Data sparsity has been a long-standing issue for accurate and trustworthy recommendation systems (RS). To alleviate the problem, many researchers pay much attention to cross-domain recommendation (CDR), which aims at transferring rich knowledge from related source domains to enhance the recommendation performance of sparse target domain. To reach the knowledge transferring purpose, recent CDR works always focus on designing different pairwise directed or undirected information transferring strategies between source and target domains. However, such pairwise transferring idea is difficult to adapt to multi-target CDR scenarios directly, e.g., transferring knowledge between multiple domains and improving their performance simultaneously, as such strategies may lead the following issues: (1) When the number of domains increases, the number of transferring modules will grow exponentially, which causes heavy computation complexity. (2) A single pairwise transferring module could only capture the relevant information of two domains, but ignores the correlated information of other domains, which may limit the transferring effectiveness. (3) When a sparse domain serves as the source domain during the pairwise transferring, it would easily leads the negative transfer problem, and the untrustworthy information may hurt the target domain recommendation performance. In this article, we consider the key challenge of the multi-target CDR task: How to identify the most valuable trustworthy information over multiple domains and transfer such information efficiently to avoid the negative transfer problem? To fulfill the above challenge, we propose a novel end-to-end model termed as DR-MTCDR , standing for D isentangled R epresentations learning for M ulti- T arget CDR . DR-MTCDR aims at transferring the trustworthy domain-shared information across domains, which has the two major advantages in both efficiency and effectiveness: (1) For efficiency, DR-MTCDR utilizes a unified module on all domains to capture disentangled domain-shared information and domain-specific information, which could support all domain recommendation and be insensitive to the number of domains. (2) For effectiveness, based on the disentangled domain-shared and domain-specific information, DR-MTCDR has the capability to lead positive effect and make trustworthy recommendation for each domain. Empirical evaluations on datasets from both public datasets and real-world large-scale financial datasets have shown that the proposed framework outperforms other state-of-the-art baselines.

Post-processing is crucial to modern recommendation systems to achieve various purposes, e.g., improving diversity, and giving reasonable itineraries which consist of combinations of items, but is merely studied in the literature. We decouple the recommendation system into two modules including a reward estimation module and a post-processing module. Our real-time post-processing module built on Ray abstracts the common post-processing problems in the itinerary recommendation as combinatorial optimization problems. Under the goal of maximizing the click-through rate, the more reasonable recommendation results are obtained by imposing various constraints on the candidate items. However, the optimization problems are typically mixed integer programming problems with quadratic terms in practice, which are NP-hard. In real-time scenarios, there are extremely high requirements for the speed of the solving process. We speed up the problem solving by linearizing and relaxing the original problem and use Ray serving as the underlying service to provide stable and efficient technical support. At last, We provide services to users by deploying the post-processing module in the itinerary recommendation scenario at Alipay's built-in applet named ''What's nearby''. The online A/B experiment shows that the user exposure click rate can be significantly improved.