Session-based Recommendation (SR) aims to predict users' next click based on their behavior within a short period, which is crucial for online platforms. However, most existing SR methods somewhat ignore the fact that user preference is not necessarily strongly related to the order of interactions. Moreover, they ignore the differences in importance between different samples, which limits the model-fitting performance. To tackle these issues, we put forward the method, Mining Interest Trends and Adaptively Assigning Sample Weight, abbreviated as MTAW. Specifically, we model users' instant interest based on their present behavior and all their previous behaviors. Meanwhile, we discriminatively integrate instant interests to capture the changing trend of user interest to make more personalized recommendations. Furthermore, we devise a novel loss function that dynamically weights the samples according to their prediction difficulty in the current epoch. Extensive experimental results on two benchmark datasets demonstrate the effectiveness and superiority of our method.

Abstract Hierarchical Text Classification (HTC) is a formidable task which involves classifying textual descriptions into a taxonomic hierarchy. Existing methods, however, have difficulty in adequately modeling the hierarchical label structures, because they tend to focus on employing graph embedding methods to encode the hierarchical structure while disregarding the fact that the HTC labels are rooted in a tree structure. This is significant because, unlike a graph, the tree structure inherently has a directive that ordains information flow from one node to another—a critical factor when applying graph embedding to the HTC task. But in the graph structure, message-passing is undirected, which will lead to the imbalance of message transmission between nodes when applied to HTC. To this end, we propose a unidirectional message-passing multi-label generation model for HTC, referred to as UMP-MG. Instead of viewing HTC as a classification problem as previous methods have done, this novel approach conceptualizes it as a sequence generation task, introducing prior hierarchical information during the decoding process. This further enables the blocking of information flow in one direction to ensure that the graph embedding method is better suited for the HTC task and thus resulted in the enhanced tree structure representation. Results obtained through experimentation on both the public WOS dataset and an E-commerce user intent classification dataset demonstrate that our proposed model can achieve superlative results.

Nowadays, it is a heated topic for many industries to build automatic question-answering (QA) systems. A key solution to these QA systems is to retrieve from a QA knowledge base the most similar question of a given question, which can be reformulated as a paraphrase identification (PI) or a natural language inference (NLI) problem. However, most existing models for PI and NLI have at least two problems: They rely on a large amount of labeled data, which is not always available in real scenarios, and they may not be efficient for industrial applications. In this paper, we study transfer learning for the PI and NLI problems, aiming to propose a general framework, which can effectively and efficiently adapt the shared knowledge learned from a resource-rich source domain to a resource-poor target domain. Specifically, since most existing transfer learning methods only focus on learning a shared feature space across domains while ignoring the relationship between the source and target domains, we propose to simultaneously learn shared representations and domain relationships in a unified framework. Furthermore, we propose an efficient and effective hybrid model by combining a sentence encoding-based method and a sentence interaction-based method as our base model. Extensive experiments on both paraphrase identification and natural language inference demonstrate that our base model is efficient and has promising performance compared to the competing models, and our transfer learning method can help to significantly boost the performance. Further analysis shows that the inter-domain and intra-domain relationship captured by our model are insightful. Last but not least, we deploy our transfer learning model for PI into our online chatbot system, which can bring in significant improvements over our existing system. Finally, we launch our new system on the chatbot platform Eva in our E-commerce site AliExpress.