One for All, All for One: Learning and Transferring User Embeddings for Cross-Domain Recommendation

Cross-domain recommendation is an important method to improve recommender system performance, especially when observations in target domains are sparse. However, most existing techniques focus on single-target or dual-target cross-domain recommendation (CDR) and are hard to be generalized to CDR with multiple target domains. In addition, the negative transfer problem is prevalent in CDR, where the recommendation performance in a target domain may not always be enhanced by knowledge learned from a source domain, especially when the source domain has sparse data. In this study, we propose CAT-ART, a multi-target CDR method that learns to improve recommendations in all participating domains through representation learning and embedding transfer. Our method consists of two parts: a self-supervised Contrastive AuToencoder (CAT) framework to generate global user embeddings based on information from all participating domains, and an Attention-based Representation Transfer (ART) framework which transfers domain-specific user embeddings from other domains to assist with target domain recommendation. CAT-ART boosts the recommendation performance in any target domain through the combined use of the learned global user representation and knowledge transferred from other domains, in addition to the original user embedding in the target domain. We conducted extensive experiments on a collected real-world CDR dataset spanning 5 domains and involving a million users. Experimental results demonstrate the superiority of the proposed method over a range of prior arts. We further conducted ablation studies to verify the effectiveness of the proposed components. Our collected dataset will be open-sourced to facilitate future research in the field of multi-domain recommender systems and user modeling.Comment: 9 pages, accepted by WSDM 202

Electronic Properties of a New All-Inorganic Perovskite TlPbI\u3csub\u3e3\u3c/sub\u3e Simulated by the First Principles

All-inorganic perovskites have been recognized as promising photovoltaic materials. We simulated the perovskite material of TlPbI3 using ab initio electronic structure calculations. The band gap of 1.33 eV is extremely close to the theoretical optimum value. Compared TlPbI3 with CsPbI3, the total energy (−3980 eV) of the former is much lower than the latter. The partial density of states (PDOS) of TlPbI3 shows that a strong bond exists between Tl and I, resulting in the lower total energy and more stable existence than CsPbI3

Tenrec: A Large-scale Multipurpose Benchmark Dataset for Recommender Systems

Existing benchmark datasets for recommender systems (RS) either are created at a small scale or involve very limited forms of user feedback. RS models evaluated on such datasets often lack practical values for large-scale real-world applications. In this paper, we describe Tenrec, a novel and publicly available data collection for RS that records various user feedback from four different recommendation scenarios. To be specific, Tenrec has the following five characteristics: (1) it is large-scale, containing around 5 million users and 140 million interactions; (2) it has not only positive user feedback, but also true negative feedback (vs. one-class recommendation); (3) it contains overlapped users and items across four different scenarios; (4) it contains various types of user positive feedback, in forms of clicks, likes, shares, and follows, etc; (5) it contains additional features beyond the user IDs and item IDs. We verify Tenrec on ten diverse recommendation tasks by running several classical baseline models per task. Tenrec has the potential to become a useful benchmark dataset for a majority of popular recommendation tasks

Enhanced Performance of Planar Perovskite Solar Cells Using Low-Temperature Solution-Processed Al-Doped SnO\u3csub\u3e2\u3c/sub\u3e as Electron Transport Layers

Lead halide perovskite solar cells (PSCs) appear to be the ideal future candidate for photovoltaic applications owing to the rapid development in recent years. The electron transport layers (ETLs) prepared by low-temperature process are essential for widespread implementation and large-scale commercialization of PSCs. Here, we report an effective approach for producing planar PSCs with Al3+ doped SnO2 ETLs prepared by using a low-temperature solution-processed method. The Al dopant in SnO2 enhanced the charge transport behavior of planar PSCs and increased the current density of the devices, compared with the undoped SnO2 ETLs. Moreover, the enhanced electrical property also improved the fill factors (FF) and power conversion efficiency (PCE) of the solar cells. This study has indicated that the low-temperature solution-processed Al-SnO2 is a promising ETL for commercialization of planar PSCs

