Cross-domain Recommendation (CDR) aims to alleviate the data sparsity and the cold-start problems in traditional recommender systems by leveraging knowledge from an informative source domain. However, previously proposed CDR models pursue an imprudent assumption that the entire information from the source domain is equally contributed to the target domain, neglecting the evil part that is completely irrelevant to users' intrinsic interest. To address this concern, in this paper, we propose a novel knowledge enhanced cross-domain recommendation framework named CoTrans, which remolds the core procedures of CDR models with: Compression on the knowledge from the source domain and Transfer of the purity to the target domain. Specifically, following the theory of Graph Information Bottleneck, CoTrans first compresses the source behaviors with the perception of information from the target domain. Then to preserve all the important information for the CDR task, the feedback signals from both domains are utilized to promote the effectiveness of the transfer procedure. Additionally, a knowledge-enhanced encoder is employed to narrow gaps caused by the non-overlapped items across separate domains. Comprehensive experiments on three widely used cross-domain datasets demonstrate that CoTrans significantly outperforms both single-domain and state-of-the-art cross-domain recommendation approaches.

Recently, the growth of service platforms brings great convenience to both users and merchants, where the service search engine plays a vital role in improving the user experience by quickly obtaining desirable results via textual queries. Unfortunately, users’ uncontrollable search customs usually bring vast amounts of long-tail queries, which severely threaten the capability of search models. Inspired by recently emerging graph neural networks (GNNs) and contrastive learning (CL), several efforts have been made in alleviating the long-tail issue and achieve considerable performance. Nevertheless, they still face a few major weaknesses. Most importantly, they do not explicitly utilize the contextual structure between heads and tails for effective knowledge transfer, and intention-level information is commonly ignored for more generalized representations.To this end, we develop a novel framework GARCIA, which exploits the graph based knowledge transfer and intention based representation generalization in a contrastive setting. In particular, we employ an adaptive encoder to produce informative representations for queries and services, as well as hierarchical structure aware representations of intentions. To fully understand tail queries and services, we equip GARCIA with a novel multi-granularity contrastive learning module, which powers representations through knowledge transfer, structure enhancement and intention generalization. Subsequently, the complete GARCIA is well trained in a pre-training&fine-tuning manner. At last, we conduct extensive experiments on both offline and online environments, which demonstrates the superior capability of GARCIA in improving tail queries and overall performance in service search scenarios.

In this paper, we study the digital marketing where marketing officers (MOs) have to commit to creating brand new promotion ads/contents based on understandings of users' needs or preferences. Users' behaviors are typically high dimensional and hard to understand. Therefore, dimension reduction of users' behaviors from high dimensions and explainability are important to help MOs launch operation-friendly marketings. As such, it is natural to exploit topic models to help MOs understand users' intents from users' behaviors (e.g., user-item visits) in case we treat each user as a document and users' behaviors of visiting an item as a word. However, users of low activities and items followed by power law distributions are common in user-item visit data, which pose significant challenges to traditional topic models. We present a social and semantic enhanced topic model (S2TM) for users' intent mining. We optimize the user-intent estimates based on a graph neural network atop of a social network, and optimize the intent-item estimates based on a skip-gram word embedding approach by linking the semantics of items to pre-trained word embeddings. We propose an efficient stochastic vari-ational inference algorithm for the inference of latent variables and learning of parameters. Extensive experiments on real-world data show the effectivenesses of S2TM in terms of perplexities, topic coherence and semantic coherence compared with state-of-the-art topic models. We further show how MOs interact with our operation-friendly intent mining system, and results on real-world marketing campaigns in terms of click-through rate at Alipay.