Seamlessly Unifying Attributes and Items: Conversational Recommendation for Cold-Start Users

Static recommendation methods like collaborative filtering suffer from the inherent limitation of performing real-time personalization for cold-start users. Online recommendation, e.g., multi-armed bandit approach, addresses this limitation by interactively exploring user preference online and pursuing the exploration-exploitation (EE) trade-off. However, existing bandit-based methods model recommendation actions homogeneously. Specifically, they only consider the items as the arms, being incapable of handling the item attributes, which naturally provide interpretable information of user's current demands and can effectively filter out undesired items. In this work, we consider the conversational recommendation for cold-start users, where a system can both ask the attributes from and recommend items to a user interactively. This important scenario was studied in a recent work. However, it employs a hand-crafted function to decide when to ask attributes or make recommendations. Such separate modeling of attributes and items makes the effectiveness of the system highly rely on the choice of the hand-crafted function, thus introducing fragility to the system. To address this limitation, we seamlessly unify attributes and items in the same arm space and achieve their EE trade-offs automatically using the framework of Thompson Sampling. Our Conversational Thompson Sampling (ConTS) model holistically solves all questions in conversational recommendation by choosing the arm with the maximal reward to play. Extensive experiments on three benchmark datasets show that ConTS outperforms the state-of-the-art methods Conversational UCB (ConUCB) and Estimation-Action-Reflection model in both metrics of success rate and average number of conversation turns.Comment: TOIS 202

CIRS: Bursting Filter Bubbles by Counterfactual Interactive Recommender System

While personalization increases the utility of recommender systems, it also brings the issue of filter bubbles. E.g., if the system keeps exposing and recommending the items that the user is interested in, it may also make the user feel bored and less satisfied. Existing work studies filter bubbles in static recommendation, where the effect of overexposure is hard to capture. In contrast, we believe it is more meaningful to study the issue in interactive recommendation and optimize long-term user satisfaction. Nevertheless, it is unrealistic to train the model online due to the high cost. As such, we have to leverage offline training data and disentangle the causal effect on user satisfaction. To achieve this goal, we propose a counterfactual interactive recommender system (CIRS) that augments offline reinforcement learning (offline RL) with causal inference. The basic idea is to first learn a causal user model on historical data to capture the overexposure effect of items on user satisfaction. It then uses the learned causal user model to help the planning of the RL policy. To conduct evaluation offline, we innovatively create an authentic RL environment (KuaiEnv) based on a real-world fully observed user rating dataset. The experiments show the effectiveness of CIRS in bursting filter bubbles and achieving long-term success in interactive recommendation. The implementation of CIRS is available via https://github.com/chongminggao/CIRS-codes.Comment: 11 pages, 9 figure

Adaptive Vague Preference Policy Learning for Multi-round Conversational Recommendation

Conversational recommendation systems (CRS) effectively address information asymmetry by dynamically eliciting user preferences through multi-turn interactions. Existing CRS widely assumes that users have clear preferences. Under this assumption, the agent will completely trust the user feedback and treat the accepted or rejected signals as strong indicators to filter items and reduce the candidate space, which may lead to the problem of over-filtering. However, in reality, users' preferences are often vague and volatile, with uncertainty about their desires and changing decisions during interactions. To address this issue, we introduce a novel scenario called Vague Preference Multi-round Conversational Recommendation (VPMCR), which considers users' vague and volatile preferences in CRS.VPMCR employs a soft estimation mechanism to assign a non-zero confidence score for all candidate items to be displayed, naturally avoiding the over-filtering problem. In the VPMCR setting, we introduce an solution called Adaptive Vague Preference Policy Learning (AVPPL), which consists of two main components: Uncertainty-aware Soft Estimation (USE) and Uncertainty-aware Policy Learning (UPL). USE estimates the uncertainty of users' vague feedback and captures their dynamic preferences using a choice-based preferences extraction module and a time-aware decaying strategy. UPL leverages the preference distribution estimated by USE to guide the conversation and adapt to changes in users' preferences to make recommendations or ask for attributes. Our extensive experiments demonstrate the effectiveness of our method in the VPMCR scenario, highlighting its potential for practical applications and improving the overall performance and applicability of CRS in real-world settings, particularly for users with vague or dynamic preferences

Formation Mechanism of Laser-Driven Magnetized "Pillars of Creation"

