1,720,959 research outputs found
Building semantic user profile for Polish web news portal
Get PDFWe present our research at Onet, the largest Polish news portal, aimed at constructing meaningful user profiles that are most descriptive of their interests in the context of the media content they browse. We used two distinct state-of-the-art numerical text-representation techniques: LDA topic modeling and Word2Vec word embeddings. We trained our models on the corpora of articles in Polish and compare them with a baseline model built on a general language corpora.We compared the performance of algorithms on two distinct tasks - similar articles retrieval and users gender classification. Our results show that the choice of text representation depends on the task - Word2Vec is more suitable for text comparison, especially for short texts such as titles. In the user profiling task, the best performance was obtained with a combination of features: topics from the article text and word embeddings from the title
On modeling cognitive and affective factors in legal decision-making
No full textIn recent years, many empirical studies of legal decision-making process have shown that it incorporates many cognitive, affective, and supra-legal factors. Our goal is to design artificial intelligence systems that model these aspects of legal decision-making. Our vision is to implement a kind of legal assistant that can be used by lawyers and judges to run through different scenarios and produce arguments for different, and possibly contradictory, decisions. We propose a multi-agent blackboard architecture for such an assistive system, employing some insights from our previous work on a context-aware recommender system
Incorporating human dimension in autonomous decision-making on moral and ethical issues
No full textAs autonomous systems are becoming more and more pervasive, they often have to make decisions concerning moral and ethical values. There are many approaches to incorporating moral values in autonomous decision-making that are based on some sort of logical deduction. However, we argue here, in order for decision-making to seem persuasive to humans, it needs to reflect human values and judgments. Employing some insights from our ongoing research using features of the blackboard architecture for a context-aware recommender system, and a legal decision-making system that incorporates supra-legal aspects, we aim to exploreif this architecture can also be adapted to implement a moral decision-making system
that generates rationales that are persuasive to humans. Our vision is that such a system can be used as an advisory system to consider a situation from different moral pers pectives, and generate ethical pros and cons of taking a particular course of action in a given context
A bias detection tree approach for detecting disparities in a recommendation model’s errors
Get PDFMany of the current recommendation systems are considered to be blackboxes that are tuned to optimize some global objective function. However, their error distribution may differ dramatically among different combinations of attributes, and such algorithms may lead to propagating hidden data biases. Identifying potential disparities in an algorithm’s functioning is essential for building recommendation systems in a fair and responsible way. In this work, we propose a model-agnostic technique to automatically detect the combinations of user and item attributes correlated with unequal treatment by the recommendation model. We refer to this technique as the Bias Detection Tree. In contrast to the existing works in this field, our method automatically detects disparities related to combinations of attributes without any a priori knowledge about protected attributes, assuming that relevant metadata is available. Our results on five public recommendation datasets show that the proposed technique can identify hidden biases in terms of four kinds of metrics for multiple collaborative filtering models. Moreover, we adapt a minimax model selection technique to control the trade-off between the global and the worst-case optimizations and improve the recommendation model’s performance for biased attributes
Bias-Aware Hierarchical Clustering for detecting the discriminated groups of users in recommendation systems
No full textOne challenge for the modern recommendation systems is the Tyranny of Majority - the generated recommendations are often optimized for the mainstream trends so that the minority preference groups remain discriminated. Moreover, most modern recommendation techniques are characterized as black-box systems. Given a lack of understanding of the dataset characteristics and insufficient diversity of represented individuals, such approaches inevitably lead to amplifying hidden data biases and existing disparities. In this research, we address this problem by proposing a novel approach to detecting and describing potentially discriminated user groups for a given recommendation algorithm. We propose a Bias-Aware Hierarchical Clustering algorithm that identifies user clusters based on latent embeddings constructed by a black-box recommender to identify users whose needs are not met by the given recommendation method. Next, a post-hoc explainer model is applied to reveal the most important descriptive features that characterize these user segments. Our method is model-agnostic and does not require any a priori information about existing disparities and sensitive attributes. An experimental evaluation on a synthetic dataset and two real-world datasets from different domains shows that, compared with other clustering methods and arbitrarily selected user groups, our method is capable of identifying underperforming segments for different recommendation algorithms, and detect more severe disparities
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe 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
Get PDF“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
Get PDFWe 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
Get PDFWe 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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