Combining Content-Based and Collaborative Filtering
نویسندگان
چکیده
منابع مشابه
A Graph-Based Method for Combining Collaborative and Content-Based Filtering
Collaborative filtering and content-based filtering are two main approaches to make recommendations in recommender systems. While each approach has its own strengths and weaknesses, combining the two approaches can improve recommendation accuracy. In this paper, we present a graph-based method that allows combining content information and rating information in a natural way. The proposed method...
متن کاملیک سامانه توصیهگر ترکیبی با استفاده از اعتماد و خوشهبندی دوجهته بهمنظور افزایش کارایی پالایشگروهی
In the present era, the amount of information grows exponentially. So, finding the required information among the mass of information has become a major challenge. The success of e-commerce systems and online business transactions depend greatly on the effective design of products recommender mechanism. Providing high quality recommendations is important for e-commerce systems to assist users i...
متن کاملCombining Collaborative, Diversity and Content Based Filtering for Recommendation System
Combining collaborative filtering with some other technique is most common in hybrid recommender systems. As many recommended items from collaborative filtering seem to be similar with respect to content, the collaborative-content hybrid system suffers in terms of quality recommendation and recommending new items as well. To alleviate such problem, we have developed a novel method that uses a d...
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Recommender systems utilize information retrieval and machine learning techniques for filtering information and can predict whether a user would like an unseen item. User similarity measurement plays an important role in collaborative filtering based recommender systems. In order to improve accuracy of traditional user based collaborative filtering techniques under new user cold-start problem a...
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The recommender systems are models that are to predict the potential interests of users among a number of items. These systems are widespread and they have many applications in real-world. These systems are generally based on one of two structural types: collaborative filtering and content filtering. There are some systems which are based on both of them. These systems are named hybrid recommen...
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