@@ -14,7 +14,7 @@ In the following, the two main approaches of \textit{collaborative-filtering} an
...
@@ -14,7 +14,7 @@ In the following, the two main approaches of \textit{collaborative-filtering} an
Unlike the \textit{content-based recommender}, the \textit{collaborative-filtering recommender (CF)} not only considers individual \textit{users} and \textit{feature vectors}, but rather a \textit{like-minded neighborhood} of each \textit{user}.
Unlike the \textit{content-based recommender}, the \textit{collaborative-filtering recommender (CF)} not only considers individual \textit{users} and \textit{feature vectors}, but rather a \textit{like-minded neighborhood} of each \textit{user}.
Missing \textit{user ratings} can be extracted by this \textit{neighbourhood} and \textit{networked} to form a whole. It is assumed that a \textit{missing rating} of the considered \textit{user}$u$ for an unknown \textit{item}$i$ will be similar to the \textit{rating} of a \textit{user}$v$ as soon as $u$ and $v$ have rated some \textit{items} similarly. The similarity of the \textit{users} is determined by the \textit{community ratings}. This type of \textit{recommender system} is also known by the term \textit{neighborhood-based recommender}\citep{DeKa11}. The main focus of \textit{neighbourhood-based methods} is on the application of iterative methods such as \textit{k-nearest-neighbours} or \textit{k-means}.
Missing \textit{user ratings} can be extracted by this \textit{neighbourhood} and \textit{networked} to form a whole. It is assumed that a \textit{missing rating} of the considered \textit{user}$u$ for an unknown \textit{item}$i$ will be similar to the \textit{rating} of a \textit{user}$v$ as soon as $u$ and $v$ have rated some \textit{items} similarly. The similarity of the \textit{users} is determined by the \textit{community ratings}. This type of \textit{recommender system} is also known by the term \textit{neighborhood-based recommender}\citep{DeKa11}. The main focus of \textit{neighbourhood-based methods} is on the application of iterative methods such as \textit{k-nearest-neighbours} or \textit{k-means}.
A \textit{neighborhood-based recommender} can be viewed from two perspetives: The first and best known problem is the so-called \textit{user-based prediction}. Here, the \textit{missing ratings} of a considered \textit{user}$u$ are to be determined from his \textit{neighborhood}$\mathcal{N}_i(u)$.
A \textit{neighborhood-based recommender} can be viewed from two perspetives: The first and best known problem is the so-called \textit{user-based prediction}. Here, the \textit{missing ratings} of a considered \textit{user}$u$ are to be determined from his \textit{neighborhood}$\mathcal{N}_i(u)$.
$\mathcal{N}_i(u)$ denotes the subset of the \textit{neighborhood} of all \textit{users} who have a similar manner of evaluation to $u$ and who have evaluated the unknown \textit{item}$i$. The second problem is that of \textit{item-based prediction}. Analogously, the similarity of the \textit{items} are determined by their received \textit{ratings}.
$\mathcal{N}_i(u)$ denotes the subset of the \textit{neighborhood} of all \textit{users} who have a similar manner of evaluation to $u$ and who have rated the unknown \textit{item}$i$. The second problem is that of \textit{item-based prediction}. Analogously, the similarity of the \textit{items} are determined by their received \textit{ratings}.
This kind of problem consideres the \textit{neighborhood}$\mathcal{N}_u(i)$ of all \textit{items} which were rated by \textit{user}$u$ and who are similar to $i$. The similarity between the objects of a \textit{neighborhood} is determined by \textit{distance functions} such as \textit{mean-squared-difference}, \textit{pearson-correlation} or \textit{cosine-similarity}.
This kind of problem consideres the \textit{neighborhood}$\mathcal{N}_u(i)$ of all \textit{items} which were rated by \textit{user}$u$ and who are similar to $i$. The similarity between the objects of a \textit{neighborhood} is determined by \textit{distance functions} such as \textit{mean-squared-difference}, \textit{pearson-correlation} or \textit{cosine-similarity}.
Figure \ref{fig:cf} shows a sketch of the general operation of \textit{collaborative-filtering}\textit{recommender}.
Figure \ref{fig:cf} shows a sketch of the general operation of \textit{collaborative-filtering}\textit{recommender}.
