Add to ORKGWe are investigating the problem of proposing serendipitous contents in a recommender system environment in order to discover latent interests and increase user satisfaction. Some results about our first experiments on contents and users clusterization in absence of meta-data will be presented
The development of information technology has stimulated an increasing number of researchers to inve...
Recommender rystem (RS) is created to solve the problem by recommending some items among a huge sele...
Most recommender algorithms are designed to suggest relevant items, but suggesting these items does ...
Recommender systems suggest items, such as movies or books, to users based on their interests. These...
Today recommenders are commonly used with various purposes, especially dealing with e-commerce and i...
Most recommender systems suggest items similar to a user profile, which results in boring recommenda...
Recommender systems are filters which suggest items or information that might be interesting to use...
Most recommender systems suggest items to a user that are popular among all users and similar to ite...
Widely used recommendation systems are mainly accuracy-oriented since they are based on item-based r...
Personalization techniques aim at helping people dealing with the ever growing amount of information...
Recommender systems are intelligent applications build to predict the rating or preference that a us...
Recommender Systems try to assist users to access complex information spaces regarding their long te...
Most recommender systems suggest items that are popular among all users and similar to items a user ...
In this paper, we propose a model to operationalise serendipity in content-based recommender systems...
Recommender systems enable users to discover items of interest from a large set of alternatives. Mos...
The development of information technology has stimulated an increasing number of researchers to inve...
Recommender rystem (RS) is created to solve the problem by recommending some items among a huge sele...
Most recommender algorithms are designed to suggest relevant items, but suggesting these items does ...
Recommender systems suggest items, such as movies or books, to users based on their interests. These...
Today recommenders are commonly used with various purposes, especially dealing with e-commerce and i...
Most recommender systems suggest items similar to a user profile, which results in boring recommenda...
Recommender systems are filters which suggest items or information that might be interesting to use...
Most recommender systems suggest items to a user that are popular among all users and similar to ite...
Widely used recommendation systems are mainly accuracy-oriented since they are based on item-based r...
Personalization techniques aim at helping people dealing with the ever growing amount of information...
Recommender systems are intelligent applications build to predict the rating or preference that a us...
Recommender Systems try to assist users to access complex information spaces regarding their long te...
Most recommender systems suggest items that are popular among all users and similar to items a user ...
In this paper, we propose a model to operationalise serendipity in content-based recommender systems...
Recommender systems enable users to discover items of interest from a large set of alternatives. Mos...
The development of information technology has stimulated an increasing number of researchers to inve...
Recommender rystem (RS) is created to solve the problem by recommending some items among a huge sele...
Most recommender algorithms are designed to suggest relevant items, but suggesting these items does ...