Suchergebnisse - "Recommender Systems"

Social Identity Theory and Algorithmic Bias: Ingroup and Outgroup Acrophily in Recommender Systems.

Autoren: Carrasco-Farré, Carlos Grimaldi, Didier Torrens, Marc et al.

Quelle: Journal of Management Information Systems. 2025, Vol. 42 Issue 4, p1017-1054. 38p.

Schlagwörter: *ALGORITHMIC bias, *RECOMMENDER systems, *SENTIMENT analysis, POLARIZATION (Social sciences), GROUP identity, INGROUPS (Social groups)

Editorial Introduction.

Autoren: Zwass, Vladimir¹ (AUTHOR) zwass@fdu.edu

Quelle: Journal of Management Information Systems. 2025, Vol. 42 Issue 4, p983-986. 4p.

Schlagwörter: *RECOMMENDER systems, *INFORMATION storage & retrieval systems, *ELECTRONIC commerce, GENERATIVE artificial intelligence, FAKE news, ETHICAL problems, COMPUTER software security

Too Many or Too Few? Information Cues in Recommender Systems and Consequences for Search and Purchase Behavior.

Autoren: Fang, Xing Kim, SunAh Chintagunta, Pradeep K.

Quelle: Journal of Marketing. Jan2026, Vol. 90 Issue 1, p9-28. 20p.

Schlagwörter: *RECOMMENDER systems, *CONSUMER behavior, *SHOPPING, *ELECTRONIC commerce, *PRICES, *PRODUCT reviews, PROMPTS (Psychology), INFLUENCE, SEARCHING behavior

Generative Artificial Intelligence (GenAI)-Based Recommender Addressing Contribution Pollution and Information Cacophony on Digital Platforms.

Autoren: Malgonde, Onkar S.¹ (AUTHOR) omalgonde@ncsu.edu

Quelle: Journal of Management Information Systems. 2025, Vol. 42 Issue 2, p457-490. 34p.

Schlagwörter: *RECOMMENDER systems, *INFORMATION networks, *POLLUTION, GENERATIVE artificial intelligence, DIGITAL technology

Envisioning Recommendations on an LLM-Based Agent Platform.

Autoren: Zhang, Jizhi Bao, Keqin Wang, Wenjie et al.

Quelle: Communications of the ACM. May2025, Vol. 68 Issue 5, p48-57. 10p.

Schlagwörter: *INTELLIGENT agents, *RECOMMENDER systems, *TRAVEL agents, LANGUAGE models, GENERATIVE pre-trained transformers, HUMAN-artificial intelligence interaction

Consumer preferences in user- vs. item-based recommender systems for search and experience products.

Autoren: Choi, Youngtae Kim, Junga

Quelle: Journal of Marketing Management. Feb2025, Vol. 41 Issue 1/2, p84-110. 27p.

Schlagwörter: *RECOMMENDER systems, *RISK aversion, *ARTIFICIAL intelligence, *CONSUMERS, *CONSUMER preferences, PERCEPTION (Philosophy)

Beyond Black-Box: Structuring Landing Page Recommender Systems Using Predicted Intents.

Autoren: Wang, Yuyan Banerjee, Cheenar Chucri, Samer et al.

Quelle: Working Papers (Faculty) - Stanford Graduate School of Business. Oct2025, p1-62. 62p.

Schlagwörter: *RECOMMENDER systems, *CONSUMER behavior, *ARTIFICIAL intelligence, WEB personalization, PREDICTION models

Reviews & Products: YOUTUBE (Web resource)

Algorithmic recommendations in the everyday life of young people: imaginaries of agency and resources.

Autoren: Jylhä, Ville Hirvonen, Noora Haider, Jutta

Quelle: Information, Communication & Society. Dec2025, Vol. 28 Issue 15, p2641-2657. 17p.

Schlagwörter: *RECOMMENDER systems, *QUALITATIVE research, *INFORMATION architecture, *INFORMATION resources, *POWER (Social sciences), SELF-expression, AGENT (Philosophy), YOUNG adults

People: GIDDENS, Anthony, 1938-

Can psychographics mitigate over-specialization in recommender-driven consumer markets? Evidence from recommender systems based simulation experiment.

Autoren: Huang, Yinghui Dong, Yuhang Li, Weiqing et al.

Quelle: Management System Engineering. 11/24/2025, Vol. 4 Issue 1, p1-22. 22p.

Schlagwörter: *RECOMMENDER systems, *CONSUMER preferences, *MARKET saturation, PSYCHOGRAPHICS, FIVE-factor model of personality, VALUE orientations

Sequence-Enhanced Graph Neural Networks for Session-Based Recommendation.

Autoren: Hu, Xiaoli Liu, Weizhao Zhang, Wenxiang et al.

