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There are roughly three stages in real industrial recommendation systems,candidates generation (retrieval), ranking and reranking. Individual-leveldiversity and system-level diversity are both important for industrialrecommender systems. The former focus on each single user's experience, whilethe latter focus on the difference among users. Graph-based retrievalstrategies are inevitably hijacked by heavy users and popular items, leading tothe convergence of candidates for users and the lack of system-level diversity.Meanwhile, in the reranking phase, Determinantal Point Process (DPP) isdeployed to increase individual-level diverisity. Heavily relying on thesemantic information of items, DPP suffers from clickbait and inaccurateattributes. Besides, most studies only focus on one of the two levels ofdiversity, and ignore the mutual influence among different stages in realrecommender systems. We argue that individual-level diversity and system-leveldiversity should be viewed as an integrated problem, and we provide anefficient and deployable solution for web-scale recommenders. Generally, wepropose to employ the retrieval graph information in diversity-based reranking,by which to weaken the hidden similarity of items exposed to users, andconsequently gain more graph explorations to improve the system-leveldiveristy. Besides, we argue that users' propensity for diversity changes overtime in content feed recommendation. Therefore, with the explored graph, wealso propose to capture the user's real-time personalized propensity to thediversity. We implement and deploy the combined system in WeChat App's TopStories used by hundreds of millions of users. Offline simulations and onlineA/B tests show our solution can effectively improve both user engagement andsystem revenue.

Graph Exploration Matters: Improving both individual-level and system-level diversity in WeChat Feed Recommender