1,720,954 research outputs found
Recommendation System Based on Complete Personalization
No full textAbstractCurrent recommender systems are very inefficient. There are many metrics that are used to measure the effectiveness of recommender systems. These metrics often include “conversion rate” and “click through rate”. Recently, these rates are in low single digit (less than 10%). In other words, for more than 90% of times, the model that the targeting system is based on, produces noise. The belief in this work is that the main problem leading to getting such unsatisfactory outcomes is the modeling problem. Much of the modeling problem could be represented and exemplified in treating users and items as member of clusters(segments). In this work, we consider full personalization of recommendation systems. We aim at personalization of users and contents simultaneously. Recommendations using baseline approach are inaccurate and targeting based on similarity-based recommendation (collaborative filtering) suffer from many disadvantages such as the neglect of interactive correlation. In this work, similarity based targeting has been combined with baseline approach and latent factor models and has been treated with adaptive regularization allowing complete personalization with respect to both users and items
Algorithmic Approach for Learning a Comprehensive View of Online Users
No full textAbstractOnline users may use many different channels, devices and venues for any online user experience. To make all services such as web design, ads, web content, shopping, personalized for every user; we need to be able to recognize them regardless of device, channels and venues they are using. This, in turn, requires building up a comprehensive view of the user which includes all of their behavioral characteristics - that are spread all over these different venues. This would not be possible without having all behavioral related data of the user which requires the capacity of connecting the user all over the devices, and channels, so to have all of their behavior under a single view. This work is a major attempt in doing this using only behavioral data of users while protecting the user's privacy
Unsupervised Feature Extraction Using Singular Value Decomposition
No full textAbstractThough modern data often provides a massive amount of information, much of the insight might be redundant or useless (noise). Thus, it is significant to recognize the most informative features of data. This will help the analysis of the data by removing the consequences of high dimensionality, in addition of obtaining other advantages of lower dimensional data such as lower computational cost and a less complex model. Modern data has high dimension, sparsity and correlation besides its characteristics of being unstructured, distorted, corrupt, deformed, and massive. Feature extraction has always been a major toll in machine learning applications. Due to these extraordinary features of modern data, feature extraction and feature reduction models and techniques have even more significance in analyzing and understanding the data
Computation of Recommender System Using Localized Regularization
No full textAbstractOnline and offline targeting and recommendations are major topics in ecommerce. The topic is treated as “Matrix Completion”, “Missing Values” and “Matrix Imputations” in statistics. The main goal in all of these fields is to compute the unknown (missing) values in the data matrix. In computing or recovering the unknown entries of the matrix, overfitting may happen which is due to the lack of sufficient information and thus some penalization of the objective function in the form of regularization becomes necessary. This work is based on a different view of regularization, i.e., a localized regularization technique which leads to improvement in the estimation of the missing values
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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