Recommender systems have made significant strides in various industries, primarily driven by extensive efforts to enhance recommendation accuracy. However, this pursuit of accuracy has inadvertently given rise to echo chamber/filter bubble effects. Especially in industry, it could impair user's experiences and prevent user from accessing a wider range of items. One of the solutions is to take diversity into account. However, most of existing works focus on user's explicit preferences, while rarely exploring user's non-interaction preferences. These neglected non-interaction preferences are especially important for broadening user's interests in alleviating echo chamber/filter bubble effects.Therefore, in this paper, we first define diversity as two distinct definitions, i.e., user-explicit diversity (U-diversity) and user-item non-interaction diversity (N-diversity) based on user historical behaviors. Then, we propose a succinct and effective method, named as Controllable Category Diversity Framework (CCDF) to achieve both high U-diversity and N-diversity simultaneously.Specifically, CCDF consists of two stages, User-Category Matching and Constrained Item Matching. The User-Category Matching utilizes the DeepU2C model and a combined loss to capture user's preferences in categories, and then selects the top-$K$ categories with a controllable parameter $K$.These top-$K$ categories will be used as trigger information in Constrained Item Matching. Offline experimental results show that our proposed DeepU2C outperforms state-of-the-art diversity-oriented methods, especially on N-diversity task. The whole framework is validated in a real-world production environment by conducting online A/B testing.

翻译：推荐系统在多个行业取得了显著进展，这主要得益于提升推荐准确性的广泛努力。然而，这种对准确性的追求无意中导致了回音室/过滤气泡效应。尤其在工业应用中，这会损害用户体验，并阻碍用户接触更广泛的内容。其中一个解决方案是考虑多样性。然而，现有研究大多聚焦于用户的显式偏好，极少探索用户的非交互偏好。这些被忽视的非交互偏好对于拓宽用户兴趣、缓解回音室/过滤气泡效应至关重要。因此，本文首先基于用户历史行为将多样性定义为两种不同的概念，即用户显式多样性（U-diversity）和用户-项目非交互多样性（N-diversity）。随后，我们提出了一种简洁有效的方法，称为可控类别多样性框架（CCDF），以同时实现高U-diversity和高N-diversity。具体而言，CCDF包含两个阶段：用户-类别匹配和约束项目匹配。用户-类别匹配阶段利用DeepU2C模型和组合损失函数捕捉用户在类别上的偏好，并通过可控参数K选择前K个类别。这些前K个类别将作为约束项目匹配阶段的触发信息。离线实验结果表明，我们提出的DeepU2C方法在面向多样性的任务中优于现有最优方法，尤其在N-diversity任务上表现突出。该框架已通过在线A/B测试在实际生产环境中得到验证。