Personalized Recommendation System for Offline Shopping

Li, Pengbo; Zhang, Guisong; Li, Chao; Xie, Zhen

doi:10.1109/icalip.2018.8455252

Cited by 8 publications

(5 citation statements)

References 7 publications

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“…The RFID scanners used in shops are handled main ly by the cashiers and they are not designed to ensure specialized user experience. The Artificial Intelligence Reco mmendation Systems for shopping that exist do not function real-t ime and do not have a system in p lace that handle the shopping as well as the recommendations at a time [6]. Thus while individual technologies do exist in the domain, no product provides a comprehensive co mplete experience to the shopkeeper and the user.…”

Section: T Able I Industry Standards Rfid Standardmentioning

confidence: 99%

“…Customers can pay their bill through credit/debit cards. The bill of the user is stored on a local database and a constant log of the purchases can be kept and analysed when needed along with detailed interfacing with peripheral devices such as Zigbee, LCD and a micro controller [6] [13]. Such Smart Billing systems allo ws users to add items in the cart and upload them in an online application that can be paid for using online wallets [8] [1].…”

Section: B Literature Surveymentioning

confidence: 99%

See 1 more Smart Citation

Automated Shopping Cart Using RFID with a Collaborative Clustering Driven Recommendation System

Gupte

Rege

Hawa

et al. 2020

2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA)

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Recent advancements in technology have seen a significant reduction in human intervention. New technologies are introduced to replace outdated conventions where emphasis is put on the incorporation of Artificial Intelligence and Automation in our daily lives. With the current COVID-19 pandemic, reducing human interaction in everyday acti ons has now been deemed necessary. One of the most important sectors affected, the shopping industry, needs to adapt to meet government and social security standards. This paper addresses a S mart S hopping Cart S ystem where the entire shopping experience is automated and handled by the customer. It details a more efficient online mode of shopping which not only reduces the need for hands-on staff but also provides specialized recommendations to users using collaborative clustering to update the shopping experience and meet the demands of our time. The current shopping system has many limitations, and introduction of Radio-frequency identification (RFID) technology as the core identification mechanism can prove useful for applications such as security, safety and inventory management.

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Section: T Able I Industry Standards Rfid Standardmentioning

confidence: 99%

Section: B Literature Surveymentioning

confidence: 99%

Automated Shopping Cart Using RFID with a Collaborative Clustering Driven Recommendation System

Gupte

Rege

Hawa

et al. 2020

2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA)

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show abstract

“…Algorithm 1 illustrates the general framework for the KNN query over CPPse-index. Given an incoming social item v, our algorithm performs KNN query by three important steps: (1) compute the hash values based on the entity-category pairs contained in v, by which a set of extended signature trees are located (Lines 5-6); (2) generate pseudo-query based on the item and each located extended signature tree (Line 7); (3) select and rank the top-k relevant users (Lines [13][14][15][16][17][18][19][20][21][22]. We maintain a max-heap U k with size k as our output ranked list.…”

Section: The Relevance Between Them Can Be Computed By Plugging Statimentioning

confidence: 99%

“…Based on the evaluation objectives of recommendation, the previous social recommendation approaches can be classified into two categories, relevance-based [5], [20], [33], [40], [41] and diversity-based [7], [14], [19], [25], [36]. Relevance-based approaches identify the most similar items matched with a user predefined profile based on the present content and context features, producing a list of items relevant to the ones viewed by this user in the past.…”

Section: Introductionmentioning

confidence: 99%

Online Social Media Recommendation Over Streams

Zhou

Qin

et al. 2019

2019 IEEE 35th International Conference on Data Engineering (ICDE)

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As one of the most popular services over online communities, the social recommendation has attracted increasing research efforts recently. Among all the recommendation tasks, an important one is social item recommendation over high speed social media streams. Existing streaming recommendation techniques are not effective for handling social users with diverse interests. Meanwhile, approaches for recommending items to a particular user are not efficient when applied to a huge number of users over high speed streams. In this paper, we propose a novel framework for the social recommendation over streaming environments. Specifically, we first propose a novel Bi-Layer Hidden Markov Model (BiHMM) that adaptively captures the behaviors of social users and their interactions with influential official accounts to predict their long-term and short-term interests. Then, we design a new probabilistic entity matching scheme for effectively identifying the relevance score of a streaming item to a user. Following that, we propose a novel indexing scheme called CPPse-index for improving the efficiency of our solution. Extensive experiments are conducted to prove the high performance of our approach in terms of the recommendation quality and time cost.

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“…In this paper, we propose a novel recommender system that integrates with social network websites, like Facebook, Twitter and Yelp, to particularly deal with the cold-start problem. While users share information with friends, not only users facilitate the spread of messages and help promote the products or services or attract more consumers, but also the recommender system learns user preferences based on users or their friends' most recent posts and relevant shopping experiences [7,8]. According to such information, even in a cold-start condition without rating or review for new products or services, the recommender system still can make follow-up recommendations, and users can save time to find ideal targets.…”

mentioning

confidence: 99%

Accuracy improvements for cold-start recommendation problem using indirect relations in social networks

Tey

Lin

et al. 2021

J Big Data

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Recent advances in Internet applications have facilitated information spreading and, thanks to a wide variety of mobile devices and the burgeoning 5G networks, users easily and quickly gain access to information. Great amounts of digital information moreover have contributed to the emergence of recommender systems that help to filter information. When the rise of mobile networks has pushed forward the growth of social media networks and users get used to posting whatever they do and wherever they visit on the Web, such quick social media updates already make it difficult for users to find historical data. For this reason, this paper presents a social network-based recommender system. Our purpose is to build a user-centered recommender system to exclude the products that users are disinterested in according to user preferences and their friends' shopping experiences so as to make recommendations effective. Since there might be no corresponding reference value for new products or services, we use indirect relations between friends and “friends’ friends” as well as sentinel friends to improve the recommendation accuracy. The simulation result has proven that our proposed mechanism is efficient in enhancing recommendation accuracy.

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Personalized Recommendation System for Offline Shopping

Cited by 8 publications

References 7 publications

Automated Shopping Cart Using RFID with a Collaborative Clustering Driven Recommendation System

Automated Shopping Cart Using RFID with a Collaborative Clustering Driven Recommendation System

Online Social Media Recommendation Over Streams

Accuracy improvements for cold-start recommendation problem using indirect relations in social networks

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