Case Base Elicitation for a Context-Aware Recommender System

2018 ◽  
pp. 170-185
Author(s):  
Jose Luis Jorro-Aragoneses ◽  
Guillermo Jimenez-Díaz ◽  
Juan Antonio Recio-García ◽  
Belén Díaz-Agudo
Keyword(s):  
Recommender System ◽  
Context Aware ◽  
Case Base

scholarly journals Context-Aware Recommender System: A Review of Recent Developmental Process and Future Research Direction

2017 ◽  
Vol 7 (12) ◽  
pp. 1211 ◽  
Author(s):  
Khalid Haruna ◽  
Maizatul Akmar Ismail ◽  
Suhendroyono Suhendroyono ◽  
Damiasih Damiasih ◽  
Adi Pierewan ◽  
...  
Keyword(s):  
Recommender System ◽  
Developmental Process ◽  
Research Direction ◽  
Future Research ◽  
Context Aware ◽  
Future Research Direction ◽  
System A

scholarly journals Context-aware Music Recommender System Based on Implicit Feedback

2021 ◽  
Vol 36 (1) ◽  
pp. WI2-D_1-10
Author(s):  
Yasufumi Takama ◽  
Jing-cheng Zhang ◽  
Hiroki Shibata
Keyword(s):  
Recommender System ◽  
Implicit Feedback ◽  
Context Aware

A Recommendation Model for Energy-Efficient Data Mining Services Based on Data Properties

2021 ◽  
Author(s):  
Zainab Al-Zanbouri
Keyword(s):  
Data Mining ◽  
Energy Consumption ◽  
Web Services ◽  
Recommender System ◽  
Energy Efficient ◽  
Contextual Information ◽  
Three Dimensions ◽  
Context Aware ◽  
Rating Data ◽  
Recommendation Accuracy

Currently, there is a big increase in the usage of data analytics applications and services because of the growth in the data produced from different sources. The QoS properties such as response time and latency of these services are important factors to decide which services to select. As a result of IT expansion, energy consumption has become a big issue. Therefore, establishing a QoS-based web service recommender system that considers energy consumption as one of the essential QoS properties represents a significant step towards selecting the energy efficient web services. This dissertation presents an experimental study on energy consumption levels and latency behavior collected from a set of data mining web services running on different datasets. Our study shows that there is a strong relation between the dataset properties and the QoS properties. Based on the findings from this study, a recommender system is built which considers three dimensions (user, service, dataset). The energy consumption values of candidate services invoked by specific users can be predicted for a given dataset. Afterwards, these services can be ranked according to their predicted energy values and presented to users. We propose three approaches to build our recommender system and we treat it as a context-aware recommendation problem. The dataset is considered as contextual information and we use a context-aware matrix factorization model to predict energy values. In the first approach, we adopt the pre-filtering model where the contextual information serves as a query for filtering relevant rating data. In the second approach, we propose a new method for the pre-filtering implementation. Finally, in the last approach, we adopt the contextual modeling method and we explore different ways of representing dataset information as contextual factors to investigate their impacts on the recommendation accuracy. We compare the proposed approaches with the baseline approaches and the results show the effectiveness of the proposed ones. Also, we compare the performance of the three approaches to discover the best-fit approach when being measured using different metrics. Both prediction and recommendation accuracy of the proposed approaches are significantly better than the baseline models.

A New Contextual Influencer User Measure to Improve the Accuracy of Recommender System

2018 ◽  
Vol 9 (4) ◽  
pp. 38-51
Author(s):  
Maryam Jallouli ◽  
Sonia Lajmi ◽  
Ikram Amous
Keyword(s):  
Social Network ◽  
Recommender System ◽  
Recommendation System ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
User Model ◽  
Context Aware ◽  
Additional Information ◽  
Context Element ◽  
Cold Start Problem

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.

A Hybrid Context Aware Recommender System with Combined Pre and Post-Filter Approach

2019 ◽  
Vol 10 (4) ◽  
pp. 1-14 ◽  
Author(s):  
Mugdha Sharma ◽  
Laxmi Ahuja ◽  
Vinay Kumar
Keyword(s):  
Performance Improvement ◽  
Recommender System ◽  
Contextual Information ◽  
Hybrid Approach ◽  
Experimental Setup ◽  
Context Aware ◽  
Contextual Modeling ◽  
Filter Approach ◽  
Post Filter

The domain of context aware recommender approaches has made substantial advancement over the last decade, but many applications still do not include contextual information while providing recommendations. Contextual information is crucial for various application areas and should not be ignored. There are generally three algorithms which can be used to include context and those are: pre-filter approach, post-filter approach, and contextual modeling. Each of the algorithms has their own drawbacks. The proposed approach modifies the post filter approach to rectify its shortcomings and combines it with the pre-filter approach based on the importance of contextual attribute provided by the user. The results of experimental setup also demonstrate that the proposed system improves the precision and ranking of the recommendations provided to user. With the help of this hybrid approach, the proposed system eliminates the problem of sparsity which is present in the pre-filter algorithm, and has performance improvement over the traditional post-filter approach.

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