Product Recommendation based on Shared Customer's Behaviour

Rodrigues, Fátima; Ferreira, Bruno M.

doi:10.1016/j.procs.2016.09.133

Cited by 28 publications

(22 citation statements)

References 13 publications

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“…In [5], authors proposed a hybrid recommendation system to surpass the difficulties in providing the right product based on customer preferences. The method combines context-based, data mining techniques and collaborative filtering.…”

Section: Related Workmentioning

confidence: 99%

Exploration of Collaboration Filtering Techniques for Product Recommendation

Kumar,

Madhavan

2020

IJEAT

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Today, recommendation system has been globally adopted as the most effective and reliable search engine for knowledge extraction in the field of education, economics and scientific research. Collaborative filtering is a proven techniques used in recommender system to make predictions or recommendations of the unknown preferences for users based on the known user preferences. In this paper, collaborative filtering task and their challenges are explored, study the different recommendation techniques and evaluate their performance using different metrics.

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Section: Related Workmentioning

confidence: 99%

Exploration of Collaboration Filtering Techniques for Product Recommendation

Kumar,

Madhavan

2020

IJEAT

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“…Recommender systems are usually classified based on how recommendations are made. Collaborative filtering makes recommendations based on items owned by users whose taste is similar to those of the given user (Rodriguez & Ferreira, 2016).…”

Section: Recommender Systemsmentioning

confidence: 99%

Seminar and Training Programs Recommender System for Faculty Members of Higher Education Institution

Paytaren

2020

IJCSR

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Purpose-This study aims to develop a personalized Recommender System that helps to address the problems encountered by the faculty members of Higher Education Institutions in the selection of Seminar and Training Programs (STP). Method-The researcher used the Descriptive Developmental Method of research to gather information relevant to the current problems and challenges encountered and used these to develop software that addresses the identified challenges. For the development of the software, the researcher adopted a step-wise approach defined in the Incremental Developmental Model. The level of acceptance of the developed system was evaluated by 24 faculty respondents. Results-The level of acceptance of the developed system was classified into functionality, reliability, and usability and the study garnered an evaluation score of 4.65, 4.67, and 4.67 respectively. The overall interpretation of the results of the evaluation is Highly Acceptable. Conclusion-The study created a system that provides seminars and training program recommendations. The developed recommender system was rated Highly Acceptable, respondents were very satisfied with the features of the system and agreed that it was functional, reliable, and usable. 360 Recommendations-This study reiterates the incorporation of the Seminar and Training Programs Recommender System in the Higher Education Institution to address the issues and challenges encountered by the faculty member in the selection of STP.

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“… For example, the most recent purchase is ranked higher (1 day ago) than the purchases made before that (1 month ago)  Most frequent customers are ranked higher (5) than the less frequent ones (1)  Also customers who spend more will be ranked higher (5) than the customers who spent less (1) This scheme is called RFM technique and this has been used in the system to ensure that correct customers are reached, and also for sending recommendations to the customer(s) with appropriate products [21]…”

Section: Calculation Of Rfm Scoresmentioning

confidence: 99%

“…Alternatively termed as 'hyper local marketing', proximity marketing targets valuable customers with targeted advertisements backed by the proximity of consumers (or devices) pertaining to a specific section [1]. This kindles them to take up a purchase decision in future.…”

Section: Introductionmentioning

confidence: 99%

IoT Based Approach for the Increased and Improved sales for the Brick and Mortar Stores

Giridhararajan¹

2020

IJATCSE

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The consistent use of smartphones in daily life has brought the wave of online shopping and digital marketing into action. Over the last few years, brick-and-mortar store owners have increasingly seen consumers migrate away from retail stores in favor of convenient and quickly accessible digital outlets. Many shoppers feel that, with the ease of smartphone and "one-click" shopping, browsing for products in a physical store is almost obsolete. However, what if the very same technology that caused this crisis in retail industry could be used to drive customers into their stores? Bluetooth Low Energy (BLE) beacons provide a great way to guide customers through a larger store and find the intended product. In addition to customer convenience, beacons are a powerful tool for storeowners to deliver targeted product recommendations. Thus, the overall solution consists of two components -Proximity sensing and Product recommendation systems. When a customer comes into the proximity of a particular section of a store, the recommender system generates total and personal recommendation for the customer. The matching entries between recommended products and products from the particular section are the final optimized recommendation delivered to the customer via mobile application. Product recommendations are powered by the purchase history data of Future group stores. That way a new lease of life can be brought to struggling brick-and-mortar stores.

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Product Recommendation based on Shared Customer's Behaviour

Cited by 28 publications

References 13 publications

Exploration of Collaboration Filtering Techniques for Product Recommendation

Exploration of Collaboration Filtering Techniques for Product Recommendation

Seminar and Training Programs Recommender System for Faculty Members of Higher Education Institution

IoT Based Approach for the Increased and Improved sales for the Brick and Mortar Stores

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