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Hyperbolic Translation-Based Sequential Recommendation
Get PDFThe goal of sequential recommendation algorithms is to predict personalized sequential behaviors of users (i.e., next-item recommendation). Learning representations of entities (i.e., users and items) from sparse interaction behaviors and capturing the relationships between entities are the main challenges for sequential recommendation. However, most sequential recommendation algorithms model relationships among entities in Euclidean space, where it is difficult to capture hierarchical relationships among entities. Moreover, most of them utilize independent components to model the user preferences and the sequential behaviors, ignoring the correlation between them. To simultaneously capture the hierarchical structure relationships and model the user preferences and the sequential behaviors in a unified framework, we propose a general hyperbolic translation-based sequential recommendation framework, namely HTSR. Specifically, we first measure the distance between entities in hyperbolic space. Then, we utilize personalized hyperbolic translation operations to model the third-order relationships among a user, his/her latest visited item, and the next item to consume. In addition, we instantiate two hyperbolic translation-based sequential recommendation models, namely Poincaré translation-based sequential recommendation (PoTSR) and Lorentzian translation-based sequential recommendation (LoTSR). PoTSR and LoTSR utilize the Poincaré distance and Lorentzian distance to measure similarities between entities, respectively. Moreover, we utilize the tangent space optimization method to determine optimal model parameters. Experimental results on five real-world datasets show that our proposed hyperbolic translation-based sequential recommendation methods outperform the state-of-the-art sequential recommendation algorithms
Dataset: Animal Traders in London 1840-1934
No full textThe dataset is drawn from a survey of the listings of sellers of animals and goods and services for animals in London Trade Directories between 1840 and 1934. Directories were surveyed at approximately ten year intervals (with some discrepancies depending on the survival of directories in archival collections). The directories are held by the Institute of Historical Research, the National Art Library and Westminster Archives Centre. To access the link please contact Jane Hamlett [email protected]
Separation-Dependent Near-Field Effects in Mie Scattering Spectra of Two Optically Trapped Aerosol Droplets
Get PDFThe backscattering of ultraviolet and visible light by a model organic aerosol droplet,squalane, is investigated upon approach of a second isolated droplet at varying separationsIllumination and collection of light is along the interparticle axis. The conditions replicate typicalbroadband light spectroscopy studies of atmospheric aerosol. -Matrix near-field modelling,which includes near-field effects, predicts separation-dependent changes in the intensity of thebackscattered light on close approach of neighbouring spheres. However, the experimental resultsshow no evidence of separation-dependent near-field effects on the scattering. The results arebest replicated by modelling the droplets as individual scatterers
The regularity of polysemy patterns in the mind:Computational and experimental data
Get PDFLinguists have often observed that the sense extensions in polysemous words follow patterns. Yet, these patterns have rarely been quantified, and it is unknown whether language users are sensitive to them. We developed four regularity metrics, focusing in this initial study on metaphor patterns that apply to nouns. We further tested adult English speakers’ capacity to understand new senses in an acceptability judgement task. We compared novel senses that followed a metaphor pattern against novel senses that did not respect any pattern. Our results showed that novel senses were judged as more acceptable when they were part of a polysemy pattern as opposed to when they were not. We also assessed whether acceptability judgements were influenced by the degree of regularity of the pattern that they follow. The results confirmed the psychological validity of degree of regularity as a measure: the more regular the polysemy pattern, the more acceptable the new sense following that pattern. Regularity metrics that captured the consistency with which a pattern is instantiated were more successful in predicting acceptability ratings than regularity metrics that captured the number of times a pattern is instantiated. These results motivate future psycholinguistic studies investigating the influence of regularity on learning, processing, and storage of polysemes in a more nuanced way than has been possible previously
Addressing climate change with behavioral science:A global intervention tournament in 63 countries
Get PDFTesting mechanisms underlying children’s reading development:The power of learning lexical representations
Get PDFEthics and international business research:Considerations and best practices
Get PDFResearch integrity matters. It enables researchers to trust each other and their findings, and provides a basis for society’s trust in our research. We explore research integrity using the lens of international business (IB) research, focusing on IB research methods. We narrow the topic further by focusing on ethical issues associated with a single project by a single author. We examine the methodological challenges involved in conducting research in the complex IB environment and propose best practices for both quantitative and qualitative IB research methods. In some ways, this is a “back to basics” message; in other ways, we draw attention to the heightened complexity of the IB environment and the need to invest in rigorous methods and ethical practices in our unending pursuit of truth
A WiFi RSS-RTT Indoor Positioning Model Based on Dynamic Model Switching Algorithm
Get PDFThe advances in WiFi technology have encouraged the development of numerous indoor positioning systems. However, their performance varies significantly across different indoor environments, making it challenging in identifying the most suitable system for all scenarios. To address this challenge, we propose an algorithm that dynamically selects the most optimal WiFi positioning model for each location. Our algorithm employs a Machine Learning weighted model selection algorithm, trained on raw WiFi RSS, raw WiFi RTT data, statistical RSS & RTT measures, and Access Point line-of-sight information. We tested our algorithm in four complex indoor environments, and compared its performance to traditional WiFi indoor positioning models and state-of-the-art stacking models, demonstrating an improvement of up to 1.8 meters on average
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