Zobrazeno 1 - 10
of 414
pro vyhledávání: '"Pujari, P. K."'
Particle Swarm Optimization (PSO) has emerged as a powerful metaheuristic global optimization approach over the past three decades. Its appeal lies in its ability to tackle complex multidimensional problems that defy conventional algorithms. However,
Externí odkaz:
http://arxiv.org/abs/2312.09703
Recommender systems aim to enhance the overall user experience by providing tailored recommendations for a variety of products and services. These systems help users make more informed decisions, leading to greater user engagement with the platform.
Externí odkaz:
http://arxiv.org/abs/2308.04247
Autor:
Kagita, Venkateswara Rao, Singh, Anshuman, Kumar, Vikas, Neerudu, Pavan Kalyan Reddy, Pujari, Arun K, Bondugula, Rohit Kumar
Group recommender systems (GRS) are critical in discovering relevant items from a near-infinite inventory based on group preferences rather than individual preferences, like recommending a movie, restaurant, or tourist destination to a group of indiv
Externí odkaz:
http://arxiv.org/abs/2307.12034
Collaborative filtering (CF) has become a popular method for developing recommender systems (RSs) where ratings of a user for new items are predicted based on her past preferences and available preference information of other users. Despite the popul
Externí odkaz:
http://arxiv.org/abs/2306.13050
Publikováno v:
Acta Materialia (2024)
The role of point defects in temporal evolution of Cr-rich alpha-prime phase separation in binary Fe-20 at.% Cr alloy is elucidated by intercepting the long term (upto 1000 h at 773 K) aging at regular intervals and probing by a combination of atom p
Externí odkaz:
http://arxiv.org/abs/2205.03434
Recommender systems based on collaborative filtering play a vital role in many E-commerce applications as they guide the user in finding their items of interest based on the user's past transactions and feedback of other similar customers. Data Spars
Externí odkaz:
http://arxiv.org/abs/2203.13995
Traditional recommendation algorithms develop techniques that can help people to choose desirable items. However, in many real-world applications, along with a set of recommendations, it is also essential to quantify each recommendation's (un)certain
Externí odkaz:
http://arxiv.org/abs/2109.08949
Recommender systems(RS), especially collaborative filtering(CF) based RS, has been playing an important role in many e-commerce applications. As the information being searched over the internet is rapidly increasing, users often face the difficulty o
Externí odkaz:
http://arxiv.org/abs/2108.01473
We consider the problem of committee selection from a fixed set of candidates where each candidate has multiple quantifiable attributes. To select the best possible committee, instead of voting for a candidate, a voter is allowed to approve the prefe
Externí odkaz:
http://arxiv.org/abs/1901.10064
Multi-label learning is concerned with the classification of data with multiple class labels. This is in contrast to the traditional classification problem where every data instance has a single label. Due to the exponential size of output space, exp
Externí odkaz:
http://arxiv.org/abs/1812.09910