27 research outputs found
U-rank: Utility-oriented Learning to Rank with Implicit Feedback
Learning to rank with implicit feedback is one of the most important tasks in
many real-world information systems where the objective is some specific
utility, e.g., clicks and revenue. However, we point out that existing methods
based on probabilistic ranking principle do not necessarily achieve the highest
utility. To this end, we propose a novel ranking framework called U-rank that
directly optimizes the expected utility of the ranking list. With a
position-aware deep click-through rate prediction model, we address the
attention bias considering both query-level and item-level features. Due to the
item-specific attention bias modeling, the optimization for expected utility
corresponds to a maximum weight matching on the item-position bipartite graph.
We base the optimization of this objective in an efficient Lambdaloss
framework, which is supported by both theoretical and empirical analysis. We
conduct extensive experiments for both web search and recommender systems over
three benchmark datasets and two proprietary datasets, where the performance
gain of U-rank over state-of-the-arts is demonstrated. Moreover, our proposed
U-rank has been deployed on a large-scale commercial recommender and a large
improvement over the production baseline has been observed in an online A/B
testing
Towards Open-World Recommendation with Knowledge Augmentation from Large Language Models
Recommender systems play a vital role in various online services. However,
the insulated nature of training and deploying separately within a specific
domain limits their access to open-world knowledge. Recently, the emergence of
large language models (LLMs) has shown promise in bridging this gap by encoding
extensive world knowledge and demonstrating reasoning capability. Nevertheless,
previous attempts to directly use LLMs as recommenders have not achieved
satisfactory results. In this work, we propose an Open-World Knowledge
Augmented Recommendation Framework with Large Language Models, dubbed KAR, to
acquire two types of external knowledge from LLMs -- the reasoning knowledge on
user preferences and the factual knowledge on items. We introduce factorization
prompting to elicit accurate reasoning on user preferences. The generated
reasoning and factual knowledge are effectively transformed and condensed into
augmented vectors by a hybrid-expert adaptor in order to be compatible with the
recommendation task. The obtained vectors can then be directly used to enhance
the performance of any recommendation model. We also ensure efficient inference
by preprocessing and prestoring the knowledge from the LLM. Extensive
experiments show that KAR significantly outperforms the state-of-the-art
baselines and is compatible with a wide range of recommendation algorithms. We
deploy KAR to Huawei's news and music recommendation platforms and gain a 7\%
and 1.7\% improvement in the online A/B test, respectively
How Can Recommender Systems Benefit from Large Language Models: A Survey
Recommender systems (RS) play important roles to match users' information
needs for Internet applications. In natural language processing (NLP) domains,
large language model (LLM) has shown astonishing emergent abilities (e.g.,
instruction following, reasoning), thus giving rise to the promising research
direction of adapting LLM to RS for performance enhancements and user
experience improvements. In this paper, we conduct a comprehensive survey on
this research direction from an application-oriented view. We first summarize
existing research works from two orthogonal perspectives: where and how to
adapt LLM to RS. For the "WHERE" question, we discuss the roles that LLM could
play in different stages of the recommendation pipeline, i.e., feature
engineering, feature encoder, scoring/ranking function, and pipeline
controller. For the "HOW" question, we investigate the training and inference
strategies, resulting in two fine-grained taxonomy criteria, i.e., whether to
tune LLMs or not, and whether to involve conventional recommendation model
(CRM) for inference. Detailed analysis and general development trajectories are
provided for both questions, respectively. Then, we highlight key challenges in
adapting LLM to RS from three aspects, i.e., efficiency, effectiveness, and
ethics. Finally, we summarize the survey and discuss the future prospects. We
also actively maintain a GitHub repository for papers and other related
resources in this rising direction:
https://github.com/CHIANGEL/Awesome-LLM-for-RecSys.Comment: 15 pages; 3 figures; summarization table in appendi
Patterns of Convergence and Divergence Between Bipolar Disorder Type I and Type II: Evidence From Integrative Genomic Analyses
Aim: Genome-wide association studies (GWAS) analyses have revealed genetic evidence of bipolar disorder (BD), but little is known about the genetic structure of BD subtypes. We aimed to investigate the genetic overlap and distinction of bipolar type I (BD I) & type II (BD II) by conducting integrative post-GWAS analyses.
Methods: We utilized single nucleotide polymorphism (SNP)-level approaches to uncover correlated and distinct genetic loci. Transcriptome-wide association analyses (TWAS) were then approached to pinpoint functional genes expressed in specific brain tissues and blood. Next, we performed cross-phenotype analysis, including exploring the potential causal associations between two BD subtypes and lithium responses and comparing the difference in genetic structures among four different psychiatric traits.
