Personalized Shopping Assistance Service at Ubiquitous Shop Space

Sae-Ueng, Somkiat; Pinyapong, Sineenard; Ogino, Akihiro; Kato, Toshikazu

doi:10.1109/waina.2008.287

Cited by 11 publications

(2 citation statements)

References 4 publications

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“…• The user's past or actual behavior history that includes for example previous visiting behaviors for location based systems [24], [3]; • Web browsing history/clicks [2] • Previous reading patterns for news recommender systems [27], [28], [29] Sae-Ueng et al [30] analysed the user's behavior log for shopping assistance using a digital camera and RFID sensors 6 . The system recommended information about a product according to the user's behavior classified under five states: Standing, Viewing, Touching, Carrying, and Fitting.…”

Section: A Proactive Recommender Systemsmentioning

confidence: 99%

Situation assessment for non-intrusive recommendation

Akermi

Faiz

2018

2018 12th International Conference on Research Challenges in Information Science (RCIS)

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With the rapid growth of mobile applications, the user is increasingly confronted with a lot of information and tend to reject notifications sent by applications installed within his/her mobile device. This rejection affects the performance of many systems, especially proactive recommender systems. Therefore, it is no longer enough for a recommender system to determine what to recommend according to users' needs, but it also has to deal with the risk of disturbing the user during the recommendation process. We believe that the several embedded applications within the user's device along with other parameters could help understand and assess the user's interruptibility in some situations. In this paper, we address intrusiveness within a proactive recommendation approach that makes use of the user's context and the applications embedded within the user's mobile device in order to assess the intrusiveness level of a given situation before recommending.

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Section: A Proactive Recommender Systemsmentioning

confidence: 99%

Situation assessment for non-intrusive recommendation

Akermi

Faiz

2018

2018 12th International Conference on Research Challenges in Information Science (RCIS)

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“…In this line of research, Sae‐Ueng et al (2008) focus on personalized shopping assistance by using personal behavior log analysis on ubiquitous shop space. The authors classify the possible user behaviors ( physical context) in five classes: “standing,” “viewing,” “touching,” “carrying,” and “fitting,” where the last behavior refers to a consumer using a mirror to match a certain object with his appearance.…”

Section: Key Cars Applicationsmentioning

confidence: 99%

Context‐Aware Recommender Systems

et al. 2011

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Context-aware recommender systems (CARS) generate more relevant recommendations by adapting them to the specific contextual situation of the user. This article explores how contextual information can be used to create more intelligent and useful recommender systems. It provides an overview of the multifaceted notion of context, discusses several approaches for incorporating contextual information in recommendation process, and illustrates the usage of such approaches in several application areas where different types of contexts are exploited. The article concludes by discussing the challenges and future research directions for context-aware recommender systems.

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