Collaborative filtering-based recommender systems by effective trust
نویسندگان
چکیده
منابع مشابه
Trust-Aware Collaborative Filtering for Recommender Systems
Recommender Systems allow people to find the resources they need by making use of the experiences and opinions of their nearest neighbours. Costly annotations by experts are replaced by a distributed process where the users take the initiative. While the collaborative approach enables the collection of a vast amount of data, a new issue arises: the quality assessment. The elicitation of trust v...
متن کاملCollaborative Filtering Recommender Systems
One of the potent personalization technologies powering the adaptive web is collaborative filtering. Collaborative filtering (CF) is the process of filtering or evaluating items through the opinions of other people. CF technology brings together the opinions of large interconnected communities on the web, supporting filtering of substantial quantities of data. In this chapter we introduce the c...
متن کاملCollaborative Filtering Recommender Systems
Recommender systems are an important part of the information and e-commerce ecosystem. They represent a powerful method for enabling users to filter through large information and product spaces. Nearly two decades of research on collaborative filtering have led to a varied set of algorithms and a rich collection of tools for evaluating their performance. Research in the field is moving in the d...
متن کاملCollaborative Filtering Recommender Systems
Recommender Systems are software tools and techniques for suggesting items to users by considering their preferences in an automated fashion. The suggestions provided are aimed at support users in various decisionmaking processes. Technically, recommender system has their origins in different fields such as Information Retrieval (IR), text classification, machine learning and Decision Support S...
متن کاملHybridising Collaborative Filtering and Trust-aware Recommender Systems
Recommender systems (RS) aim to predict items that users would appreciate, over a list of items. In evaluation of recommender systems, two issues can be defined: accuracy of prediction which implies the satisfaction of the user, coverage which implies the percentage of satisfied users. Collaborative filtering (CF) is the master approach in this domain, but still has some weaknesses especially a...
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ژورنال
عنوان ژورنال: International Journal of Data Science and Analytics
سال: 2017
ISSN: 2364-415X,2364-4168
DOI: 10.1007/s41060-017-0049-y