Mining personality traits from social messages for game recommender systems

Yang, Hsin-Chang; Huang, Zi-Rui

doi:10.1016/j.knosys.2018.11.025

Cited by 47 publications

(27 citation statements)

References 25 publications

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“…For example, [108] propose to model the automobile purchase intentions of customers based on their hobbies and personality. [106] have developed a system for recommending games to players based on personality which is modelled from their chats with other players. -Word polarity detection: Personality detection can be exploited for word polarity disambiguation in sentiment lexicons, as the same concept can convey different meaning to different types of people.…”

Section: Applicationsmentioning

confidence: 99%

Recent trends in deep learning based personality detection

et al. 2019

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Recently, the automatic prediction of personality traits has received a lot of attention. Specifically, personality trait prediction from multimodal data has emerged as a hot topic within the field of affective computing. In this paper, we review significant machine learning models which have been employed for personality detection, with an emphasis on deep learning-based methods. This review paper provides an overview of the most popular approaches to automated personality detection, various computational datasets, its industrial applications, and state-of-the-art machine learning models for personality detection with specific focus on multimodal approaches. Personality detection is a very broad and diverse topic: this survey only focuses on computational approaches and leaves out psychological studies on personality detection.

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Section: Applicationsmentioning

confidence: 99%

Recent trends in deep learning based personality detection

et al. 2019

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“…Individuals with similar personality traits would have common interest and tastes in choosing and buying goods (Farnadi et al, 2016;Nunes and Hu, 2012). The use of personality traits of people in RSs improves the quality of recommendations and customer experience (Fern andez-Tob ıas et al, 2016;Yang and Huang, 2019), and previous researches have shown that the personality-based CF works significantly better than the conventional CF (Dunn et al, 2009;Fern andez-Tob ıas et al, 2016;Karumur et al, 2018;Nunes and Hu, 2012;Roffo, 2016;Tkal ci c et al, 2010;Vinciarelli and Mohammadi, 2014;Wu and Chen, 2015;Yang and Huang, 2019).…”

Section: Personality In Recommender Systemsmentioning

confidence: 99%

“…Different classifications of personality traits have been put forward by different scholars and psychologists (Karumur et al, 2018;Yang and Huang, 2019). One of the most common and accepted classifications is the one proposed by McCrae and Costa (1991), which divides personality traits into five dimensions (Big Five) including extroversion, neuroticism, openness to experience, agreeableness and conscientiousness (McCrae and Costa, 2003;Vinciarelli and Mohammadi, 2014).…”

Section: Personality In Recommender Systemsmentioning

confidence: 99%

“…Therefore, these people usually like fashion clothing with new brands and diverse colors which have images, artworks and so on (Dunn et al, 2009;Zhang et al, 2017). The reason is that this personality type tends to experience new options in every field (Nunes and Hu, 2012;Yang and Huang, 2019).…”

Section: Personality In Recommender Systemsmentioning

confidence: 99%

“…There are many models for describing personality traits, among which Big Five (BF) is a highly comprehensive and popular personality model (Tkal ci c et al, 2010;Yang and Huang, 2019) that describes people's personality in terms of five traits, namely extroversion, neuroticism, openness to experience, agreeableness and conscientiousness (Farnadi et al, 2016;Vinciarelli and Mohammadi, 2014). These personality traits play a decisive role in the attitude and behavior of individuals such as their taste of clothing (John and Robins, 1993;Onori et al, 2016).…”

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An effective recommender system based on personality traits, demographics and behavior of customers in time context

Khodabandehlou

Golpayegani

Rahman

2020

DTA

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PurposeImproving the performance of recommender systems (RSs) has always been a major challenge in the area of e-commerce because the systems face issues such as cold start, sparsity, scalability and interest drift that affect their performance. Despite the efforts made to solve these problems, there is still no RS that can solve or reduce all the problems simultaneously. Therefore, the purpose of this study is to provide an effective and comprehensive RS to solve or reduce all of the above issues, which uses a combination of basic customer information as well as big data techniques.Design/methodology/approachThe most important steps in the proposed RS are: (1) collecting demographic and behavioral data of customers from an e-clothing store; (2) assessing customer personality traits; (3) creating a new user-item matrix based on customer/user interest; (4) calculating the similarity between customers with efficient k-nearest neighbor (EKNN) algorithm based on locality-sensitive hashing (LSH) approach and (5) defining a new similarity function based on a combination of personality traits, demographic characteristics and time-based purchasing behavior that are the key incentives for customers' purchases.FindingsThe proposed method was compared with different baselines (matrix factorization and ensemble). The results showed that the proposed method in terms of all evaluation measures led to a significant improvement in traditional collaborative filtering (CF) performance, and with a significant difference (more than 40%), performed better than all baselines. According to the results, we find that our proposed method, which uses a combination of personality information and demographics, as well as tracking the recent interests and needs of the customer with the LSH approach, helps to improve the effectiveness of the recommendations more than the baselines. This is due to the fact that this method, which uses the above information in conjunction with the LSH technique, is more effective and more accurate in solving problems of cold start, scalability, sparsity and interest drift.Research limitations/implicationsThe research data were limited to only one e-clothing store.Practical implicationsIn order to achieve an accurate and real-time RS in e-commerce, it is essential to use a combination of customer information with efficient techniques. In this regard, according to the results of the research, the use of personality traits and demographic characteristics lead to a more accurate knowledge of customers' interests and thus better identification of similar customers. Therefore, this information should be considered as a solution to reduce the problems of cold start and sparsity. Also, a better judgment can be made about customers' interests by considering their recent purchases; therefore, in order to solve the problems of interest drifts, different weights should be assigned to purchases and launch time of products/items at different times (the more recent, the more weight). Finally, the LSH technique is used to increase the RS scalability in e-commerce. In total, a combination of personality traits, demographics and customer purchasing behavior over time with the LSH technique should be used to achieve an ideal RS. Using the RS proposed in this research, it is possible to create a comfortable and enjoyable shopping experience for customers by providing real-time recommendations that match customers' preferences and can result in an increase in the profitability of e-shops.Originality/valueIn this study, by considering a combination of personality traits, demographic characteristics and time-based purchasing behavior of customers along with the LSH technique, we were able for the first time to simultaneously solve the basic problems of CF, namely cold start, scalability, sparsity and interest drift, which led to a decrease in significant errors of recommendations and an increase in the accuracy of CF. The average errors of the recommendations provided to users based on the proposed model is only about 13%, and the accuracy and compliance of these recommendations with the interests of customers is about 92%. In addition, a 40% difference between the accuracy of the proposed method and the traditional CF method has been observed. This level of accuracy in RSs is very significant and special, which is certainly welcomed by e-business owners. This is also a new scientific finding that is very useful for programmers, users and researchers. In general, the main contributions of this research are: 1) proposing an accurate RS using personality traits, demographic characteristics and time-based purchasing behavior; 2) proposing an effective and comprehensive RS for a “clothing” online store; 3) improving the RS performance by solving the cold start issue using personality traits and demographic characteristics; 4) improving the scalability issue in RS through efficient k-nearest neighbors; 5) Mitigating the sparsity issue by using personality traits and demographic characteristics and also by densifying the user-item matrix and 6) improving the RS accuracy by solving the interest drift issue through developing a time-based user-item matrix.

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