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pro vyhledávání: '"Kagita, Venkateswara Rao"'
We study approval-based committee voting in which a target number of candidates are selected based on voters' approval preferences over candidates. In contrast to most of the work, we consider the setting where voters express uncertain approval prefe
Externí odkaz:
http://arxiv.org/abs/2407.19391
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 emerged as one of the most prominent implementation strategies for building recommender systems. The key idea is to exploit the usage patterns of individuals to generate personalized recommendations. CF techniques, es
Externí odkaz:
http://arxiv.org/abs/2306.13887
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
Autor:
Neerudu, Pavan Kalyan Reddy, Oota, Subba Reddy, Marreddy, Mounika, Kagita, Venkateswara Rao, Gupta, Manish
Transformer-based pretrained models like BERT, GPT-2 and T5 have been finetuned for a large number of natural language processing (NLP) tasks, and have been shown to be very effective. However, while finetuning, what changes across layers in these mo
Externí odkaz:
http://arxiv.org/abs/2305.14453
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
Publikováno v:
In Knowledge-Based Systems 8 April 2024 289
Publikováno v:
In Expert Systems With Applications 15 March 2024 238 Part B
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