Pillars of Creation, one of the most recognized objects in the sky, are believed to be associated with the formation of young stars. However, so far, the formation and maintenance mechanism for the pillars are still not fully understood due to the complexity of the nonlinear radiation magneto-hydrodynamics (RMHD). Here, assuming laboratory laser-driven conditions, we studied the self-consistent dynamics of pillar structures in magnetic fields by means of two-dimensional (2D) and three-dimensional (3D) RMHD simulations, and these results also support our proposed experimental scheme. We find only when the magnetic pressure and ablation pressure are comparable, the magnetic field can significantly alter the plasma hydrodynamics. For medium magnetized cases ($\beta_{initial} \approx 3.5$), {the initial magnetic fields undergo compression and amplification. This amplification results in the magnetic pressure inside the pillar becoming large enough to support the sides of the pillar against radial collapse due to pressure from the surrounding hot plasma. This effect is particularly pronounced for the parallel component ($B_y$), which is consistent with observational results.} In contrast, a strong perpendicular ($B_x, B_z$) magnetic field ($\beta_{initial} < 1$) almost remains its initial distribution and significantly suppresses the expansion of blow-off gas plasma, leading to the inability to form pillar-like structures. The 3D simulations suggest that the bending at the head of `Column \uppercase\expandafter{\romannumeral1}' in pillars of creation may be due to the non-parallel magnetic fields. After similarity scaling transformation, our results can be applied to explain the formation and maintenance mechanism of the pillars, and can also provide useful information for future experimental designs

Characteristics of Wild Cherry Beverage Co-fermented by Hanseniaspora uvarum and Saccharomyces cerevisiae

One strain of Hanseniaspora uvarum YT-35 was screened from fermented sediment of wild cherry. Hanseniaspora uvarum YT-35 and commercial Saccharomyces cerevisiae were used as coculture for manufacture of fermented wild cherry beverage. The dynamics of microbial populations, reducing sugars and ethanol were analyzed at different stages of fermentation using single-strain fermentation with 2 strains of bacteria as a control. Meanwhile, the organic acids and volatile aromatic compounds of the fermented beverages were detected by high-performance liquid chromatography (HPLC) and headspace solid-phase microextraction/gas chromatography-mass spectrometry (HS-SPME/GC-MS). The results showed that H. uvarum YT-35 dominated in the pre-fermentation stage of co-culture. Compared with single fermentation with S. cerevisiae, the coculture fermentation resulted in lower ethanol content (3.51 g/L). Notably, HPLC results revealed that coculture fermented beverage reduced the yield of citric, malic and quinic acids and increased the yield of glacial acetic acid. HS-SPME/GC-MS results revealed that coculture fermented beverage produced more volatile compounds of esters, such as ethyl caproate, methyl benzoate and isoamyl octanoate and showed enhanced contents of ethyl laurate, ethyl octanoate, phenyl ethyl alcohol, benzyl alcohol, octanoic acid and lauric acid. Meanwhile, clustering analysis revealed that coculture fermentation were correlated with the greatest number of volatile aroma compounds in the fermented wild cherry beverage. This study provides scientific basis and theoretical guidance for the research of coculture strains with different metabolic potential in improving the quality of fruit juice fermented beverage

Integration of Brassinosteroid Signal Transduction with the Transcription Network for Plant Growth Regulation in Arabidopsis

SummaryBrassinosteroids (BRs) regulate a wide range of developmental and physiological processes in plants through a receptor-kinase signaling pathway that controls the BZR transcription factors. Here, we use transcript profiling and chromatin-immunoprecipitation microarray (ChIP-chip) experiments to identify 953 BR-regulated BZR1 target (BRBT) genes. Functional studies of selected BRBTs further demonstrate roles in BR promotion of cell elongation. The BRBT genes reveal numerous molecular links between the BR-signaling pathway and downstream components involved in developmental and physiological processes. Furthermore, the results reveal extensive crosstalk between BR and other hormonal and light-signaling pathways at multiple levels. For example, BZR1 not only controls the expression of many signaling components of other hormonal and light pathways but also coregulates common target genes with light-signaling transcription factors. Our results provide a genomic map of steroid hormone actions in plants that reveals a regulatory network that integrates hormonal and light-signaling pathways for plant growth regulation

PNG young children cohort study dataset

The explanation of the variables are included in the README

Influence of steam parameters on static and dynamic characteristics of labyrinth seal

[Objectives] In order to study the influence of working medium parameters on the static and dynamic characteristics of seals in turbomachinery,[Methods] a three-dimensional model of a labyrinth seal was created, and air and steam were applied in the numerical simulation. The Computational Fluid Dynamics (CFD) method and a rotating frame were applied to analyze the influence of different steam parameters on the leakage characteristics and dynamic characteristic coefficients.[Results] The results show that great differences in leakage flow rate are apparent under different air and steam conditions, and the fluid-induced force shows linear and nonlinear variation with the increasing whirl speed. When the steam temperature increases, the system stability decreases as the dynamic characteristic coefficients change.[Conclusions] In consequence, working medium parameters are of great significance for turbine stability, and the influence of working medium parameters on the static and dynamic characteristics of seals should be given great attention in practical application