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 engagement 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, and 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 (CF) models. This research underscores the significance of including group and package information while learning latent representations of users and items for personalized recommendations. These components 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, namely Regularized Matrix Factorization and Maximum Margin Matrix factorization, as the baseline models and demonstrate their customization to various recommendation tasks. Experimental results on two publicly available datasets are reported, comparing them to other baseline approaches for various recommendation tasks.

翻译：推荐系统旨在通过为各类产品和服务提供定制化推荐来提升整体用户体验。这些系统帮助用户做出更明智的决策，从而提高用户与平台的互动。然而，推荐系统的实现高度依赖于具体情境，可能涉及向用户或群体推荐单个物品或物品包。由于缺乏能够处理不同层级推荐任务的全面统一方法，在实际部署过程中需要仔细探索多种模型。此外，这些独立模型必须紧密适配其生成推荐的情境，以避免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，可同时处理个性化推荐、群体推荐、包推荐及包到组推荐这四类任务，弥补了当前研究领域的空白。该框架可与大多数基于传统矩阵分解的协同过滤（CF）模型集成。本研究强调了在为用户和物品学习潜在表示时融入群体及包信息对提升个性化推荐的重要性。这些组件通过强制用户/物品表示与其对应的群体/包表示紧密对齐，从而挖掘更丰富的潜在表示。我们以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，展示了其在不同推荐任务中的定制化实现。实验基于两个公开数据集，报告了与其他基线方法在各推荐任务上的对比结果。