Quelle: International Journal of Software Engineering & Knowledge Engineering. Dec2025, Vol. 35 Issue 12, p1789-1809. 21p.

Schlagwörter: *RECOMMENDER systems, *ONLINE shopping, *ARTIFICIAL neural networks, GRAPH neural networks

Inflated Recommendations.

Autoren: Peitz, Martin Sobolev, Anton

Quelle: RAND Journal of Economics (Wiley-Blackwell). Nov2025, p1. 18p. 4 Illustrations.

Schlagwörter: *WELFARE economics, *RECOMMENDER systems, *MONOPOLIES, *CONSUMERS, STATISTICAL bias

Uncovering the role of weak ties in implicit networks of influence: a network analysis on recommendation algorithms' neighborhood.

Autoren: Baccelloni, Angelo Mazzú, Marco Francesco Ricotta, Francesco et al.

Quelle: European Journal of Marketing. 2025, Vol. 59 Issue 12, p2725-2762. 38p.

Schlagwörter: *RECOMMENDER systems, *INTERPERSONAL relations, *ELECTRONIC commerce, *WORD of mouth advertising, SOCIAL influence, INFORMATION dissemination, IMPLICIT learning

From experimentation to strategy: the ambidextrous balancing act of developing news recommender systems.

Autoren: Vandenbroucke, Hanne Smets, Annelien

Quelle: Journal of Media Business Studies. Nov2025, p1-28. 28p. 4 Illustrations.

Schlagwörter: *RECOMMENDER systems, *ORGANIZATIONAL ambidexterity, *KNOWLEDGE management, *INNOVATION adoption, *STAKEHOLDER analysis

A session-based recommendation method for user-centric new product design.

Autoren: Wu, Jun Lan, Xinyi Chen, Xianyi

Quelle: Journal of Engineering Design. Oct2025, p1-23. 23p. 6 Illustrations.

Schlagwörter: *PRODUCT design, *RECOMMENDER systems, *DATA structures, *CONSUMER preferences, DATA quality, MACHINE learning, USER-centered system design

Optimization-driven enhancements in recommendation systems: A computational approach.

Autoren: Manaloor, Rakshith Joseph Roy, Pradeep Kumar Singh, Sunil Kumar

Quelle: Journal of Industrial & Management Optimization. Nov2025, Vol. 21 Issue 11, p1-23. 23p.

Schlagwörter: *RECOMMENDER systems, DIFFERENTIAL evolution, GENETIC algorithms, PARTICLE swarm optimization, OPTIMIZATION algorithms

MOSAIC: multimodal multistakeholder-aware visual art recommendation.

Autoren: Yilma, Bereket A. Leiva, Luis A.

Quelle: User Modeling & User-Adapted Interaction. Dec2025, Vol. 35 Issue 4, p1-33. 33p.

Schlagwörter: *RECOMMENDER systems, *STAKEHOLDER theory, *STAKEHOLDER analysis, ART, EVALUATION methodology, HETEROGENEITY, MULTIMODAL user interfaces, MOSAICS (Art)

Application of collaborative filtering algorithm with improved trust degree in clothing personalization and customization designs.

Autoren: Meng, Niu¹ (AUTHOR) hellonm2023@126.com

Quelle: International Journal of Clothing Science & Technology. 2025, Vol. 37 Issue 6, p1044-1061. 18p.

Schlagwörter: *RECOMMENDER systems, *CLOTHING industry, *DIGITAL transformation, TRUST, CUSTOM-made clothing, CUSTOM design

Seasonality‐Aware, Positional, and Topological‐Guided GNN (SPT‐GNN) for Movie Recommendation.

Autoren: Özden, Cevher Özcan, Alper

Quelle: Computational Intelligence. Oct2025, Vol. 41 Issue 5, p1-11. 11p.

Schlagwörter: *RECOMMENDER systems, GRAPH neural networks, KNOWLEDGE representation (Information theory), KNOWLEDGE graphs, TOPOLOGICAL property, TRANSFER of training

Graph Neural Network‐Based Online Collaborative Filtering Using Transductive Node Embeddings.

Autoren: Szűcs, Gábor Kiss, Richárd

Quelle: Computational Intelligence. Oct2025, Vol. 41 Issue 5, p1-11. 11p.

Schlagwörter: *RECOMMENDER systems, GRAPH neural networks, MATRIX decomposition

Uncovering Recommendation Serendipity with Objective Data-driven Factor Investigation.

Autoren: Jiang, Wenjun Li, Song Li, Xueqi et al.

Quelle: ACM Transactions on Information Systems. Nov2025, Vol. 43 Issue 6, p1-33. 33p.

Schlagwörter: *RECOMMENDER systems, *QUANTITATIVE research, SERENDIPITY, LITERATURE, AMBIGUITY