A novel mobile recommender system for indoor shopping

Fang, Bing; Liao, Stephen Shaoyi; Xu, Kaiquan; Cheng, Hao; Zhu, Chen; Chen, Huaping

doi:10.1016/j.eswa.2012.03.038

Cited by 46 publications

(29 citation statements)

References 16 publications

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“…use keywords for product search or web site views) or refer to the consumer preferences on promotion offers, advertisements, and product attributes (e.g. similarity, proximity, price, and reputation) (Brown and Sankaranarayanan, 2011;Fang et al, 2012;Guan et al, 2008;Kim et al, 2004;Kowatsch and Maass, 2010;Kurkovsky and Harihar, 2006;Kwon, 2006;Randell and Muller, 2000;Moukas et al, 2000;Olugbara et al, 2010;Yang et al, 2008;Zuva et al, 2012). Liou and Liu (2012) and Liu and Liou (2011) go one step further.…”

mentioning

confidence: 99%

Mobile shopping: a classification framework and literature review

Groß

2015

International Journal of Retail &Amp; Distribution Management

174

167

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If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. AbstractPurpose -The purpose of this paper is to classify and organize the accumulated knowledge about mobile shopping (m-shopping) as revealed in the present literature regarding retail. A classification framework has been applied, consisting of three categories: online distribution channels, advanced technology for in-store shopping, and technology perspectives. Design/methodology/approach -A term-based search method was applied in which the literature was restricted to peer-reviewed articles in English-language journals of a certain date and specific online databases. Finally, 81 peer-reviewed articles, published between 2000 and 2012 around the world, were taken into account. Findings -Considering the retail environment, the interest in m-shopping for both advanced technology for in-store shopping and for the online distribution channel has increased continuously over the last decade. Moreover, while studies have mostly explored the consumers' acceptance and reactions to m-shopping themes, the technology perspective is still being researched. Research limitations/implications -Due to the selected term-based search method, the number of identified publications is limited. Additionally, the selected framework requires a corresponding assignment to an explicitly stated topic, which was not always possible. Originality/value -As far as the author knows, this paper provides the first systematic review of m-shopping literature, which not only helps to organize retail-based literature, but also investigates significant gaps on this topic, thus facilitating future research.

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confidence: 99%

Mobile shopping: a classification framework and literature review

Groß

2015

International Journal of Retail &Amp; Distribution Management

174

167

View full text Add to dashboard Cite

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“…To the best of our knowledge, only a few literatures [13][14][15] address the problem of indoor POI recommendation based on user's trajectories. Specifically, Lin [13] proposed an indoor location system by regarding the stay time in certain shops as item rating.…”

Section: Indoor Poi Recommendationmentioning

confidence: 99%

“…Specifically, Lin [13] proposed an indoor location system by regarding the stay time in certain shops as item rating. Fang et al [15] mined customer's preference from WiFi RSSI patterns, that is, time spent in a store and check-in frequency of store, and then proposed a recommendation system for indoor shopping. Jin et al [14] proposed an indoor hotspots detecting method by considering user's interests in locations and the relationship between users and locations.…”

Section: Indoor Poi Recommendationmentioning

confidence: 99%

“…Existing indoor POI recommendation algorithms, that is, user-based [13] method or item-based method [15], will suffer serious data sparsity problem since numerous users only have few POI checkins. In addition, few historical trajectories cannot reflect user's POI preference and further infer reasonable User-POI relation and thus may result in cold-start problem in recommendation system.…”

Section: User-user Relation Inferencementioning

confidence: 99%

See 1 more Smart Citation

Location-Aware POI Recommendation for Indoor Space by Exploiting WiFi Logs

Zheng

Chen

et al. 2017

Mobile Information Systems

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Indoor shopping trajectories provide us with a new approach to understanding user's behaviour pattern in urban shopping mall, which can be derived from user-generated WiFi logs using indoor localization technology. In this paper, we propose a locationaware Point-of-Interest (POI) recommendation service in urban shopping mall that offers a user a set of indoor POIs by considering both personal interest and location preference. The POI recommendation service cannot only improve user's shopping experience but also help the store owner better understand user's shopping preference and intent. Specifically, the proposed method consists of two phases: offline modelling and online recommendation. The offline modelling phase is designed to learn user preference by mining his/her historical shopping trajectories. The online recommendation phase automatically produces top-recommended POIs based on the learnt preference. To demonstrate the utility of our proposed approach, we have performed a comprehensive experiment evaluation on a real-world dataset collected by 468 users over 33 days. The experimental results show that the proposed recommendation service achieves much better recommendation performance than several existing benchmark methods.

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“…[7] and [26] use RFID data for targeted advertising inside a retail store. [27] uses RFID data for predicting retail store sales.…”

Section: B Mining Rfid Datamentioning

confidence: 99%

A Framework for Mining RFID Data From Schedule-Based Systems

Ertek

Zhang

2017

IEEE Trans. Syst. Man Cybern, Syst.

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Abstract-A schedule-based system is a system that operates on or contains within a schedule of events and breaks at particular time intervals. Given RFID data from a schedule-based system, what set of actions and computations, and what type of data mining methods can be applied so that one can obtain actionable insights regarding the system and domain? The research goal of this paper is to answer this posed research question through the development of a framework that systematically produces actionable insights for a given schedule-based system. We show that through integrating appropriate data analysis methodologies as a unified framework, one can obtain many insights from even a very simple RFID dataset, which contains only very few fields. The developed framework is general, and is applicable to any schedule-based system, as long as it operates under a few basic assumptions. The types of insights are also general, and are formulated in the most abstract possible way. The applicability of the developed framework is illustrated through a case study, where real world data from a schedule-based system is analyzed using the introduced framework. Insights obtained include the profiling of entities and events, the interactions between entity and events, and the relations between events.

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A novel mobile recommender system for indoor shopping

Cited by 46 publications

References 16 publications

Mobile shopping: a classification framework and literature review

Mobile shopping: a classification framework and literature review

Location-Aware POI Recommendation for Indoor Space by Exploiting WiFi Logs

A Framework for Mining RFID Data From Schedule-Based Systems

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