Results: SNP-level evidence revealed three genomic loci, SLC25A17, ZNF184, and RPL10AP3, shared by BD I and II, and one locus (MAD1L1) and significant gene sets involved in calcium channel activity, neural and synapsed signals that distinguished two subtypes. TWAS data implicated different genes affecting BD I and II through expression in specific brain regions (nucleus accumbens for BD I). Cross-phenotype analyses indicated that BD I and II share continuous genetic structures with schizophrenia and major depressive disorder, which help fill the gaps left by the dichotomy of mental disorders.
Conclusion: These combined evidences illustrate genetic convergence and divergence between BD I and II and provide an underlying biological and trans-diagnostic insight into major psychiatric disorders
A natural fast-cleaving branching ribozyme from the amoeboflagellate Naegleria pringsheimi
Transient changes in the ST-T waveform mimicking myocardial infarction in a child with near-drowning: a case report
Drowning is a common cause of childhood morbidity and mortality worldwide. Anoxia, hypothermia, and metabolic acidosis are mainly responsible for this morbidity. Drowning may lead to multiple organ damage, especially cardiac damage, in cases in which severe hypothermia and hypoxemia occur. We report a case of a 4-year-old girl who was admitted to our hospital’s Emergency Department because of drowning. She had elevated troponin I concentrations and ST-segment elevation with T wave inversion. However, cardiovascular computed tomography showed no obvious abnormalities in the coronary arteries. We suggest that cardiac damage in this situation is caused by coronary artery spasms. To the best of our knowledge, this is the first case of cardiac damage with electrocardiographic changes after drowning in a preschool child
A Meta-Analysis Based Method for Prioritizing Candidate Genes Involved in a Pre-specific Function
The identification of genes associated with a given biological function in plants remains a challenge, although network-based gene prioritization algorithms have been developed for Arabidopsis thaliana and many non-model plant species. Nevertheless, these network-based gene prioritization algorithms have encountered several problems; one in particular is that of unsatisfactory prediction accuracy due to limited network coverage, varying link quality, and/or uncertain network connectivity. Thus a model that integrates complementary biological data may be expected to increase the prediction accuracy of gene prioritization. Towards this goal, we developed a novel gene prioritization method named RafSee, to rank candidate genes using a random forest algorithm that integrates sequence, evolutionary, and epigenetic features of plants. Subsequently, we proposed an integrative approach named RAP (Rank Aggregation-based data fusion for gene Prioritization), in which an order statistics-based meta-analysis was used to aggregate the rank of the network-based gene prioritization method and RafSee, for accurately prioritizing candidate genes involved in a pre-specific biological function. Finally, we showcased the utility of RAP by prioritizing 380 flowering-time genes in Arabidopsis. The ‘leave-one-out’ cross-validation experiment showed that RafSee could work as a complement to a current state-of-art network-based gene prioritization system (AraNet v2). Moreover, RAP ranked 53.68% (204/380) flowering-time genes higher than AraNet v2, resulting in an 39.46% improvement in term of the first quartile rank. Further evaluations also showed that RAP was effective in prioritizing genes-related to different abiotic stresses. To enhance the usability of RAP for Arabidopsis and non-model plant species, an R package implementing the method is freely available at http://bioinfo.nwafu.edu.cn/software
Epidemiological and Clinical Characteristics of Kawasaki Disease and Factors Associated with Coronary Artery Abnormalities in East China: Nine Years Experience
Effects of carbon doping on annealing behavior of a CoCrFeNiMn high-entropy alloy
A 1Â at% carbon non-equiatomic doped Co19Cr5Fe38Ni19Mn19 high-entropy alloy was synthesized by vacuum arc-melting in a high-purity argon atmosphere, followed by homogenization, cold rolling, and annealing under various conditions. The evolutions of microstructure and mechanical properties during annealing were systematically studied and compared with the carbon free alloy. Results showed that nano-sized fibrous deformation grains were formed in the 90% cold rolled alloys, resulting in high strength but low plasticity. Recovery sub-structures and recrystallized grains gradually formed with increasing annealing temperature, leading to a significant decrease in defect density, thereby softening the materials and increasing their plasticity. The early stage of recovery was mainly related to the migration of vacancy and interstitial carbon atoms, while dislocation climb became the main recovery mechanism in the late recovery stage. The carbon-doped alloy exhibited a higher recovery activation energy compared with the carbon free alloy. Therefore, 1Â at% interstitial carbon effectively increased the recovery and recrystallization resistance of the Co19Cr5Fe38Ni19Mn19 alloy