Learning-to-rank is a core technique in the top-N recommendation task, where an ideal ranker would be a mapping from an item set to an arrangement (a.k.a. permutation). Most existing solutions fall in the paradigm of probabilistic ranking principle (PRP), i.e., first score each item in the candidate set and then perform a sort operation to generate the top ranking list. However, these approaches neglect the contextual dependence among candidate items during individual scoring, and the sort operation is non-differentiable. To bypass the above issues, we propose Set-To-Arrangement Ranking (STARank), a new framework directly generates the permutations of the candidate items without the need for individually scoring and sort operations; and is end-to-end differentiable. As a result, STARank can operate when only the ground-truth permutations are accessible without requiring access to the ground-truth relevance scores for items. For this purpose, STARank first reads the candidate items in the context of the user browsing history, whose representations are fed into a Plackett-Luce module to arrange the given items into a list. To effectively utilize the given ground-truth permutations for supervising STARank, we leverage the internal consistency property of Plackett-Luce models to derive a computationally efficient list-wise loss. Experimental comparisons against 9 the state-of-the-art methods on 2 learning-to-rank benchmark datasets and 3 top-N real-world recommendation datasets demonstrate the superiority of STARank in terms of conventional ranking metrics. Notice that these ranking metrics do not consider the effects of the contextual dependence among the items in the list, we design a new family of simulation-based ranking metrics, where existing metrics can be regarded as special cases. STARank can consistently achieve better performance in terms of PBM and UBM simulation-based metrics.

As an emerging field driven by industrial applications, integrated re-ranking combines lists from upstream sources into a single list, and presents it to the user. The quality of integrated re-ranking is especially sensitive to real-time user behaviors and preferences. However, existing methods are all built on the cloud-to-edge framework, where mixed lists are generated by the cloud model and then sent to the devices. Despite its effectiveness, such a framework fails to capture users' real-time preferences due to the network bandwidth and latency. Hence, we propose to place the integrated re-ranking model on devices, allowing for the full exploitation of real-time behaviors. To achieve this, we need to address two key issues: first, how to extract users' preferences for different sources from heterogeneous and imbalanced user behaviors; second, how to explore the correlation between the extracted personalized preferences and the candidate items. In this work, we present the first on-Device Integrated Re-ranking framework, DIR, to avoid delays in processing real-time user behaviors. DIR includes a multi-sequence behavior modeling module to extract the user's source-level preferences, and a preference-adaptive re-ranking module to incorporate personalized source-level preferences into the re-ranking of candidate items. Besides, we design exposure loss and utility loss to jointly optimize exposure fairness and overall utility. Extensive experiments on three datasets show that DIR significantly outperforms the state-of-the-art baselines in utility-based and fairness-based metrics.

User-tag profiling is an effective way of mining user attributes in modern recommender systems. However, prior researches fail to extract users' precise preferences for tags in the items due to their incomplete feature-input patterns. To convert user-item interactions to user-tag preferences, we propose a novel feature-based framework named Coalition Tag Multi-View Mapping (CTMVM), which identifies and investigates two special features, Coalition Feature and Privileged Feature. The former indicates decisive tags in each click where relationships between tags in one item are treated as a coalition game. The latter represents highly informative features that only occur during training. For the coalition feature, we adopt Shapley Value based Empowerment (SVE) to model the tags in items with a game-theoretic paradigm and charge the network to straight master user preferences for essential tags. For the privileged feature, we present Privileged Knowledge Mapping (PKM) to explicitly distill privileged feature knowledge for each tag into one single embedding, which assists the model in predicting user-tag preferences at a more fine-grained level. However, the barren capacity of single embeddings limits the diverse relations between each tag and different privileged features. Therefore, we further propose Adaptive Multi-View Mapping (AMVM) model to enhance effect by handling multiple mapping networks. Excellent offline experiment results on two public and one private datasets show the out-standing performance of CTMVM. After the deployment on Alibaba large-scale recommendation systems, CTMVM achieved improvement by 10.81% and 6.74% in terms of Theme-CTR and Item-CTR respectively, which validates the effectiveness of taking in the two particular features for training.