A new similarity function for selecting neighbors for each target item in collaborative filtering
As one of the collaborative filtering (CF) techniques, memory-based CF technique which recommends items to users based on rating information of like-minded users (called neighbors) has been widely used and has also proven to be useful in many practices in the age of information overload. However, there is still considerable room for improving the quality of recommendation. Shortly, similarity functions in traditional CF compute a similarity between a target user and the other user without considering a target item. More specifically, they give an equal weight to each of the co-rated items rated by both users. Neighbors of a target user, therefore, are identical for all target items. However, a reasonable assumption is that the similarity between a target item and each of the co-rated items should be considered when finding neighbors of a target user. Additionally, a different set of neighbors should be selected for each different target item. Thus, the objective of this paper is to propose a new similarity function in order to select different neighbors for each different target item. In the new similarity function, the rating of a user on an item is weighted by the item similarity between the item and the target item. Experimental results from MovieLens dataset and Netflix dataset provide evidence that our recommender model considerably outperforms the traditional CF-based recommender model.
collaborative filtering information overload item similarity recommendation system