There are roughly three stages in real industrial recommendation systems,
candidates generation (retrieval), ranking and reranking. Individual-level
diversity and system-level diversity are both important for industrial
recommender systems. The former focus on each single user's experience, while
the latter focus on the difference among users. Graph-based retrieval
strategies are inevitably hijacked by heavy users and popular items, leading to
the convergence of candidates for users and the lack of system-level diversity.
Meanwhile, in the reranking phase, Determinantal Point Process (DPP) is
deployed to increase individual-level diverisity. Heavily relying on the
semantic information of items, DPP suffers from clickbait and inaccurate
attributes. Besides, most studies only focus on one of the two levels of
diversity, and ignore the mutual influence among different stages in real
recommender systems. We argue that individual-level diversity and system-level
diversity should be viewed as an integrated problem, and we provide an
efficient and deployable solution for web-scale recommenders. Generally, we
propose to employ the retrieval graph information in diversity-based reranking,
by which to weaken the hidden similarity of items exposed to users, and
consequently gain more graph explorations to improve the system-level
diveristy. Besides, we argue that users' propensity for diversity changes over
time in content feed recommendation. Therefore, with the explored graph, we
also propose to capture the user's real-time personalized propensity to the
diversity. We implement and deploy the combined system in WeChat App's Top
Stories used by hundreds of millions of users. Offline simulations and online
A/B tests show our solution can effectively improve both user engagement and
system revenue.

提出了一种解决工业级推荐系统中个人层面多样性和系统层面多样性的集成问题的方案，使用检索图信息来增加多样性，捕捉用户的实时多样性倾向，并在微信应用的 Top Stories 等应用中实现并部署，离线模拟和在线 AB 测试显示该方案可以有效提高用户参与度和系统收入。