Context-aware Sequential Recommendation

Since sequential information plays an important role in modeling user behaviors, various sequential recommendation methods have been proposed. Methods based on Markov assumption are widely-used, but independently combine several most recent components. Recently, Recurrent Neural Networks (RNN) based methods have been successfully applied in several sequential modeling tasks. However, for real-world applications, these methods have difficulty in modeling the contextual information, which has been proved to be very important for behavior modeling. In this paper, we propose a novel model, named Context-Aware Recurrent Neural Networks (CA-RNN). Instead of using the constant input matrix and transition matrix in conventional RNN models, CA-RNN employs adaptive context-specific input matrices and adaptive context-specific transition matrices. The adaptive context-specific input matrices capture external situations where user behaviors happen, such as time, location, weather and so on. And the adaptive context-specific transition matrices capture how lengths of time intervals between adjacent behaviors in historical sequences affect the transition of global sequential features. Experimental results show that the proposed CA-RNN model yields significant improvements over state-of-the-art sequential recommendation methods and context-aware recommendation methods on two public datasets, i.e., the Taobao dataset and the Movielens-1M dataset.Comment: IEEE International Conference on Data Mining (ICDM) 2016, to apea

Experimental and numerical investigation on the influence of the clocking position on hydraulic performance of a centrifugal pump with guide vane

The investigation of the clocking effect mainly concentrates on turbines and compressors, but seldom in centrifugal pumps. In this paper, using numerical simulation and experiment, the influence of the clocking effect on the hydraulic performance of centrifugal pump with guide vane is studied. Numerical simulations with SST k-w turbulence model were applied to obtain the inner flow field of the test pump. The numerical simulations coincide with the test result, which indicates the accurate of the utilized numerical approach. The results show the clocking positions have an important effect on hydraulic performance of the centrifugal pump with guide vane. The pump demonstrates the higher efficiency and head as the tongue locate between two guide vanes. The hydraulic performance of the volute is a major factor impacting the performance of the centrifugal pump with different clocking positions. However, the clocking position has almost no effect on the performances of the impeller and diffuser. When the guide vane is close to the volute tongue, flow field of volute is more non-uniform, and the energy loss in volute appears to be larger. The results and the method of this paper can provide theoretical reference for the design and installation of guide vane in centrifugal pump

NormBank: A Knowledge Bank of Situational Social Norms

We present NormBank, a knowledge bank of 155k situational norms. This resource is designed to ground flexible normative reasoning for interactive, assistive, and collaborative AI systems. Unlike prior commonsense resources, NormBank grounds each inference within a multivalent sociocultural frame, which includes the setting (e.g., restaurant), the agents' contingent roles (waiter, customer), their attributes (age, gender), and other physical, social, and cultural constraints (e.g., the temperature or the country of operation). In total, NormBank contains 63k unique constraints from a taxonomy that we introduce and iteratively refine here. Constraints then apply in different combinations to frame social norms. Under these manipulations, norms are non-monotonic - one can cancel an inference by updating its frame even slightly. Still, we find evidence that neural models can help reliably extend the scope and coverage of NormBank. We further demonstrate the utility of this resource with a series of transfer experiments

DyVal: Dynamic Evaluation of Large Language Models for Reasoning Tasks

Large language models (LLMs) have achieved remarkable performance in various evaluation benchmarks. However, concerns are raised about potential data contamination in their considerable volume of training corpus. Moreover, the static nature and fixed complexity of current benchmarks may inadequately gauge the advancing capabilities of LLMs. In this paper, we introduce DyVal, a general and flexible protocol for dynamic evaluation of LLMs. Based on our framework, we build graph-informed DyVal by leveraging the structural advantage of directed acyclic graphs to dynamically generate evaluation samples with controllable complexities. DyVal generates challenging evaluation sets on reasoning tasks including mathematics, logical reasoning, and algorithm problems. We evaluate various LLMs ranging from Flan-T5-large to GPT-3.5-Turbo and GPT-4. Experiments show that LLMs perform worse in DyVal-generated evaluation samples with different complexities, highlighting the significance of dynamic evaluation. We also analyze the failure cases and results of different prompting methods. Moreover, DyVal-generated samples are not only evaluation sets, but also helpful data for fine-tuning to improve the performance of LLMs on existing benchmarks. We hope that DyVal can shed light on future evaluation research of LLMs. Code is available at: https://github.com/microsoft/promptbench.Comment: ICLR 2024 spotlight; 38 pages; code is at aka.ms/dyva

Who buys new energy vehicles in china? Assessing social-psychological predictors of purchasing awareness, intention, and policy

This paper investigates the salience of social-psychological factors in explaining why drivers purchase (or fail to purchase) New Energy Vehicles (NEVs)—including hybrid electric vehicles, battery electric vehicles, and fuel cell electric vehicles—in China. A questionnaire measuring six dimensions (including attitudes, subjective norms, perceived behavioral control, personal norms, low-carbon awareness and policy) was distributed in Tianjin, where aggressive policy incentives for NEVs exist yet adoption rates remain low. Correlation analysis and hierarchical multiple regression analyses are applied data collected through 811 valid questionnaires. We present three main findings. First, there is an “awareness-behavior gap” whereby low-carbon awareness has a moderating effect on purchasing behavior via psychological factors. Second, subjective norms has a stronger influence on intention to purchase New Energy Vehicles than other social-psychological factors. Third, acceptability of government policies has positive significant impact on adoption of New Energy Vehicles, which can provide reference potential template for other countries whose market for New Energy Vehicles is also in an early stage

Decoding Susceptibility: Modeling Misbelief to Misinformation Through a Computational Approach

Susceptibility to misinformation describes the degree of belief in unverifiable claims, a latent aspect of individuals' mental processes that is not observable. Existing susceptibility studies heavily rely on self-reported beliefs, which can be subject to bias, expensive to collect, and challenging to scale for downstream applications. To address these limitations, in this work, we propose a computational approach to model users' latent susceptibility levels. As shown in previous research, susceptibility is influenced by various factors (e.g., demographic factors, political ideology), and directly influences people's reposting behavior on social media. To represent the underlying mental process, our susceptibility modeling incorporates these factors as inputs, guided by the supervision of people's sharing behavior. Using COVID-19 as a testbed domain, our experiments demonstrate a significant alignment between the susceptibility scores estimated by our computational modeling and human judgments, confirming the effectiveness of this latent modeling approach. Furthermore, we apply our model to annotate susceptibility scores on a large-scale dataset and analyze the relationships between susceptibility with various factors. Our analysis reveals that political leanings and psychological factors exhibit varying degrees of association with susceptibility to COVID-19 misinformation