title: A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem

authors: Ahn, Hyung Jun

subjects: similarity measure

abstract: Collaborative filtering is one of the most successful and widely used methods of automated product recommendation in online stores. The most critical component of the method is the mechanism of finding similarities among users using product ratings data so that products can be recommended based on the similarities. The calculation of similarities has relied on traditional distance and vector similarity measures such as Pearson’s correlation and cosine which, however, have been seldom questioned in terms of their effectiveness in the recommendation problem domain. This paper presents a new heuristic similarity measure that focuses on improving recommendation performance under cold-start conditions where only a small number of ratings are available for similarity calculation for each user. Experiments using three different datasets show the superiority of the measure in new user cold-start conditions.

text: A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem Collaborative filtering is one of the most successful and widely used methods of automated product recommendation in online stores. The most critical component of the method is the mechanism of finding similarities among users using product ratings data so that products can be recommended based on the similarities. The calculation of similarities has relied on traditional distance and vector similarity measures such as Pearson’s correlation and cosine which, however, have been seldom questioned in terms of their effectiveness in the recommendation problem domain. This paper presents a new heuristic similarity measure that focuses on improving recommendation performance under cold-start conditions where only a small number of ratings are available for similarity calculation for each user. Experiments using three different datasets show the superiority of the measure in new user cold-start conditions.

year: 2008

title: AI-enabled automated common vulnerability scoring from common vulnerabilities and exposures descriptions

authors: Zhang, Zijing

subjects: computer science

abstract: With the sheer amount of vulnerabilities, manually evaluating the impact of them is challenging. This paper proposes employing artificial intelligence models as substitutes for humans or as aides to human experts in estimating vulnerabilities. We compare the precision, recall, and F1 score amongst the Universal Sentence Encoder, Generative Pre-trained Transformer, and Support Vector Machine, trained on 118,000 vulnerabilities and tested on 51,000 vulnerabilities, with human experts on mean estimation error and variance for each type of vulnerability from the state of the art work in estimating vulnerability severity scores. The Universal Sentence Encoder demonstrates superior performance with results (72/77 % accuracy on severity level prediction) that significantly outperform human experts in assessment tasks for various types of vulnerabilities with high efficiency for memory consumption and low running time. Additionally, we examine the efficacy of our models in predicting the components of vulnerability evaluation metrics and the severity level of vulnerabilities. The findings highlight the potential of artificial intelligence agents to assist cybersecurity experts in this task which in the current state of the art is entirely manual.

text: AI-enabled automated common vulnerability scoring from common vulnerabilities and exposures descriptions With the sheer amount of vulnerabilities, manually evaluating the impact of them is challenging. This paper proposes employing artificial intelligence models as substitutes for humans or as aides to human experts in estimating vulnerabilities. We compare the precision, recall, and F1 score amongst the Universal Sentence Encoder, Generative Pre-trained Transformer, and Support Vector Machine, trained on 118,000 vulnerabilities and tested on 51,000 vulnerabilities, with human experts on mean estimation error and variance for each type of vulnerability from the state of the art work in estimating vulnerability severity scores. The Universal Sentence Encoder demonstrates superior performance with results (72/77 % accuracy on severity level prediction) that significantly outperform human experts in assessment tasks for various types of vulnerabilities with high efficiency for memory consumption and low running time. Additionally, we examine the efficacy of our models in predicting the components of vulnerability evaluation metrics and the severity level of vulnerabilities. The findings highlight the potential of artificial intelligence agents to assist cybersecurity experts in this task which in the current state of the art is entirely manual.

year: 2025

title: /u/ fronting and /t/ aspiration in Māori and New Zealand English

authors: Maclagan, Margaret

subjects: Maori

abstract: This article examines the relationship between the frontness of /u/ and the aspiration of /t/ in both Māori and New Zealand English (NZE). In both languages, these processes can be observed since the earliest recordings dating from the latter part of the nineteenth century. We report analyses of these developments for three groups of male speakers of Māori spanning the twentieth century. We compare the Māori analyses with analyses of related features of the speakers' English and of the English of monolingual contemporaries. The occurrence of these processes in Māori cannot be seen simply as interference from NZE as the Māori-speaking population became increasingly bilingual. We conclude that it was the arrival of English with its contrast between aspirated and unaspirated plosives, rather than direct borrowing, that was the trigger for the fronting of the hitherto stable back Māori /u/ vowel together with increased aspiration of /t/ before both /i/ and /u/.

text: /u/ fronting and /t/ aspiration in Māori and New Zealand English This article examines the relationship between the frontness of /u/ and the aspiration of /t/ in both Māori and New Zealand English (NZE). In both languages, these processes can be observed since the earliest recordings dating from the latter part of the nineteenth century. We report analyses of these developments for three groups of male speakers of Māori spanning the twentieth century. We compare the Māori analyses with analyses of related features of the speakers' English and of the English of monolingual contemporaries. The occurrence of these processes in Māori cannot be seen simply as interference from NZE as the Māori-speaking population became increasingly bilingual. We conclude that it was the arrival of English with its contrast between aspirated and unaspirated plosives, rather than direct borrowing, that was the trigger for the fronting of the hitherto stable back Māori /u/ vowel together with increased aspiration of /t/ before both /i/ and /u/.

year: 2009