User Personality and the New User Problem in a Context-Aware Point of Interest Recommender System

Braunhofer, Matthias; Elahi, Mehdi; Ricci, Francesco

doi:10.1007/978-3-319-14343-9_39

Cited by 36 publications

(24 citation statements)

References 30 publications

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“…However, authors can introduce their biases in text (Recasens et al, 2013). Accurate prediction of the true user traits is important for applications such as recommender systems (Braunhofer et al, 2015) or medical diagnoses (Chattopadhyay et al, 2011). Influencing perceived traits, on the other hand, enables a whole different range of applications -for example, researchers demonstrated that the perceived demographics influence student attitude towards a tutor (Baylor and Kim, 2004;Rosenberg-Kima et al, 2008).…”

Section: Related Workmentioning

confidence: 99%

Analyzing Biases in Human Perception of User Age and Gender from Text

Flek¹,

Carpenter²,

Giorgi³

et al. 2016

Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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User traits disclosed through written text, such as age and gender, can be used to personalize applications such as recommender systems or conversational agents. However, human perception of these traits is not perfectly aligned with reality. In this paper, we conduct a large-scale crowdsourcing experiment on guessing age and gender from tweets. We systematically analyze the quality and possible biases of these predictions. We identify the textual cues which lead to miss-assessments of traits or make annotators more or less confident in their choice. Our study demonstrates that differences between real and perceived traits are noteworthy and elucidates inaccurately used stereotypes in human perception.

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Section: Related Workmentioning

confidence: 99%

Analyzing Biases in Human Perception of User Age and Gender from Text

Flek¹,

Carpenter²,

Giorgi³

et al. 2016

Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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show abstract

“…In a survey conducted to elicit tourists' preferences, Neidhardt et al (2015) use a picture based approach to address preferences on an emotional level. Braunhofer et al (2015) show that personality traits of the Big-5 model provide useful information for generating context-aware recommendations. They argue that personality trait data are relatively easy to collect and especially useful for ranking the recommendations in case of new users.…”

Section: Related Workmentioning

confidence: 98%

“…Travel Recommender Systems (TRSs) help to overcome the information load tourists may experience when they search for options, by providing users selected items that match their personal preferences (Braunhofer et al 2015). For this a critical element of TRSs is the ability to acquire the relevant information about preferences and needs of the user and identify the POIs that match his or her interests.…”

Section: Introductionmentioning

confidence: 99%

Estimating a latent-class user model for travel recommender systems

Arentze

Kemperman

Aksenov

2018

Inf Technol Tourism

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In determining the selection of sites to visit on a trip tourists have to trade-off attraction values against routing and time-use characteristics of points of interest (POIs). For recommending optimal personalized travel plans an accurate assessment of how users make these trade-offs is important. In this paper we report the results of a study conducted to estimate a user model for travel recommender systems. The proposed model is part of c-Space-a tour-recommender system for tourists on a city trip which uses the LATUS algorithm to find personalized optimal tours. The model takes into account a multi-attribute utility function of POIs as well as dynamic needs of persons on a trip. A stated choice experiment is designed where the current need is manipulated as a context variable and activity choice alternatives are varied. A random sample of 316 individuals participated in the on-line survey. A latent-class analysis shows that significant differences exist between tourists in terms of how they make the trade-offs between the factors and respond to needs. The estimation results provide the parameters of a multi-class user model that can be used for travel recommender systems.

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“…Several psychology-based studies have explored the relationship between personality traits and the types of the music people enjoy (Rentfrow & Gosling, 2003) and the manner in which music is consumed in everyday life (Chamorro-Premuzic & Furnham, 2007). Personality-based user modeling has also been proposed to improve recommender system performance (Onori, Micarelli, & Sansonetti, 2016;Braunhofer, Elahi, & Ricci, 2015;Braunhofer, Elahi, & Ricci, 2014;Hu & Pu, 2011; for a review, see Tkalcic & Chen, 2015). Onori et al (2016) explored methods of incorporating the Big Five personality traits into music recommender systems.…”

Section: Preference Characteristics In the Context Of Personalized Rementioning

confidence: 99%

Music discovery and revisiting behaviors of individuals with different preference characteristics: An experience sampling approach

Tang

Jhang

2019

Asso for Info Science & Tech

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A mobile device‐enabled experience sampling study was conducted in which 44 participants answered questions about their music experiences 5 times a day for 2 weeks. Data regarding 4 aspects of their music‐related psychological traits—“music involvement,” “musical identity,” “preference diversity,” and “preference openness”—were also collected through a background questionnaire. A classification of music access modes was proposed based on the circumstances that lead to a music listening experience. A mixed regression analysis revealed several significant interaction effects between psychological traits and the mode of music access on music enjoyment. Foremost among these was a positive interaction effect between preference openness and the playing of a known track triggered by musical cues, and that between preference diversity and exposure to new music played by others. Individuals with a strong musical identity tended to enjoy music played of their own volition without any apparent triggers. Furthermore, a multimodal logistic regression analysis also revealed the relationships between these psychological traits and the likelihood of different music access modes. Preference diversity significantly increased the likelihood of music listening triggered by need arousal. The results support the proposition that users' music‐related psychological traits should be considered in personalized recommendation strategies.

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User Personality and the New User Problem in a Context-Aware Point of Interest Recommender System

Cited by 36 publications

References 30 publications

Analyzing Biases in Human Perception of User Age and Gender from Text

Analyzing Biases in Human Perception of User Age and Gender from Text

Estimating a latent-class user model for travel recommender systems

Music discovery and revisiting behaviors of individuals with different preference characteristics: An experience sampling approach

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