A Social Influence Approach for Group User Modeling in Group Recommendation Systems

2016 ◽  
Vol 31 (5) ◽  
pp. 40-48 ◽  
Author(s):  
Junpeng Guo ◽  
Yanlin Zhu ◽  
Aiai Li ◽  
Qipeng Wang ◽  
Weiguo Han
Keyword(s):  
Social Influence ◽  
User Modeling ◽  
Recommendation Systems ◽  
Group Recommendation

A Social Influence Approach for Group User Modeling in Group Recommendation Systems

2017 ◽  
pp. 1-1
Author(s):  
Junpeng Guo ◽  
Yanlin Zhu ◽  
Aiai Li ◽  
Qipeng Wang ◽  
Weiguo Han
Keyword(s):  
Social Influence ◽  
User Modeling ◽  
Recommendation Systems ◽  
Group Recommendation

A Movie E-Shop Recommendation Model Based on Web Usage and Ontological Data

2011 ◽  
pp. 51-68
Author(s):  
Andreas Aresti ◽  
Penelope Markellou ◽  
Ioanna Mousourouli ◽  
Spiros Sirmakessis ◽  
Athanasios Tsakalidis
Keyword(s):  
Artificial Intelligence ◽  
User Modeling ◽  
Web Mining ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Model Based ◽  
Web Usage ◽  
One To One ◽  
Online Shops

Recommendation systems are special personalization tools that help users to find interesting information and services in complex online shops. Even though today’s e-commerce environments have drastically evolved and now incorporate techniques from other domains and application areas such as Web mining, semantics, artificial intelligence, user modeling, and profiling setting up a successful recommendation system is not a trivial or straightforward task. This chapter argues that by monitoring, analyzing, and understanding the behavior of customers, their demographics, opinions, preferences, and history, as well as taking into consideration the specific e-shop ontology and by applying Web mining techniques, the effectiveness of produced recommendations can be significantly improved. In this way, the e-shop may upgrade users’ interaction, increase its usability, convert users to buyers, retain current customers, and establish long-term and loyal one-to-one relationships.

scholarly journals A Time-Aware Hybrid Approach for Intelligent Recommendation Systems for Individual and Group Users

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Zhao Huang ◽  
Pavel Stakhiyevich
Keyword(s):  
Hybrid Approach ◽  
Recommendation Systems ◽  
User Preferences ◽  
User Preference ◽  
User Profiles ◽  
Group Recommendation ◽  
Group Recommendations ◽  
Time Awareness ◽  
Hybrid Recommendation ◽  
Time Aware

Although personal and group recommendation systems have been quickly developed recently, challenges and limitations still exist. In particular, users constantly explore new items and change their preferences throughout time, which causes difficulties in building accurate user profiles and providing precise recommendation outcomes. In this context, this study addresses the time awareness of the user preferences and proposes a hybrid recommendation approach for both individual and group recommendations to better meet the user preference changes and thus improve the recommendation performance. The experimental results show that the proposed approach outperforms several baseline algorithms in terms of precision, recall, novelty, and diversity, in both personal and group recommendations. Moreover, it is clear that the recommendation performance can be largely improved by capturing the user preference changes in the study. These findings are beneficial for increasing the understanding of the user dynamic preference changes in building more precise user profiles and expanding the knowledge of developing more effective and efficient recommendation systems.

Sign in / Sign up

Export Citation Format

Share Document