Extracting Features of Entertainment Products: A Guided Latent Dirichlet Allocation Approach Informed by the Psychology of Media Consumption:

doi:10.1177/0022243718820559

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Extracting Features of Entertainment Products: A Guided Latent Dirichlet Allocation Approach Informed by the Psychology of Media Consumption:

Journal Article•DOI•

Extracting Features of Entertainment Products: A Guided Latent Dirichlet Allocation Approach Informed by the Psychology of Media Consumption:

Olivier Toubia, Garud Iyengar, Renée Bunnell, Alain Lemaire

01 Feb 2019-Journal of Marketing Research (SAGE PublicationsSage CA: Los Angeles, CA)-Vol. 56, Iss: 1, pp 18-36

TL;DR: In this article, a quantitative approach for describing entertainment products, in a way that allows for improving the predictive performance of consumer choice models for these products, has been proposed to improve the prediction performance of these models.

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Abstract: The authors propose a quantitative approach for describing entertainment products, in a way that allows for improving the predictive performance of consumer choice models for these products. Their ...

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Uniting the Tribes: Using Text for Marketing Insight

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Abstract: Words are part of almost every marketplace interaction. Online reviews, customer service calls, press releases, marketing communications, and other interactions create a wealth of textual data. But...

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Natural language processing (NLP) in management research: A literature review

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Yue Kang¹, Zhao Cai², Chee-Wee Tan³, Qian Huang¹, Hefu Liu¹ - Show less +1 more•Institutions (3)

University of Science and Technology of China¹, The University of Nottingham Ningbo China², Copenhagen Business School³

04 May 2020

TL;DR: This research presents a meta-modelling framework that automates the very labor-intensive and therefore time-heavy and expensive process of manually cataloging and cataloging individual words in a language.

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Abstract: Natural language processing (NLP) is gaining momentum in management research for its ability to automatically analyze and comprehend human language. Yet, despite its extensive application in manage...

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Brave New World? On AI and the Management of Customer Relationships

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Barak Libai¹, Yakov Bart², Sonja Gensler³, Charles F. Hofacker⁴, Andreas M. Kaplan, Kim Kötterheinrich³, Eike B. Kroll - Show less +3 more•Institutions (4)

Interdisciplinary Center Herzliya¹, Northeastern University², University of Münster³, Florida State University⁴

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TL;DR: In this paper, the authors conduct a critical analysis of how artificial intelligence (AI) affects the essential nature of customer relationship management (CRM) and survey the AI capabilities that will transform CRM into AI-CRM and examine how the transformation will influence customer acquisition, development and retention.

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Marketing ecosystem: An outside-in view for sustainable advantage

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Jonathan Z. Zhang¹, George F. Watson²•Institutions (2)

Colorado State University¹, Portland State University²

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TL;DR: In this paper, the authors propose that firm strategies should be informed through the lens of the marketing ecosystem that considers the interrelated and dynamic megatrends in the modern business environment.

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47 citations

Cites background from "Extracting Features of Entertainmen..."

...Although these text generators are not the firm's customers, they could nevertheless shed light on broad consumer cultural trends such as tastes in movies (Toubia et al., 2019) and music (Berger & Packard, 2018)....
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Journal Article•DOI•

A Survey on Artificial Intelligence in Chinese Sign Language Recognition

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Xianwei Jiang¹, Suresh Chandra Satapathy, Longxiang Yang¹, Shuihua Wang², Shuihua Wang³, Yudong Zhang³, Yudong Zhang⁴ - Show less +3 more•Institutions (4)

Nanjing Normal University¹, Loughborough University², University of Leicester³, King Abdulaziz University⁴

01 Dec 2020-Arabian Journal for Science and Engineering

TL;DR: This paper aims to cover the most recent approaches in Chinese Sign Language Recognition (CSLR) with a thorough review of superior methods from 2000 to 2019 in CSLR researches, and methods of classification and feature extraction, accuracy/performance evaluation, and sample size/datasets were compared.

