Smart Billing Using Content-Based Recommender Systems Based on Fingerprint

Fog computing-assisted path planning for smart shopping

“… Type PoS Rec PP Nav. Customer Comment Killamsetty, et al Javali and Bhavanishankar, Naveenprabu, et al Ayoola et al and Gunasagar and Balachander [ 6 , 17 , 26 , 27 , 37 ] SSC × × × All Reduces POS time, but cashier is still needed Gupte et al and Yewatkar, et al [ 19 , 51 ] SSC × × All Recommendation is limited to a single shop Pangasa and Aggarwal, Gupte et al and Yewatkar et al [ 19 , 39 , 51 ] SSC × All Locates an item at a time Paul et al [ 41 ] SSC × × All Recommendation is limited to a single shop Yanfu et al and Mittal et al [ 34 , 50 ] SSC, Biometric × ...…”

“…It helps record each customer’s shopping history. The SSC-V3 in [ 50 ] captures the customer’s face and then uses face identification to access their account on the shop’s server, while [ 34 ] uses fingerprint. The cart uses the customer’s consumption interest data and the shop’s available products to generate a list of recommended products for him.…”

Exploiting relational tag expansion for dynamic user profile in a tag-aware ranking recommender system

“…The neighbourhood-based technique is most prevalent among different CF approaches, and a key point is to discover a suitable neighbourhood size. The content filtering recommendation approach [26,33,34] is based on the idea that the features of resources can be useful in generating interesting recommendations for users [24]. This approach intends to recommend resources similar to those a target user has liked in the past.…”

The Distribution of Etiology and Clinical Presentations of Birth Defects: A Preliminary Assessment of China Birth Cohort Study

“…In early ages, collaborative filtering-based methods [5,6,44,62] are primarily used to model the user's behavior patterns from the user-item interactions. Later on, with the introduction of user and item side information into recommendation systems, content-based recommendation [36,37,40,53,58] and knowledge-based recommendation [2,8,16,18] have gained attention due to their ability to provide personalized recommendations.…”