An Adaptive, Context-Aware, and Stacked Attention Network-Based Recommendation System to Capture Users Temporal Preference

Jung-Hsien Chiang ◽  
Chung-Yao Ma ◽  
Chi-Shiang Wang ◽  
Pei-Yi Hao
2019 ◽  
Vol 31 (12) ◽  
pp. 9295-9305 ◽  
Jiaxu Leng ◽  
Ying Liu ◽  
Shang Chen

2013 ◽  
Vol 479-480 ◽  
pp. 1213-1217
Mu Yen Chen ◽  
Ming Ni Wu ◽  
Hsien En Lin

This study integrates the concept of context-awareness with association algorithms and social media to establish the Context-aware and Social Recommendation System (CASRS). The Simple RSSI Indoor Localization Module (SRILM) locates the user position; integrating SRILM with Apriori Recommendation Module (ARM) provides effective recommended product information. The Social Media Recommendation Module (SMRM) connects to users social relations, so that the effectiveness for users to gain product information is greatly enhanced. This study develops the system based on actual context.

Maryam Jallouli ◽  
Sonia Lajmi ◽  
Ikram Amous

In the last decade, social-based recommender systems have become the best way to resolve a user's cold start problem. In fact, it enriches the user's model by adding additional information provided from his social network. Most of those approaches are based on a collaborative filtering and compute similarities between the users. The authors' preliminary objective in this work is to propose an innovative context aware metric between users (called contextual influencer user). These new similarities are called C-COS, C-PCC and C-MSD, where C refers to the category. The contextual influencer user model is integrated into a social based recommendation system. The category of the items is considered as the most pertinent context element. The authors' proposal is implemented and tested within the food dataset. The experimentation proved that the contextual influencer user measure achieves 0.873, 0.874, and 0.882 in terms of Mean Absolute Error (MAE) corresponding to C-cos, C-pcc and C-msd, respectively. The experimental results showed that their model outperforms several existing methods.

Constantinos Costa ◽  
Xiaoyu Ge ◽  
Evan McEllhenney ◽  
Evan Kebler ◽  
Panos K. Chrysanthis ◽  

Sign in / Sign up

Export Citation Format

Share Document