TWIN: Personality-based Intelligent Recommender System

Roshchina, Alexandra; Cardiff, John; Rosso, Paolo

doi:10.3233/ifs-141484

Cited by 39 publications

(28 citation statements)

References 25 publications

(40 reference statements)

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“…The evaluation measures like precision and recall in classification and information retrieval can be used for testing the performance of SA. To evaluate the SA quality of Watson NLU, the sentiment test data were used from the Center for Machine Learning and Intelligent Systems (CMLIS) at the University of California (Kotzias et al, 2015), NLP at Cornell University (Pang et al, 2002), and TripAdvisor data (Roshchina et al, 2015). The requests were sent to Watson NLU API to evaluate the sentiment output metrics of these test data.…”

Section: Methodsmentioning

confidence: 99%

Using Machine Learning for Analyzing Sentiment Orientations Toward Eight Countries

Guo

Zhang

2020

SAGE Open

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By using machine learning technique, this article presents sentiment and concept analyses on 48,043 articles published in The Economist from 1991 through 2016. The Economist is one of the world’s most influential political and economic magazines. The article analyzes and compares the magazine’s sentiment orientations toward the Group of Seven’s ingroup member countries (Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States) and its outgroup member country China. The sentiment analyses are performed on and compared between different periods of Clinton’s, Bush’s, and Obama’s administrations in the United States; Major’s, Blair’s, Brown’s, and Cameron’s cabinets in the United Kingdom; and Kohl’s, Schröder’s, and Merkel’s in Germany. The relationship between China hosting the Olympic Games or its growing economic power and the magazine’s sentiment orientations toward the country is examined. The concept analysis on the articles with extreme positivity or negativity shows that there is no difference between the ingroup and outgroup members in the topics covered in The Economist.

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Section: Methodsmentioning

confidence: 99%

Using Machine Learning for Analyzing Sentiment Orientations Toward Eight Countries

Guo

Zhang

2020

SAGE Open

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“…On the other hand, in eBay users are also entitled to write reviews about products, and additionally, they can write comments about purchase and sale transactions, since eBay is an e-commerce site. Trip Advisor dataset was extracted as part of the Twin Persona Research Project [41], while eBay dataset was obtained in September 2015 using crawler techniques. Reputation hr /(hr +nhr ), where hr is the total number of reviews set as helpful by others users at least once, and nhr is the total number of reviews that were not set as helpful at least once A text corpus is compatible with our model if it contains: (i) data about users; (ii) texts written by these users; and (iii) data that characterizes the user's trust.…”

Section: Proposed Modelmentioning

confidence: 99%

Inferring Trust Using Personality Aspects Extracted from Texts

Granatyr

Gomes

Dias

et al. 2019

2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)

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Trust mechanisms are considered the logical protection of software systems, preventing malicious people from taking advantage or cheating others. Although these concepts are widely used, most applications in this field do not consider affective aspects to aid in trust computation. Researchers of Psychology, Neurology, Anthropology, and Computer Science argue that affective aspects are essential to human's decisionmaking processes. So far, there is a lack of understanding about how these aspects impact user's trust, particularly when they are inserted in an evaluation system. In this paper, we propose a trust model that accounts for personality using three personality models: Big Five, Needs, and Values. We tested our approach by extracting personality aspects from texts provided by two online human-fed evaluation systems and correlating them to reputation values. The empirical experiments show statistically significant better results in comparison to nonpersonality-wise approaches.

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“…Combining this approach with a rating-based CF technique, the authors showed signifi-cant improvements over the baseline of considering only ratings data. Finally, in [64] Roshchina presented an approach that extracts five factors profiles by analyzing hotel reviews written by users, and incorporates these profiles into a nearest neighbor algorithm to enhance personalized recommendations.…”

Section: Personality-based Collaborative Filteringmentioning

confidence: 99%

“…Moreover, with respect to previous work, the experimental study presented here has been conducted on much larger datasets composed of "likes", which are positive only evaluations, rather than ratings, in several domains. Specifically, as described in Section 4, our dataset consisted of 159,551 users and 16,303 items in the movie, music and book domains, while in [32,33] the considered data set contains only 111 users, in [51] and [64] it is around 100, and in [72] 52; and all of these data sets contain only a very limited number of items.…”

Section: Personality-based Collaborative Filteringmentioning

confidence: 99%

Alleviating the new user problem in collaborative filtering by exploiting personality information

Fernández-Tobías

Braunhofer

Elahi

et al. 2016

User Model User-Adap Inter

110

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Esta es la versión de autor del artículo publicado en: This is an author produced version of a paper published in: In this paper we analyze viable solutions to the new user problem in collaborative filtering that are based on the exploitation of user personality information: (a) personality-based collaborative filtering, which directly improves the recommendation prediction model by incorporating user personality information; (b) personality-based active learning, which utilizes personality information for identifying additional useful preference data in the target recommendation domain to be elicited from the user; and (c) personality-based cross-domain recommendation, which exploits personality information to better use user preference data from auxiliary domains which can be used to compensate the lack of user preference data in the target domain. We benchmark the effectiveness of these methods on large datasets that span several domains, namely movies, music and books. Our results show that personality-aware methods achieve performance improvements that range from 6% to 94% for users completely new to the system, while increasing the novelty of the recommended items by 3% to 40% with respect to the non-personalized popularity baseline. We also discuss the limitations of our approach and the situations in which the proposed methods can be better applied, hence providing guidelines for researchers and practitioners in the field.

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TWIN: Personality-based Intelligent Recommender System

Cited by 39 publications

References 25 publications

Using Machine Learning for Analyzing Sentiment Orientations Toward Eight Countries

Using Machine Learning for Analyzing Sentiment Orientations Toward Eight Countries

Inferring Trust Using Personality Aspects Extracted from Texts

Alleviating the new user problem in collaborative filtering by exploiting personality information

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