A Survey on Large Language Models for Recommendation

Large Language Models (LLMs) have emerged as powerful tools in the field of Natural Language Processing (NLP) and have recently gained significant attention in the domain of Recommendation Systems (RS). These models, trained on massive amounts of data using self-supervised learning, have demonstrated remarkable success in learning universal representations and have the potential to enhance various aspects of recommendation systems by some effective transfer techniques such as fine-tuning and prompt tuning, and so on. The crucial aspect of harnessing the power of language models in enhancing recommendation quality is the utilization of their high-quality representations of textual features and their extensive coverage of external knowledge to establish correlations between items and users. To provide a comprehensive understanding of the existing LLM-based recommendation systems, this survey presents a taxonomy that categorizes these models into two major paradigms, respectively Discriminative LLM for Recommendation (DLLM4Rec) and Generative LLM for Recommendation (GLLM4Rec), with the latter being systematically sorted out for the first time. Furthermore, we systematically review and analyze existing LLM-based recommendation systems within each paradigm, providing insights into their methodologies, techniques, and performance. Additionally, we identify key challenges and several valuable findings to provide researchers and practitioners with inspiration. We have also created a GitHub repository to index relevant papers on LLMs for recommendation, https://github.com/WLiK/LLM4Rec.Comment: 10 pages, 3 figure

Research on observability of two wire-guided torpedoes tracking system

Under special condition, the surface target could be attacked by the submarine with two wire-guided torpedoes with passive acoustic homing mode. When the acoustic homing device detects the target bearing and transmit it back to the submarine through the wire, a three-sensor detection system is constituted by the submarine’s integrated sonar and the two wire-guided torpedoes. Whether the system can effectively track the target is related to the observability of the system. Only by satisfying the observable conditions can the moving parameters of the target could be calculated. For the nonlinear characteristics of the two wire-guided torpedoes tracking system, the observation equation is treated by pseudo-linear processing, and then the observability of the linear system is analyzed by using the observability decision theorem of the linear system, then the conditions for judging the observability are given. Finally, the correctness of the conclusion is verified by simulation

Influence of Loess–Mudstone Strata Structure on Slope Seismic Stability of Loess Plateau in China

The widely distributed loess–mudstone strata structure in the Loess Plateau of China is one of the fundamental reasons for the susceptibility and group occurrence of seismic landslides in the area. This study took the loess landslides induced by the Haiyuan earthquake as a case study. Through extensive field investigations, the loess cover thickness, mudstone dip angle, slope height, and slope angle were chosen as the influencing factors. Together with mathematical statistics and geotechnical tests, two models, one of a loess–mudstone slope and the other of a pure loess slope, were constructed. Their seismic stabilities under different slope angles and slope heights were compared to analyze the influence of the underlying mudstone strata on the loess slope seismic stability. Meanwhile, a sensitivity analysis of these strata factors affecting the seismic stability of the loess–mudstone slope was carried out based on the orthogonal test. The results showed that ignoring the underlying mudstone strata would lead to smaller calculation results than the actual situation. The sensitivity influence of the factors on the loess–mudstone slope seismic stability comprised the slope angle, loess cover thickness, slope height, and mudstone dip angle from high to low

Decision Variants for the Automatic Determination of Optimal Feature Subset in RF-RFE

Feature selection, which identifies a set of most informative features from the original feature space, has been widely used to simplify the predictor. Recursive feature elimination (RFE), as one of the most popular feature selection approaches, is effective in data dimension reduction and efficiency increase. A ranking of features, as well as candidate subsets with the corresponding accuracy, is produced through RFE. The subset with highest accuracy (HA) or a preset number of features (PreNum) are often used as the final subset. However, this may lead to a large number of features being selected, or if there is no prior knowledge about this preset number, it is often ambiguous and subjective regarding final subset selection. A proper decision variant is in high demand to automatically determine the optimal subset. In this study, we conduct pioneering work to explore the decision variant after obtaining a list of candidate subsets from RFE. We provide a detailed analysis and comparison of several decision variants to automatically select the optimal feature subset. Random forest (RF)-recursive feature elimination (RF-RFE) algorithm and a voting strategy are introduced. We validated the variants on two totally different molecular biology datasets, one for a toxicogenomic study and the other one for protein sequence analysis. The study provides an automated way to determine the optimal feature subset when using RF-RFE

Carbon Allocation of Quercus mongolica Fisch. ex Ledeb. across Different Life Stages Differed by Tree and Shrub Growth Forms at the Driest Site of Its Distribution

There are less than 10% of woody species that can have both tree and shrub growth forms globally. At the xeric timberline, we observed the tree-to-shrub shift of the Quercus mongolica Fisch. ex Ledeb.. Few studies have explored the underlined mechanism of this morphological transition of tree-to-shrub in arid regions. To examine whether the tree-to-shrub shift affects carbohydrate allocation and to verify the effect of life stage on non-structural carbohydrate (NSC) storage, we measured the concentration of soluble sugar and starch of Q. mongolica in the seedlings, saplings, and adult trees by selecting two sites with either tree or shrub growth forms of Q. mongolica at the driest area of its distribution. Accordingly, there was no significant difference in the radial growth with different growth forms (p > 0.05). The results showed that the effects of growth form on NSC concentrations are significant in the seedling and sapling stages, but become less pronounced as Q. mongolica grows. The results of the linear mixed model showed that life stage has a significant effect on soluble sugar concentration of tree-form (p < 0.05), starch and TNC concentration of shrub-form (p < 0.05). Compared with a shrub form without seedling stage, a tree form needs to accumulate more soluble sugar from seedling stage to adapt to arid environment. Saplings and adult shrubs store more starch, especially in thick roots, in preparation for sprout regeneration. Our study shows that the same species with tree and shrub forms embody differentiated carbohydrate allocation strategies, suggesting that shrub form can better adapt to a drier habitat, and the tree-to-shrub shift can benefit the expansion of woody species distribution in dryland

Biosynthesis of Silver and Gold Crystals Using Grapefruit Extract

In this paper, biological synthesis of silver and gold crystals using grapefruit extract is reported. On treatment of aqueous solutions of silver nitrate and chloroauric acid with grapefruit extract, the formation of stable silver and gold particles at high concentrations is observed to occur. The silver particles formed are quasi-spherical or irregular with sizes ranging from several hundred nanometers to several microns. The gold quasi-spheres with holes on surfaces and with diameters ranging from 1 to 3 microns are obtained. The formation mechanism of silver and gold crystals is discussed, indicating that the soluble biomolecules such as protein(s) and vitamin C in grapefruit extract may play a crucial role in defining the morphology and/or crystal phase of silver and gold crystals

Green Synthesis of Barium Sulfate Particles Using Plant Extracts

The biological molecules in the extracts of four fruits or vegetables: kiwifruit, oranges, tomato and carrot, were used as templates to synthesize barium sulfate (BaSO4) particles. The products were characterized by scanning electron microscopy, Fourier transform infrared spectroscopy and X-ray power diffractometry. The results showed that, leaf-shaped barite BaSO4 crystals with toothed edge were obtained with kiwifruit extracts; thorn spherical barium sulfate crystals with diameter of 2-4 micrometers were produced with tomato extracts; rod-like or quasi-spherical BaSO4 crystals with size of several hundred nanometers to several micrometers were gained with orange extracts; while quasi-spherical BaSO4 nano-crystals were obtained with carrot extracts. The formation mechanism of BaSO4 is also discussed, showing that the proteins, carbohydrates, vitamins and organic acids in above four kinds of fruits or vegetables may provide nucleation sites, controlling the growth of BaSO4 crystals with different morphologies