Frequency-based similarity measure for context-aware recommender systems

2016 ◽  
Author(s):  
Mohammed Wasid ◽  
Vibhor Kant ◽  
Rashid Ali
Keyword(s):  
Recommender Systems ◽  
Similarity Measure ◽  
Context Aware

Context-Aware Recommender Systems

2012 ◽  
Vol 23 (1) ◽  
pp. 1-20 ◽  
Author(s):  
Li-Cai WANG ◽  
Xiang-Wu MENG ◽  
Yu-Jie ZHANG
Keyword(s):  
Recommender Systems ◽  
Context Aware

scholarly journals Context-aware recommender systems and cultural heritage: a survey

2021 ◽  
Author(s):  
Mario Casillo ◽  
Francesco Colace ◽  
Dajana Conte ◽  
Marco Lombardi ◽  
Domenico Santaniello ◽  
...  
Keyword(s):  
Cultural Heritage ◽  
Recommender Systems ◽  
Context Awareness ◽  
Technological Development ◽  
Context Aware ◽  
Human Beings ◽  
Context Analysis ◽  
Analysis Techniques ◽  
The Right ◽  
Technological Improvements

AbstractIn the Big Data era, every sector has adapted to technological development to service the vast amount of information available. In this way, each field has benefited from technological improvements over the years. The cultural and artistic field was no exception, and several studies contributed to the aim of the interaction between human beings and artistic-cultural heritage. In this scenario, systems able to analyze the current situation and recommend the right services play a crucial role. In particular, in the Recommender Systems field, Context-Awareness helps to improve the recommendations provided. This article aims to present a general overview of the introduction of Context analysis techniques in Recommender Systems and discuss some challenging applications to the Cultural Heritage field.

Using Topic Hierarchies with Privileged Information to Improve Context-Aware Recommender Systems

2014 ◽  
Author(s):  
Camila V. Sundermann ◽  
Marcos A. Domingues ◽  
Ricardo M. Marcacini ◽  
Solange O. Rezende
Keyword(s):  
Recommender Systems ◽  
Context Aware ◽  
Privileged Information

scholarly journals A New Similarity Measure for User-based Collaborative Filtering in Recommender Systems

2015 ◽  
Vol 14 (9) ◽  
pp. 6118-6128 ◽  
Author(s):  
T. Srikanth ◽  
M. Shashi
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Similarity Measure ◽  
Recommender System ◽  
Distance Measure ◽  
Current User ◽  
Challenging Problem ◽  
Popular Approach ◽  
Similarity Calculation

Collaborative filtering is a popular approach in recommender Systems that helps users in identifying the items they may like in a wagon of items. Finding similarity among users with the available item ratings so as to predict rating(s) for unseen item(s) based on the preferences of likeminded users for the current user is a challenging problem. Traditional measures like Cosine similarity and Pearson correlation’s correlation exhibit some drawbacks in similarity calculation. This paper presents a new similarity measure which improves the performance of Recommender System. Experimental results on MovieLens dataset show that our proposed distance measure improves the quality of prediction. We present clustering results as an extension to validate the effectiveness of our proposed method.

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