In recent years, the textile and fashion industries have witnessed an enormous amount of
growth in fast fashion. On e-commerce platforms, where numerous choices are available, an …

In location-based social networks (LBSNs), extensive user check-in data incorporating user
preferences for location is collected through Internet of Things devices, including cell …

Recommender systems help users to find information that fits their preferences in an
overloaded search space. Collaborative filtering systems suffer from increasingly severe …

In the era of social media, exponential growth of information generated by online social
media and e-commerce applications demands expert and intelligent recommendation …

Knowledge graphs (KGs) can provide users with semantic information and relations among
numerous entities and nodes, which can greatly facilitate the performance of recommender …

Recommender system suggests a personalized recommendation by filtering the information
based on users interest. Nowadays, users like to purchase the best possible items and …

Recommender system (RS) can produce personalized service to users by analyzing their
historical information. User-based collaborative filtering (UBCF) approach is widely utilized …

Firms of all sizes are “joining the conversation” on social media platforms and increasingly
trademarking hashtags related to their products and brands. This added effort to protect …

Online recommender systems generally suffer from severe data sparsity problems, and this
are particularly prevalent in newly launched systems that do not have sufficient amounts of …

The collaborative filtering (CF) recommendation algorithm predicts the purchases of specific
users based on their characteristics and purchase history. This study empirically analyzes …