نتایج جستجو برای: recommendation systems
تعداد نتایج: 1210479 فیلتر نتایج به سال:
Recent advances in position localization techniques have fundamentally enhanced social networking services, allowing users to share their locations and location-related content, such as geo-tagged photos and notes. We refer to these social networks as location-based social networks (LBSNs). Location data both bridges the gap between the physical and digital worlds and enables a deeper understan...
This paper analyses aspects about the recommendation process in distributed information systems. It extracts similarities and differences between recommendations in estores and the recommendations applied to an e-learning environment. It also explains the phenomena of self-organization and cooperative emergence in complex systems coupled with bio-inspired algorithms to improve knowledge discove...
As the growing interest of web recommendation systems those are applied to deliver customized data for their users, we started working on this system. Generally the recommendation systems are divided into two major categories such as collaborative recommendation system and content based recommendation system. In case of collaborative recommendation systems, these try to seek out users who share...
A Recommender System (RS) is a composition of software tools and machine learning techniques that provides valuable piece of advice for items or services chosen by a user. Recommender systems are currently useful in both the research and in the commercial areas. Numerous approaches have been proposed for providing recommendations. Certainly, recommendation systems have an assortment of properti...
This article argues why personal recommender systems in technology-enhanced learning have to be adjusted to the specific character of learning. Personal recommender systems are strongly depend on the context or domain they operate in, and it is often not possible to take one recommender system from one context and transfer it to another context or domain. The article describes a number of disti...
Recommender systems offer users a more intelligent and personalised mechanism to seek out new information. Content-based recommender systems generally prefer to retrieve a set of items maximally similar to a users’ query and/or profile. We argue that as new types of recommendation domains and tasks emerge, this blind faith in the similarity assumption begins to seem flawed. We show that very of...
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