Add to ORKGMost of the traditional recommendation algorithms are based on TF-IDF, a term-based weighting method. This paper proposes a new method for recommending news items based on the weighting of the occurrences of references to concepts, which we call Concept Frequency-Inverse Document Frequency (CFIDF). In an experimental setup we apply CF-IDF to a set of newswires in which we detect 1; 167 instances of a set of 65 concepts from a domain ontology. The proposed method yields significantly better results with respect to accuracy, recall, and F1 than the TF-IDF method we use as a basis for comparison
Content-based news recommendations are usually made by employing the cosine similarity and the TF-ID...
News items play an increasingly important role in the current business decision processes. Due to th...
AbstractTo deal with the challenge of information overload, in this paper, we propose a financial ne...
Most of the traditional recommendation algorithms are based on TF-IDF, a term-based weighting method...
When recommending news items, most of the traditional algorithms are based on TF-IDF, i.e., a term-b...
When recommending news items, most of the traditional algorithms are based on TF-IDF, i.e., a term-b...
Traditionally, content-based recommendation is performed using term occurrences, which are leveraged...
News item recommendation is commonly performed using the TF-IDF weighting technique in combination w...
As the usage of internet is increasing, we are getting more dependent on it in our daily life. The I...
Traditionally, content-based news recommendation is performed by means of the cosine similarity and ...
Recommending news items is traditionally done by term-based algorithms like TF-IDF. This paper conce...
Keywords: in information retrieval for decades. We propose a novel term weighting method based on wh...
Recommending news items is traditionally done by term-based algorithms like TF-IDF. This paper conce...
A set of related articles is a useful addition to the newly published news. Such news articles conta...
Recommending news items is traditionally done by term-based algorithms like TF-IDF. This paper conce...
Content-based news recommendations are usually made by employing the cosine similarity and the TF-ID...
News items play an increasingly important role in the current business decision processes. Due to th...
AbstractTo deal with the challenge of information overload, in this paper, we propose a financial ne...
Most of the traditional recommendation algorithms are based on TF-IDF, a term-based weighting method...
When recommending news items, most of the traditional algorithms are based on TF-IDF, i.e., a term-b...
When recommending news items, most of the traditional algorithms are based on TF-IDF, i.e., a term-b...
Traditionally, content-based recommendation is performed using term occurrences, which are leveraged...
News item recommendation is commonly performed using the TF-IDF weighting technique in combination w...
As the usage of internet is increasing, we are getting more dependent on it in our daily life. The I...
Traditionally, content-based news recommendation is performed by means of the cosine similarity and ...
Recommending news items is traditionally done by term-based algorithms like TF-IDF. This paper conce...
Keywords: in information retrieval for decades. We propose a novel term weighting method based on wh...
Recommending news items is traditionally done by term-based algorithms like TF-IDF. This paper conce...
A set of related articles is a useful addition to the newly published news. Such news articles conta...
Recommending news items is traditionally done by term-based algorithms like TF-IDF. This paper conce...
Content-based news recommendations are usually made by employing the cosine similarity and the TF-ID...
News items play an increasingly important role in the current business decision processes. Due to th...
AbstractTo deal with the challenge of information overload, in this paper, we propose a financial ne...