1,720,962 research outputs found
Determining short-term changes in the hydraulic properties of a sandy-loam soil by a three-run infiltration experiment
Full text linkSoil structure-dependent parameters can vary rapidly as a consequence of perturbing events such as intense rainfall. Investigating their short-term changes is therefore essential to understand the general behaviour of a porous medium. The aim of this study is to gain insight into the effects of wetting, perturbation and recovery processes through different sequences of Beerkan infiltration experiments performed on a sandy-loam soil. Two different three-run infiltration experiments (LHL and LLL) were carried out by pouring water at low (L, non-perturbing) and high (H, perturbing) heights above the soil surface and at short time intervals (hours, days). The results demonstrate that the proposed method allows one to capture short-term variations in soil structure-dependent parameters. The developed methodology is expected to simplify the parameterization of hydrological models with temporally variable soil hydraulic properties
Leveraging Semantic Embeddings of User Reviews with Off-the-Shelf LLMs for Recommender Systems
Full text linkEnhancing Recommender Systems (RS) with plain-text reviews has been a challenging effort despite significant efforts in the past. Recently, Large Language Models (LLMs) have demonstrated exceptional capabilities in understanding natural language semantics, leading to promising applications across various fields. Nonetheless, applying these models to recommendation tasks introduces several challenges, including high computational demands and the potential for generating inaccurate or fabricated content (”hallucinations”). Consequently, instead of directly employing LLMs as generative models for recommendations, our research explores whether embeddings derived from plain-text reviews can enrich traditional recommendation algorithms and analyze the recommendation impact of different LLM embeddings with high effectiveness in NLP tasks. We conduct our experimental analysis using two Amazon Review Datasets, and three pre-trained LLM embedding models
Pre-Trained LLM Embeddings of Product Reviews for Recommendation
Full text linkA significant amount of past literature has shown that it is difficult to leverage plain-text reviews to improve recommendation effectiveness. Since then, Large Language Models (LLMs) have shown unprecedented ability to capture natural language semantics, which has been applied to multiple domains with good results. However, re-purposing them for recommendation is not straightforward, due to their high computational cost and the risk of hallucinations. For these reasons, rather than using LLMs as models to directly generate recommendations, we investigate if LLM embeddings of plain-text reviews can be a useful input to improve the quality of traditional review-based recommendation algorithms, by adapting their architecture to process said embeddings rather than word-level ones. We structure an empirical analysis using two Amazon Review Datasets and three LLMs to produce embeddings: OpenAI, Wang’s Mistral and VoyageAI. The results show that LLM embeddings can be effectively used in review-based models developed for word-level embeddings, yet one baseline model still achieves greater accuracy
Leveraging Semantic Embeddings of User Reviews with Off-the-Shelf LLMs for Recommender Systems
Full text linkEnhancing Recommender Systems (RS) with plain-text reviews has been a challenging effort despite significant efforts in the past. Recently, Large Language Models (LLMs) have demonstrated exceptional capabilities in understanding natural language semantics, leading to promising applications across various fields. Nonetheless, applying these models to recommendation tasks introduces several challenges, including high computational demands and the potential for generating inaccurate or fabricated content (”hallucinations”). Consequently, instead of directly employing LLMs as generative models for recommendations, our research explores whether embeddings derived from plain-text reviews can enrich traditional recommendation algorithms and analyze the recommendation impact of different LLM embeddings with high effectiveness in NLP tasks. We conduct our experimental analysis using two Amazon Review Datasets, and three pre-trained LLM embedding models
Pre-Trained LLM Embeddings of Product Reviews for Recommendation
Full text linkA significant amount of past literature has shown that it is difficult to leverage plain-text reviews to improve recommendation effectiveness. Since then, Large Language Models (LLMs) have shown unprecedented ability to capture natural language semantics, which has been applied to multiple domains with good results. However, re-purposing them for recommendation is not straightforward, due to their high computational cost and the risk of hallucinations. For these reasons, rather than using LLMs as models to directly generate recommendations, we investigate if LLM embeddings of plain-text reviews can be a useful input to improve the quality of traditional review-based recommendation algorithms, by adapting their architecture to process said embeddings rather than word-level ones. We structure an empirical analysis using two Amazon Review Datasets and three LLMs to produce embeddings: OpenAI, Wang’s Mistral and VoyageAI. The results show that LLM embeddings can be effectively used in review-based models developed for word-level embeddings, yet one baseline model still achieves greater accuracy
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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