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Abstract: Chinese Sign Language (CSL) offers the main means of communication for the hearing impaired in China. Sign Language Recognition (SLR) can shorten the distance between the hearing-impaired and healthy people and help them integrate into the society. Therefore, SLR has become the focus of sign language application research. Over the years, the continuous development of new technologies provides a source and motivation for SLR. This paper aims to cover the most recent approaches in Chinese Sign Language Recognition (CSLR). With a thorough review of superior methods from 2000 to 2019 in CSLR researches, various techniques and algorithms such as scale-invariant feature transform, histogram of oriented gradients, wavelet entropy, Hu moment invariant, Fourier descriptor, gray-level co-occurrence matrix, dynamic time warping, principal component analysis, autoencoder, hidden Markov model (HMM), support vector machine (SVM), random forest, skin color modeling method, k-NN, artificial neural network, convolutional neural network (CNN), and transfer learning are discussed in detail, which are based on several major stages, that is, data acquisition, preprocessing, feature extraction, and classification. CSLR was summarized from some aspect as follows: methods of classification and feature extraction, accuracy/performance evaluation, and sample size/datasets. The advantages and limitations of different CSLR approaches were compared. It was found that data acquisition is mainly through Kinect and camera, and the feature extraction focuses on hand’s shape and spatiotemporal factors, but ignoring facial expressions. HMM and SVM are used most in the classification. CNN is becoming more and more popular, and a deep neural network-based recognition approach will be the future trend. However, due to the complexity of the contemporary Chinese language, CSLR generally has a lower accuracy than other SLR. It is necessary to establish an appropriate dataset to conduct comparable experiments. The issue of decreasing accuracy as the dataset increases needs to resolve. Overall, our study is hoped to give a comprehensive presentation for those people who are interested in CSLR and SLR and to further contribute to the future research.

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30 citations

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Proceedings Article•

Latent Dirichlet Allocation

[...]

David M. Blei¹, Andrew Y. Ng¹, Michael I. Jordan¹•Institutions (1)

University of California, Berkeley¹

03 Jan 2001

TL;DR: This paper proposed a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams, and Hof-mann's aspect model, also known as probabilistic latent semantic indexing (pLSI).

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Abstract: We propose a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams [6], and Hof-mann's aspect model, also known as probabilistic latent semantic indexing (pLSI) [3]. In the context of text modeling, our model posits that each document is generated as a mixture of topics, where the continuous-valued mixture proportions are distributed as a latent Dirichlet random variable. Inference and learning are carried out efficiently via variational algorithms. We present empirical results on applications of this model to problems in text modeling, collaborative filtering, and text classification.

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25,546 citations

Journal Article•DOI•

Positive psychology: An introduction.

[...]

Martin E. P. Seligman, Mihaly Csikszentmihalyi¹•Institutions (1)

Claremont Graduate University¹

01 Jan 2000-American Psychologist

TL;DR: The authors outline a framework for a science of positive psychology, point to gaps in the authors' knowledge, and predict that the next century will see a science and profession that will come to understand and build the factors that allow individuals, communities, and societies to flourish.

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Abstract: A science of positive subjective experience, positive individual traits, and positive institutions promises to improve quality of life and prevent the pathologies that arise when life is barren and meaningless, The exclusive focus on pathology that has dominated so much of our discipline results in a model of the human being lacking the positive features that make life worth living. Hope, wisdom, creativity, future mindedness, courage, spirituality, responsibility, and perseverance are ignored or explained as transformations of more authentic negative impulses. The 15 articles in this millennial issue of the American Psychologist discuss such issues as what enables happiness, the effects of autonomy and self-regulation, how optimism and hope affect health, what constitutes wisdom, and how talent and creativity come to fruition. The authors outline a framework for a science of positive psychology, point to gaps in our knowledge, and predict that the next century will see a science and profession that will come to understand and build the factors that allow individuals, communities, and societies to flourish.

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12,650 citations

"Extracting Features of Entertainmen..." refers background in this paper

...Positive psychology is the branch of psychology that focuses on the achievement of a satisfactory life (for an introduction to positive psychology, see Seligman and Csikszentmihalyi 2000; Seligman et al. 2005)....
[...]

Journal Article•DOI•

Indexing by Latent Semantic Analysis

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Scott Deerwester¹, Susan T. Dumais², George W. Furnas², Thomas K. Landauer², Richard A. Harshman³ - Show less +1 more•Institutions (3)

University of Chicago¹, Telcordia Technologies², University of Western Ontario³

01 Sep 1990-Journal of the Association for Information Science and Technology

TL;DR: A new method for automatic indexing and retrieval to take advantage of implicit higher-order structure in the association of terms with documents (“semantic structure”) in order to improve the detection of relevant documents on the basis of terms found in queries.

