Results 21 to 30 of about 23,764 (310)

Promoting cold-start items in recommender systems. [PDF]

PLoS ONE, 2014
As one of the major challenges, cold-start problem plagues nearly all recommender systems. In particular, new items will be overlooked, impeding the development of new products online.
Jin-Hu Liu, Tao Zhou, Zi-Ke Zhang, Zimo Yang, Chuang Liu, Wei-Min Li   +5 more
doaj   +1 more source

Quantum Recommendation Systems

CoRR, 2016
A recommendation system uses the past purchases or ratings of n products by a group of m users, in order to provide personalized recommendations to individual users. The information is modeled as an m \times n preference matrix which is assumed to have a good rank-k approximation, for a small constant k. In this work, we present a quantum algorithm for
Kerenidis, Iordanis, Prakash, Anupam
openaire   +5 more sources

Trustworthy Recommender Systems

CoRR, 2022
Recommender systems (RSs) aim to help users to effectively retrieve items of their interests from a large catalogue. For a quite long period of time, researchers and practitioners have been focusing on developing accurate RSs. Recent years have witnessed an increasing number of threats to RSs, coming from attacks, system and user generated noise ...
Shoujin Wang, Xiuzhen Zhang 0001, Yan Wang 0002, Huan Liu 0001, Francesco Ricci 0001   +4 more
openaire   +2 more sources

Assessment Methods for Evaluation of Recommender Systems: A Survey

Foundations of Computing and Decision Sciences, 2021
The recommender system (RS) filters out important information from a large pool of dynamically generated information to set some important decisions in terms of some recommendations according to the user’s past behavior, preferences, and interests.
Kuanr Madhusree, Mohapatra Puspanjali
doaj   +1 more source

Optimization of Recommender Systems Using Particle Swarms

Ingeniería, 2023
Background: Recommender systems are one of the most widely used technologies by electronic businesses and internet applications as part of their strategies to improve customer experiences and boost sales.
Nancy Yaneth Gelvez Garcia, Jesús Gil-Ruíz, Jhon Fredy Bayona-Navarro   +2 more
doaj   +1 more source

Hands on Explainable Recommender Systems with Knowledge Graphs

, 2022
The goal of this tutorial is to present the RecSys community with recent advances on explainable recommender systems with knowledge graphs. We will first introduce conceptual foundations, by surveying the state of the art and describing real-world ...
Fenu G., Balloccu G., Boratto L., Marras M.   +3 more
core   +1 more source

Recommender Systems Evaluator: A Framework for Evaluating the Performance of Recommender Systems [PDF]

, 2021
Recommender systems are filters that suggest products of interest to customers, which may positively impact sales. Nowadays, there is a multitude of algorithms for recommender systems, and their performance varies widely.
Tardiole Kuehne, Bruno, Batista, Bruno G., Peixoto, Maycon L.M., Reiff-Marganiec, Stephan, dos Santos, Paulo V.G., Paulo V. G. dos Santos, Bruno G. Batista, Moreira, Edmilson Marmo, Edmilson Marmo Moreira, Leite, Dionisio M., Bruno Tardiole Kuehne, Dionisio M. Leite, Stephan Reiff-Marganiec, Maycon L. M. Peixoto   +13 more
core   +1 more source

Local Model-Agnostic Explanations for Black-box Recommender Systems Using Interaction Graphs and Link Prediction Techniques.

International Journal of Interactive Multimedia and Artificial Intelligence, 2023
Explanations in recommender systems are a requirement to improve users’ trust and experience. Traditionally, explanations in recommender systems are derived from their internal data regarding ratings, item features, and user profiles.
Marta Caro Martínez, Guillermo Jiménez Díaz, Juan A. Recio García   +2 more
doaj   +1 more source

Users’ Responsiveness to Persuasive Techniques in Recommender Systems

Frontiers in Artificial Intelligence, 2021
Understanding user’s behavior and their interactions with artificial-intelligent-based systems is as important as analyzing the performance of the algorithms used in these systems.
Alaa Alslaity, Thomas Tran
doaj   +1 more source

Typology of Personalization in Recommender Systems [PDF]

مدیریت نوآوری و راهبردهای عملیاتی, 2022
Purpose: With the development of science and technology, large volumes of structured, semi-structured, and unstructured data are generated daily at breakneck speeds from various sources.
Marziyeh Nourahmadi, Hojjatollah Sadeqi
doaj   +1 more source