CFGAN: A generic collaborative filtering framework based on generative adversarial networks
Generative Adversarial Networks (GAN) have achieved big success in various domains
such as image generation, music generation, and natural language generation. In this …
such as image generation, music generation, and natural language generation. In this …
User preference mining based on fine-grained sentiment analysis
User preference mining is an application of data mining that attracts increasing attention.
Although most of the existing user preference mining methods achieved significant …
Although most of the existing user preference mining methods achieved significant …
Addressing the cold-start problem in recommender systems based on frequent patterns
A Panteli, B Boutsinas - Algorithms, 2023 - mdpi.com
Recommender systems aim to forecast users' rank, interests, and preferences in specific
products and recommend them to a user for purchase. Collaborative filtering is the most …
products and recommend them to a user for purchase. Collaborative filtering is the most …
Collaborative adversarial autoencoders: An effective collaborative filtering model under the GAN framework
Recently, deep learning has become a preferred choice for performing tasks in diverse
application domains such as computer vision, natural language processing, sensor data …
application domains such as computer vision, natural language processing, sensor data …
Enriching artificial intelligence explanations with knowledge fragments
J Rožanec, E Trajkova, I Novalija, P Zajec, K Kenda… - Future internet, 2022 - mdpi.com
Artificial intelligence models are increasingly used in manufacturing to inform decision
making. Responsible decision making requires accurate forecasts and an understanding of …
making. Responsible decision making requires accurate forecasts and an understanding of …
Learning to recommend diverse items over implicit feedback on PANDOR
In this paper, we present a novel and publicly available dataset for online recommendation
provided by Purch1. The dataset records the clicks generated by users of one of Purch's …
provided by Purch1. The dataset records the clicks generated by users of one of Purch's …
Learning explicit user interest boundary for recommendation
The core objective of modelling recommender systems from implicit feedback is to maximize
the positive sample score sp and minimize the negative sample score sn, which can usually …
the positive sample score sp and minimize the negative sample score sn, which can usually …
Modeling user preferences in online stores based on user mouse behavior on page elements
S SadighZadeh, M Kaedi - Journal of Systems and Information …, 2022 - emerald.com
Purpose Online businesses require a deep understanding of their customers' interests to
innovate and develop new products and services. Users, on the other hand, rarely express …
innovate and develop new products and services. Users, on the other hand, rarely express …
Generalization bounds for learning under graph-dependence: A survey
Traditional statistical learning theory relies on the assumption that data are identically and
independently distributed (iid). However, this assumption often does not hold in many real …
independently distributed (iid). However, this assumption often does not hold in many real …
From “Thumbs Up” to “10 out of 10”: Reconsidering Scalar Feedback in Interactive Reinforcement Learning
Learning from human feedback is an effective way to improve robotic learning in exploration-
heavy tasks. Compared to the wide application of binary human feedback, scalar human …
heavy tasks. Compared to the wide application of binary human feedback, scalar human …