To ensure long-term space missions, an autonomous localization and mapping system for lunar rovers is demanded. While the target-oriented localization and mapping problem can be solved through state-of-the-art methods, they greatly rely on human-in-loop remote operations, posing several challenges for the visual system of a rover when operating in a distant, unknown, and feature-sparse lunar environment. This article presents a segment anything model (SAM)-augmented target-oriented simultaneous localization and mapping (SLAM) framework that enables rovers to estimate the relative distance to the target on the lunar surface, thus ensuring the safety of the exploration task. Based on the proposed point-prompted object instance extraction (OIE) pipeline, object correspondences are first predicted in the middle-end of Lo-SLAM, where reliable semantic constraints are robustly associated cross image frames. We then maintain camera-object relative positioning between the camera and target in the visual odometry of Lo-SLAM. Meanwhile, a multilevel mapping and representation framework is proposed to keep the target explicit and characterized in different subtasks. Extensive experiments are conducted on our dataset, stereo planetary tracks (SePTs). Results show that the proposed Lo-SLAM is validated on challenging lunar scenarios with dramatic viewpoints and object scale changes. The average pose errors are 0.36 m in centroid and 0.27 m in scale, and the average object-centric trajectory error is 0.49% or so. An open-source dataset has been released athttps://github.com/miaTian99/SePT_Stereo-Planetary-Tracks.

In recent years, group buying has become one popular kind of online shopping activities, thanks to its larger sales and lower unit price. Unfortunately, seldom research focuses on the recommendations specifically for group buying by now. Although some recommendation models have been proposed for group recommendation, they can not be directly used to achieve the real-world group buying recommendation, due to the essential difference between group recommendation and group buying recommendation. In this paper, we first formalize the task of group buying recommendation into two sub-tasks. Then, based on our insights into the correlations and interactions between the two sub-tasks, we propose a novel recommendation model for group buying, namely MGBR, which is built mainly with a multi-task learning module. To improve recommendation performance further, we devise some collaborative expert networks and adjusted gates in the multi-task learning module, to promote the information interaction between the two sub-tasks. Furthermore, we propose two auxiliary losses corresponding to the two sub-tasks, to refine the representation learning in our model. Our extensive experiments not only demonstrate that the augmented representations learned in our model result in better performance than previous recommendation models, but also justify the impacts of the specially designed components in our model. To reproduce our model’s recommendation results conveniently, we have provided our model’s source code and dataset on https://github.com/DeqingYang/MGBR.

Deep neural networks (DNNs) have been widely imported into collaborative-filtering (CF) based recommender systems and yielded remarkable superiority over traditional recommendation models. However, most deep CF-based models perform weakly when observed user-item interactions are sparse since user preferences and item characteristics are inferred mainly based on observed (historical) interactions. To address this problem, we propose a deep knowledge-enhanced recommendation model in this paper. Specifically, to augment user/item representations in the scenario of sparse historical user-item interactions, we first incorporate the knowledge from open knowledge graphs and personal information of users as side information, from which sufficient features of users and items are extracted. Second, to well capture shifted user preferences, we leverage a memory component constituted by recently interacted items rather than all historical ones. Third, attentive user representations are generated by attention mechanism to capture the diversity of user preferences. Furthermore, we build a convolutional neural network to pool the latent features in user representations for better user modeling, which enhances recommendation performance further. Our extensive experiments conducted against two real-world datasets, i.e., Douban movie and NetEase music, demonstrate our model’s remarkable superiority over the state-of-the-art deep recommendation models.

Self-attention mechanism is primarily designed to capture the correlation (interaction) between any two objects in a sequence. Inspired by self-attention's success in many NLP tasks, some researchers have employed self-attention in sequential recommendation to refine user representations by capturing the correlations between the historical interacted items of a user. However, the user representations in previous self-attention based models are not flexible enough since the self-attention is only applied on user side, restricting performance improvement. In this paper, we propose a deep recommendation model with feature-level self-attention, namely SAFrec, which exhibits enhanced recommendation performance mainly due to its two advantages. The first one is that SAFrec employs self-attention mechanism on user side and item side simultaneously, to co-refine user representations and item representations. The second one is that, SAFrec leverages item features distilled from open knowledge graphs or websites, to represent users and items on fine-grained level (feature-level). Thus the correlations between users and items are discovered sufficiently. The extensive experiments conducted over two real datasets (NetEase music and Book-Crossing) not only demonstrate SAFrec's superiority on top-n recommendation over the state-of-the-art deep recommendation models, but also validate the significance of incorporating self-attention mechanism and feature-level representations.