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Abstract: A new method for automatic indexing and retrieval is described. The approach is to take advantage of implicit higher-order structure in the association of terms with documents (“semantic structure”) in order to improve the detection of relevant documents on the basis of terms found in queries. The particular technique used is singular-value decomposition, in which a large term by document matrix is decomposed into a set of ca. 100 orthogonal factors from which the original matrix can be approximated by linear combination. Documents are represented by ca. 100 item vectors of factor weights. Queries are represented as pseudo-document vectors formed from weighted combinations of terms, and documents with supra-threshold cosine values are returned. initial tests find this completely automatic method for retrieval to be promising.

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12,443 citations

Additional excerpts

...We consider replacing the bag-of-words variables created using LSA with guided LDA features (version 4), as both of these sets of features are based on some natural language processing of movie descriptions....
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...Features Version 1 Version 2 Version 3 Version 4 Version 5 Intercept P P P P P Average critic rating P P P P Average user score P P P P Production budget P P P P Widest release P P P P Widest release2 P P P P Domestic box office P P P P MPAA rating P P P P Run time P P P P Competition P P P P Star power P P P P DVD release timing P P P P DVD sales rank P P P P Genres P P Content variables P P Semantic variables P P Bag-of-words variables from LSA P Guided LDA topic weights P P DIC 492.91 406.50 371.65 232.02 280.28 In-sample hit rate 62.09% 71.78% 76.30% 88.21% 85.08% Out-of-sample hit rate 61.67% 66.44% 67.94% 70.32% 71.19% Notes: Each column corresponds to one set of features....
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...Replacing the unsupervised features based on LSA with guided LDA features results in significant improvement in in-sample and out-ofsample fit (version 4 vs. version 3)....
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...It is a probabilistic version of LSA, thus enabling likelihoodbased inference....
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...We find that for both the bagged-CART model based on Eliashberg, Hui, and Zhang (2007) and the kernelbased model from Eliashberg, Hui, and Zhang (2014), performance is improved when guided LDA features are included instead of the LSA bag-of-words variables....
[...]

Journal Article•DOI•

Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions

[...]

Gediminas Adomavicius¹, Alexander Tuzhilin•Institutions (1)

University of Minnesota¹

01 Jun 2005-IEEE Transactions on Knowledge and Data Engineering

TL;DR: This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches.

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Abstract: This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multicriteria ratings, and a provision of more flexible and less intrusive types of recommendations.

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9,873 citations

"Extracting Features of Entertainmen..." refers background or methods in this paper

...However, this approach also leads to a very large set of features, leading to a curse of dimensionality (Adomavicius and Tuzhilin 2005). are driven at least in part by the alignment of their psychological profile with the psychological themes featured in the product....
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...However, this approach also leads to a very large set of features, leading to a curse of dimensionality (Adomavicius and Tuzhilin 2005)....
[...]
...Approaches for leveraging panel data in the media and entertainment industry have been classified into three categories (Adomavicius and Tuzhilin 2005): pure collaborative approaches, in which a user’s behavior is predicted on the basis of past behavior of similar users; content-based approaches,…...
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Journal Article•DOI•

Matrix Factorization Techniques for Recommender Systems

[...]

Yehuda Koren¹, Robert M. Bell, Chris Volinsky•Institutions (1)

Yahoo!¹

01 Aug 2009-IEEE Computer

TL;DR: As the Netflix Prize competition has demonstrated, matrix factorization models are superior to classic nearest neighbor techniques for producing product recommendations, allowing the incorporation of additional information such as implicit feedback, temporal effects, and confidence levels.

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Abstract: As the Netflix Prize competition has demonstrated, matrix factorization models are superior to classic nearest neighbor techniques for producing product recommendations, allowing the incorporation of additional information such as implicit feedback, temporal effects, and confidence levels

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9,583 citations

"Extracting Features of Entertainmen..." refers background or methods in this paper

...Popular collaborative approaches include variants of neighborhood-based collaborative filtering (CF; Breese, Heckerman, and Kadie 1998; Linden, Smith, and York 2003) and latent factor models (Koren, Bell, and Volinsky 2009)....
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...In particular, CF is a very popular approach for predicting consumption at the individual level and recommending entertainment products to consumers (Koren, Bell, and Volinsky 2009; Linden, Smith, and York 2003)....
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