Recommendation systems leverage future internet services to predict personalized recommendations for products, services, media entities or other offerings. Based on the research and development of the FIcontent 2 initiative, we introduce an approach to compensate Cold Start and Sparsity Problems by analyzing semantics of external textual data, in terms of comments from social networks as well as item reviews from product and rating services. Thereby sentiment analysis and semantic keyword extraction approaches are explained and evaluated by using preliminary implementations. The mined data is transferred into, so called, preference ontologies describing the users interest in automatic analyzed topics and subsequently mapped to the propertie...
Traditionally, recommender systems have relied on user preference data (such as ratings) and product...
In recommender systems, the cold-start problem is a common challenge. When a new item has no ratings...
With the rapid proliferation of online social networks, the information overload problem becomes inc...
Some of the known issues of recommendation algorithms are a result of the so called "Cold Start Prob...
Poster Papers of ISCRAM-Med 2016 - Third International Conference on Information Systems for Crisis ...
Poster Papers of ISCRAM-Med 2016 - Third International Conference on Information Systems for Crisis ...
Poster Papers of ISCRAM-Med 2016 - Third International Conference on Information Systems for Crisis ...
Poster Papers of ISCRAM-Med 2016 - Third International Conference on Information Systems for Crisis ...
Poster Papers of ISCRAM-Med 2016 - Third International Conference on Information Systems for Crisis ...
Social media and the development of web 2.0 encourage the user to participate more interactively in ...
A recommender system aims to provide users with personalized online product or service recommendatio...
This paper reports on a generalizable system model design that analyzes the unstructured customer re...
The study of sentiment analysis on social media posts can be used to analyse human emotions towards ...
The amount of information and users has been increasing at a remarkable rate in recent years. This i...
Traditionally, recommender systems have relied on user preference data (such as ratings) and product...
Traditionally, recommender systems have relied on user preference data (such as ratings) and product...
In recommender systems, the cold-start problem is a common challenge. When a new item has no ratings...
With the rapid proliferation of online social networks, the information overload problem becomes inc...
Some of the known issues of recommendation algorithms are a result of the so called "Cold Start Prob...
Poster Papers of ISCRAM-Med 2016 - Third International Conference on Information Systems for Crisis ...
Poster Papers of ISCRAM-Med 2016 - Third International Conference on Information Systems for Crisis ...
Poster Papers of ISCRAM-Med 2016 - Third International Conference on Information Systems for Crisis ...
Poster Papers of ISCRAM-Med 2016 - Third International Conference on Information Systems for Crisis ...
Poster Papers of ISCRAM-Med 2016 - Third International Conference on Information Systems for Crisis ...
Social media and the development of web 2.0 encourage the user to participate more interactively in ...
A recommender system aims to provide users with personalized online product or service recommendatio...
This paper reports on a generalizable system model design that analyzes the unstructured customer re...
The study of sentiment analysis on social media posts can be used to analyse human emotions towards ...
The amount of information and users has been increasing at a remarkable rate in recent years. This i...
Traditionally, recommender systems have relied on user preference data (such as ratings) and product...
Traditionally, recommender systems have relied on user preference data (such as ratings) and product...
In recommender systems, the cold-start problem is a common challenge. When a new item has no ratings...
With the rapid proliferation of online social networks, the information overload problem becomes inc...