Factors influencing willingness to provide personal information for personalized recommendations

Kim, Min Sung; Kim, Seong-Cheol

doi:10.1016/j.chb.2018.06.031

Cited by 57 publications

(47 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This work proposed a TV program recommendation system for multiple viewers (group) based on merging user profiles. Similarly, the study in [11] proposed the merging of multiple preferences to improve recommendation results. RecTime [36], proposed a real-time recommender for an online broadcasting system, which considers a user's preferences and time factors simultaneously.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Factors Affecting the Performance of Recommender Systems in a Smart TV Environment

2019

View full text Add to dashboard Cite

The recommender systems are deployed on the Web for reducing cognitive overload. It uses different parameters, such as profile information, feedbacks, history, etc., as input and recommends items to a user or group of users. Such parameters are easy to predict and calculate for a single user on a personalized device, such as a personal computer or smartphone. However, watching the Web contents on a smart TV is significantly different from other connected devices. For example, the smart TV is a multi-user, lean-back supported device, and normally enjoyed in groups. Moreover, the performance of a recommender system is questionable due to the dynamic interests of groups in front of a smart TV. This paper discussed in detail the existing recommender system approaches in the context of smart TV environment. Moreover, it highlights the issues and challenges in existing recommendations for smart TV viewer(s) and presents some research opportunities to cope with these issues. The paper further reports some overlooked factors that affect the recommendation process on a smart TV. A subjective study of viewers’ watching behavior on a smart TV is also presented for validating these factors. Results show that apart from all technological advancement, the viewers are enjoying smart TV as a passive, lean-back device, and mostly used for watching live channels and videos on the big screen. Furthermore, in most households, smart TV is enjoyed in groups as a shared device which creates hurdles in personalized recommendations. This is because predicting the group members and satisfying each member is still an issue. The findings of this study suggest that for precise and relevant recommendations on smart TVs, the recommender systems need to adapt to the varying watching behavior of viewer(s).

show abstract

Section: Literature Reviewmentioning

confidence: 99%

“…Such approaches are neither viable nor accurate to recommend items to the exact viewer(s) of smart TV. Hence, in the context of smart TV viewing scenarios, content filtering, channel recommendation, scheduling programs, and personalized viewership are challenging opportunities [11].…”

Section: Introductionmentioning

confidence: 99%

Factors Affecting the Performance of Recommender Systems in a Smart TV Environment

2019

View full text Add to dashboard Cite

show abstract

“…The assessment of social benefits and privacy cost (well-known as Privacy Calculus theory) is a complex task that users should complete for effective communication in social networks [29]. This theoretical privacy calculus framework has been proven in the OSN domain by a large number of research works [151,220,54,138]. However, these research works have mostly assessed the users' perceptions and preferences towards the privacy risks and socials benefits of information disclosure in a general way.…”

Section: Privacy Calculus Theory and Privacy Decisionsmentioning

confidence: 99%

“…For instance, to measure the cost of self-disclosure decisions, [109] defined information privacy in four taxonomic dimensions: collection, unauthorized secondary use, improper access, and errors. Conversely, [138] consider that risk appraisal emerges from the vulnerability and the severity perceived in OSN decisions. These privacy concerns generally reflect a personal predisposition to worry about privacy and are therefore antecedent to risk beliefs, which are defined as the expectation of losses related to selfdisclosure [151].…”

Section: Privacy Cost and Overexposurementioning

confidence: 99%

“…As a consequence, the users' data becomes personal, but how does the sensitivity value of data in disclosure actions contribute to the users' cost-benefit? [138] assessed different types of personal data and how it impacts benefits and risks in video recommendation systems. They found a significant impact on all of the personal types regarding risk appraisal.…”

Section: Sensitivity Of the Messagementioning

confidence: 99%

See 1 more Smart Citation

Measures of Privacy Protection on Social Environments

Bordera¹

View full text Add to dashboard Cite

La dedicació d'aquesta tesi és obligatòria perquè sinó més d'un/a em mata. ha, ha, ha Amb aquestes línies vull expressar la meua gratitud... ...a la meua família pel seu constant suport. A ma mare per tot el que es preocupa per mi, que no passi fam, fred, i una infinitud de coses mes. A mon pare i a la meua germana, que encara que siguin molt pesats i no paren de demanar-me favors relacionats amb la "informàtica", em donen suport. I sobretot agrair-li'ho a la meua parella Bea, a la que li dec moltes coses, que ha hagut d'aguantar-me en els bons i mals moments (perquè encara que no ho paregui, jo sóc més bitxo que ella). No oblidi agrair-li'ho també als meus avis que ens han deixat i a mà iaio Vicent al que no visito el que és suficient però que vull moltíssim. ...als amics que m'acompanyen sempre. Als amics d' Alzira (tu també Ivan, guapo!), que des que era un nano, han compartit en mi tantíssimes coses. Des de les vesprades de mega-construccions amb Legos (Eh Aitor!), tardes jugant a videojocs, i desfilades militars als corredors de casa Julian a birres, mandanga, i nits de festa amb els meus cosins preferits (Aidan e Ian). També recordar als més pesats/cabuts com Frank amb el que he discutit molt de temes inversemblants a la part que graciosos. Als amics de la universitat que continuen allí (Javi i Cristian) i als que es troben treballant a l'empresa privada (Tomàs, Nico, Jose2, Jovi, Pedro, Marco, etc.) que compartissen amb mi la passió per la informàtica i pel deport ("¡VUAMOS!" Elias). ...als companys de l'equip d'investigació del VRAIN amb els quals vaig començar aquest camí i han finançat els meus treballs. A Sole i Javi que foren els meus mentors als inicis i dels quals he aprés un fum. Al Boss, Vicent Botti, que sempre m'ha oferit oportunitats al VRAIN i que hem brindà l'oportunitat de realitzar aquesta tesi. I especialment a Ana i Elena que han sigut les millores directores de tesi, les quals m'han ajudat en tot moment, inclús quan no es trobaven bé o després d'haver donat a llum a un preciós bebé (Felicitats Elena!).

show abstract

Consumer engagement with AI‐powered voice assistants: A behavioral reasoning perspective

Açikgöz

Perez‐Vega

Okumuş

et al. 2023

Psychology and Marketing

View full text Add to dashboard Cite

This study draws upon Behavioral Reasoning Theory and the Technology Acceptance Model to investigate consumer engagement with AI‐powered voice assistants. The study creates a theoretical model to examine the effects of reasons for and reasons against using voice assistants. This research exemplifies attitudes towards using voice assistants and willingness to provide personal information as key constructs. The current study tests data from 491 voice assistant users via mTurk, and we utilize a multimethod analysis scheme including the partial least squares technique and the fuzzy set qualitative comparative analysis approach to provide an assessment of the proposed model. Findings indicated that while privacy cynicism has a negative impact upon the attitude towards using voice assistants, the countervailing values of trust, perceived usefulness, and ease of use have off‐setting positive impact. The study also highlights the moderating role of habit on the behavioral mechanisms driving consumer engagement via willingness to provide privacy information. This research advances the emerging literature on voice assistants with respect to privacy‐related factors driving consumer engagement.

show abstract

Factors influencing willingness to provide personal information for personalized recommendations

Cited by 57 publications

References 25 publications

Factors Affecting the Performance of Recommender Systems in a Smart TV Environment

Factors Affecting the Performance of Recommender Systems in a Smart TV Environment

Measures of Privacy Protection on Social Environments

Consumer engagement with AI‐powered voice assistants: A behavioral reasoning perspective

Contact Info

Product

Resources

About