Solving Cold-Start Problem by Combining Personality Traits and Demographic Attributes in a User Based Recommender System

2017 ◽  
Vol 7 (5) ◽  
pp. 231-239 ◽  
Author(s):  
◽  
Zewengel Tilahun ◽  
Huang Dong Jun ◽  
Ammar Oad ◽  
◽  
...  
Keyword(s):  
Personality Traits ◽  
Recommender System ◽  
Cold Start ◽  
Demographic Attributes ◽  
Cold Start Problem

scholarly journals A DNN-Based Cross-Domain Recommender System for Alleviating Cold-Start Problem in E-Commerce

2020 ◽  
Vol 1 ◽  
pp. 194-206
Author(s):  
Hanxin Wang ◽  
Daichi Amagata ◽  
Takuya Makeawa ◽  
Takahiro Hara ◽  
Niu Hao ◽  
...  
Keyword(s):  
Recommender System ◽  
Cold Start ◽  
Cross Domain ◽  
Cold Start Problem

A recommender system to address the Cold Start problem for App usage prediction

2018 ◽  
Vol 10 (9) ◽  
pp. 2257-2268
Author(s):  
Di Han ◽  
Jianqing Li ◽  
Lei Yang ◽  
Zihua Zeng
Keyword(s):  
Recommender System ◽  
Cold Start ◽  
Cold Start Problem

scholarly journals HERS: Modeling Influential Contexts with Heterogeneous Relations for Sparse and Cold-Start Recommendation

2019 ◽  
Vol 33 ◽  
pp. 3830-3837 ◽  
Author(s):  
Liang Hu ◽  
Songlei Jian ◽  
Longbing Cao ◽  
Zhiping Gu ◽  
Qingkui Chen ◽  
...  
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
Real World ◽  
Cold Start ◽  
Data Sparsity ◽  
Real World Datasets ◽  
Item Item ◽  
Cold Start Problem

Classic recommender systems face challenges in addressing the data sparsity and cold-start problems with only modeling the user-item relation. An essential direction is to incorporate and understand the additional heterogeneous relations, e.g., user-user and item-item relations, since each user-item interaction is often influenced by other users and items, which form the user’s/item’s influential contexts. This induces important yet challenging issues, including modeling heterogeneous relations, interactions, and the strength of the influence from users/items in the influential contexts. To this end, we design Influential-Context Aggregation Units (ICAU) to aggregate the user-user/item-item relations within a given context as the influential context embeddings. Accordingly, we propose a Heterogeneous relations-Embedded Recommender System (HERS) based on ICAUs to model and interpret the underlying motivation of user-item interactions by considering user-user and item-item influences. The experiments on two real-world datasets show the highly improved recommendation quality made by HERS and its superiority in handling the cold-start problem. In addition, we demonstrate the interpretability of modeling influential contexts in explaining the recommendation results.

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