵分解和最大间隔矩阵分解作为基线模型，并展示了它们向各类推荐任务的自定义扩展。本文报告了在两个公开数据集上的实验结果，并与考虑针对群体或套餐推荐的个体评分反馈的其他基线方法进行了比较。