On Practical Diversified Recommendation with Controllable Category Diversity Framework
CoRR(2024)
摘要
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.
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