TransRec: Learning Transferable Recommendation from Mixture-of-Modality Feedback

Learning large-scale pre-trained models on broad-ranging data and then transfer to a wide range of target tasks has become the de facto paradigm in many machine learning (ML) communities. Such big models are not only strong performers in practice but also offer a promising way to break out of the task-specific modeling restrictions, thereby enabling task-agnostic and unified ML systems. However, such a popular paradigm is mainly unexplored by the recommender systems (RS) community. A critical issue is that standard recommendation models are primarily built on categorical identity features. That is, the users and the interacted items are represented by their unique IDs, which are generally not shareable across different systems or platforms. To pursue the transferable recommendations, we propose studying pre-trained RS models in a novel scenario where a user's interaction feedback involves a mixture-of-modality (MoM) items, e.g., text and images. We then present TransRec, a very simple modification made on the popular ID-based RS framework. TransRec learns directly from the raw features of the MoM items in an end-to-end training manner and thus enables effective transfer learning under various scenarios without relying on overlapped users or items. We empirically study the transferring ability of TransRec across four different real-world recommendation settings. Besides, we look at its effects by scaling source and target data size. Our results suggest that learning neural recommendation models from MoM feedback provides a promising way to realize universal RS

Editorial: Advances in Haptic Feedback for Neurorobotics Applications Research Topic

This Research Topic aims to highlight the most advanced achievements in motion intention decoding, haptic sensing, and haptic feedback for Neural-Machine Interface (NMI)-based neurorobotics research, which can be applied for teleoperation, human robot interaction with prosthetics, assistive and rehabilitative robots, and other relevant circumstances. The novel neural decoding methods, novel haptic feedback modalities, and new control strategies reported in this Research Topic should inspire and guide the future direction of this field

Identification of Salt Tolerance-related microRNAs and Their Targets in Maize (Zea mays L.) Using High-throughput Sequencing and Degradome Analysis

To identify the known and novel microRNAs (miRNAs) and their targets that are involved in the response and adaptation of maize (Zea mays) to salt stress, miRNAs and their targets were identified by a combined analysis of the deep sequencing of small RNAs (sRNA) and degradome libraries. The identities were confirmed by a quantitative expression analysis with over 100 million raw reads of sRNA and degradome sequences. A total of 1040 previously known miRNAs were identified from four maize libraries, with 762 and 726 miRNAs derived from leaves and roots, respectively, and 448 miRNAs that were common between the leaves and roots. A total of 37 potential new miRNAs were selected based on the same criteria in response to salt stress. In addition to known miR167 and miR164 species, novel putative miR167 and miR164 species were also identified. Deep sequencing of miRNAs and the degradome [with quantitative reverse transcription polymerase chain reaction (qRT-PCR) analyses of their targets] showed that more than one species of novel miRNA may play key roles in the response to salinity in maize. Furthermore, the interaction between miRNAs and their targets may play various roles in different parts of maize in response to salinity

Comparison of Canopy Cover and Leaf Area Index Estimation from Airborne LiDAR and Digital Aerial Photogrammetry in Tropical Forests

Digital aerial photogrammetry (DAP) has emerged as an alternative to airborne laser scanning (ALS) for forest inventory applications, as it offers a low-cost and flexible three-dimensional (3D) point cloud. Unlike the forest inventory attributes (e.g., tree height and diameter at breast height), the relative ability of DAP and ALS in predicting canopy structural variables (i.e., canopy cover and leaf area index (LAI)) has not been sufficiently investigated by previous studies. In this study, we comprehensively compared the canopy cover and LAI estimates using DAP- and ALS-based methods over 166 selected tropical forest sample plots with seven different tree species and forest types. We also explored the relationship between field-measured aboveground biomass (AGB) and the LAI estimates. The airborne LAI estimates were subsequently compared with the Sentinel-2-based LAI values that were retrieved using a one-dimensional radiative transfer model. The results demonstrated that the DAP-based method generally overestimated the two canopy variables compared to ALS-based methods but with relatively high correlations regardless of forest type and species (R2 of 0.80 for canopy cover and R2 of 0.76 for LAI). Under different forest types and species, the R2 of canopy cover and LAI range from 0.64 to 0.89 and from 0.54 to 0.87, respectively. Apparently, different correlations between AGB and LAI were found for different forest types and species where the mixed coniferous and broad-leaved forest shows the best correlation with R2 larger than 0.70 for both methods. The comparison with satellite retrievals verified that the ALS-based estimates are more consistent with Sentinel-2-based estimates than DAP-based estimates. We concluded that DAP data failed to provide analogous results to ALS data for canopy variable estimation in tropical forests