نتایج جستجو برای: user similarity
تعداد نتایج: 345266 فیلتر نتایج به سال:
Recommendation systems have been developed in many domains to help users with information overload from the large volume of online multimedia content by providing them appropriate options. Recently hybrid recommendation require a amount data understand users’ interests and give suggestions. However, several internet privacy issues make skeptical about sharing their personal service providers, l...
In view of the existing user similarity calculation principle of recommendation algorithm is single, and recommender system accuracy is not well, we propose a novel social multi-attribute collaborative filtering algorithm (SoMu). We first define the user attraction similarity by users’ historical rated behaviors using graph theory, and secondly, define the user interaction similarity by users’ ...
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...
Recommender systems play an important role in helping people finding items they like. One type of recommender system is collaborative filtering that considers feedback of like-minded people. The fundamental assumption of collaborative filtering is that people who previously shared similar preferences behave similarly later on. This paper introduces several novel, classification-based similarity...
In recent years, collaborative filtering (CF) methods are important and widely accepted techniques are available for recommender systems. One of these techniques is user based that produces useful recommendations based on the similarity by the ratings of likeminded users. However, these systems suffer from several inherent shortcomings such as data sparsity and cold start problems. With the dev...
Collaborative Filtering techniques offer recommendations to users by leveraging on the preferences of like-minded users. They thus rely highly on similarity measures to determine proximity between users. However, most of the previously proposed similarity measures are heuristics based and are not guaranteed to work well under all data environments. We propose a method employing Genetic algorith...
Collaborative recommender is the most popular recommendation technique nowadays and it mainly employs the user item rating data set. Traditional collaborative filtering approaches compute a similarity value between the target user and each other user by computing the relativity of their ratings, and they only consider the ratings information. User attribute information associated with a user's ...
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