Deep Group Recommender System Model Based on User Trust

2020 ◽  
Author(s):  
Yulong Song ◽  
Wenming Ma ◽  
Tongtong Liu
Keyword(s):  
Recommender System ◽  
System Model ◽  
Model Based ◽  
User Trust ◽  
Group Recommender

An interpretable mechanism for personalized recommendation based on cross feature

2021 ◽  
pp. 1-12
Author(s):  
Lv YE ◽  
Yue Yang ◽  
Jian-Xu Zeng
Keyword(s):  
Recommender System ◽  
Online Shopping ◽  
Side Information ◽  
Attention Mechanism ◽  
Personalized Recommendation ◽  
Model Based ◽  
Embedded Model ◽  
Feature Attention ◽  
Match Score

The existing recommender system provides personalized recommendation service for users in online shopping, entertainment, and other activities. In order to improve the probability of users accepting the system’s recommendation service, compared with the traditional recommender system, the interpretable recommender system will give the recommendation reasons and results at the same time. In this paper, an interpretable recommendation model based on XGBoost tree is proposed to obtain comprehensible and effective cross features from side information. The results are input into the embedded model based on attention mechanism to capture the invisible interaction among user IDs, item IDs and cross features. The captured interactions are used to predict the match score between the user and the recommended item. Cross-feature attention score is used to generate different recommendation reasons for different user-items.Experimental results show that the proposed algorithm can guarantee the quality of recommendation. The transparency and readability of the recommendation process has been improved by providing reference reasons. This method can help users better understand the recommendation behavior of the system and has certain enlightenment to help the recommender system become more personalized and intelligent.

A model-based approach to measuring denitrification and greenhouse gas production in lakes

2021 ◽  
Author(s):  
Juliet Falco Ajambo-Doherty
Keyword(s):  
Greenhouse Gas ◽  
Atmospheric Pressure ◽  
Schmidt Number ◽  
Spatial Scales ◽  
Gas Production ◽  
System Model ◽  
Biogeochemical Processes ◽  
Model Based ◽  
Temporal And Spatial ◽  
System Metabolism

An existing whole-system model based on changes in dissolved N₂ concentration was modified for lentic systems. Field validations carried out at Christie Lake in Dundas, ON and Turtle Pond in Stoney Creek, ON (Canada). New model inputs included air temperature, atmospheric pressure, relative humidity, wind velocity, and Schmidt number. Mont Carlo analysis was integrated into the model to better constrain error in model estimates of denitrification, whole-system metabolism, and greenhouse gas production. Denitrification rates ranged from -419-4415 µmol N.m-².h-¹ in Christie Lake and from 10-74 µmol N.m-².h-¹ in Turtle Pond. N₂O production ranged from 915-10,635 nmol N.m-².h-¹ in Christie Lake and from -344-131 nmol N.m-².h-¹ in Turtle Pond. The whole-system model allows for the examination of biogeochemical processes at ecologically significant temporal and spatial scales.

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