Sequential recommendations have gained great attention in recent years. Considering that user interactions naturally follow sequential decision tasks, many researchers have explored the integration of Reinforcement Learning (RL) into sequential recommendations. However, the limited exploration gap between recommender systems and other RL scenarios brings great challenges. Fortunately, Offline RL, which learns from expert experiences in offline datasets, has emerged as a potential solution. Nevertheless, utilizing offline RL to model user preferences is non-trivial due to strict model overestimation and implicit feedback issues. To address these challenges, we propose a novel offline RL framework called Pair-Wise Q-Learning (PQL). Our approach includes a state encoder module to generate a unified representation of user interaction states, a conservative Q-Learning approach with double Q-Networks to reduce overestimation and enhance robustness, a dynamic negative sampling strategy based on Q-values, and a pair-wise learning module to handle implicit feedback. We jointly optimize our model using conservative Q-Learning and pair-wise learning. Experimental results on two real-world e-commerce datasets, considering clicks and purchases, demonstrate the superior performance of our proposed PQL over existing methods.

In this paper, we present a fast and strong neural approach for general purpose text matching applications.We explore what is sufficient to build a fast and well-performed text matching model and propose to keep three key features available for inter-sequence alignment: original point-wise features, previous aligned features, and contextual features while simplifying all the remaining components.We conduct experiments on four well-studied benchmark datasets across tasks of natural language inference, paraphrase identification and answer selection.The performance of our model is on par with the state-of-the-art on all datasets with much fewer parameters and the inference speed is at least 6 times faster compared with similarly performed ones.

Fine-grained sentiment analysis is a useful tool for producers to understand consumers’ needs as well as complaints about products and related aspects from online platforms. In this article, we define a novel task named “Multi-Entity Aspect-Based Sentiment Analysis (ME-ABSA)”. It investigates the sentiment towards entities and their related aspects. It makes the well-studied aspect-based sentiment analysis a special case of this type, where the number of entities is limited to one. We contribute a new dataset for this task, with multi-entity Chinese posts in it. We propose to model context, entity, and aspect memory to address the task and incorporate dependency information for further improvement. Experiments show that our methods perform significantly better than baseline methods on datasets for both ME-ABSA task and ABSA task. The in-depth analysis further validates the effectiveness of our methods and shows that our methods are capable of generalizing to new (entity, aspect) combinations with little loss of accuracy. This observation indicates that data annotation in real applications can be largely simplified.

Inspired by recent works in Aspect-Based Sentiment Analysis (ABSA) on product reviews and faced with more complex posts on social media platforms mentioning multiple entities as well as multiple aspects, we define a novel task called Multi-Entity Aspect-Based Sentiment Analysis (ME-ABSA). This task aims at fine-grained sentiment analysis of (entity, aspect) combinations, making the well-studied ABSA task a special case of it. To address the task, we propose an innovative method that models Context memory, Entity memory and Aspect memory, called CEA method. Our experimental results show that our CEA method achieves a significant gain over several baselines, including the state-of-the-art method for the ABSA task, and their enhanced versions, on datasets for ME-ABSA and ABSA tasks. The in-depth analysis illustrates the significant advantage of the CEA method over baseline methods for several hard-to-predict post types. Furthermore, we show that the CEA method is capable of generalizing to new (entity, aspect) combinations with little loss of accuracy. This observation indicates that data annotation in real applications can be largely simplified.