Recommender systems and their features
CiteSeerX — Recommender Systems Based on Navigation Path Features
... navigation path based re
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A Firefly Approach to Collaborative Filtering based Recommender Systems through Fuzzy Features
... The Firefly method is consisting as swarm intelligence algorithm, an evolutionary model inspired by social performance & nature [16]. It is used to resolve optimization difficulties. Fireflies produce short and rhythmic ...
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A Particle Swarm Approach to Collaborative Filtering based Recommender Systems through Fuzzy Features
... hybrid features that retains the accuracy of memory – based CF as well as the scalability of model-based CF in an efficient ...user features are imprecise in nature, therefore these can be represented more ...
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Recommender systems
... Note that unary ratings may also be collected as part of a user’s implicit feedback, where the time spent viewing an item, or purchasing it, would convey to the system that a user is interested in the item. 1.2 Data ...
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Recommender Systems: A Survey
... research. Item –to – item correlation is employed for generating recommendations rather than deriving a user – to – item correlation and defining methodologies. Data gathering is the first and foremost phase of ...
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A Recommender System Based on Multi-features
... Collaborative recommender systems are based on an important feature of human behaviour that is the tendency to consume a limited set of ...filtering systems are based on similarity of users in which ...
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Recommender Systems for the Semantic Web
... the features collected are sufficient to reproduce the clusterings as those that emerge through recording real users’ listening ...’random recommender’, and the fact that reducing the number of neighbours ...
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Knowledge-based recommender systems
... The first FindMe system was the Car Navigator, an information access system for descriptions of new car models. In this system, cars were rated against a long list of criteria such as horsepower, price or gas mileage, ...
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Recommender Systems in E-Commerce
... of recommender systems is fully Automatic, completely Ephemeral ...Persistent systems that require Manual effort from the ...that systems that are more Persistent create a relationship with ...
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A survey of recommender systems in Twitter
... identifies features that might be useful for recommending ...five features, namely popularity, activity, lo- cation, friends in common and content of the tweets, are predicted to be relevant for ...
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Recommender Systems and their Security Concerns
... a recommender system only accepts authentic profiles and al- low them to post rating information then it will be very difficult for an attacker to mount an ...important features because the attackers may ...
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Overview and comparison of recommender systems
... of recommender systems is most commonly classified into three main categories: content–based, collaborative filtering and hybrid recommendation algorithms which combine the features from the first ...
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Recommender Systems: A Healthy Obsession
... While training programmes prescribe a variety of dif- ferent session-types, current apps do little when it comes to monitoring or assessing an athlete’s adherence to spe- cific training sessions. Most apps simply do not ...
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Smooth neighborhood recommender systems
... training recommender systems: collaborative filtering, which predicts users’ behaviors based on similar users (Bell and Koren, 2007), and content- based filtering, which builds user and item profiles based ...
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Introduction to Recommender Systems Handbook
... the features associated with the compared ...based recommender systems, imposing some order among the extensive and diverse aspects involved in their design and ...state-of-the-art systems ...
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Recommender Systems and the Social Web
... Figure 1 illustrates the leading social web services and their possible contribution to the user models of classical recommender systems. We will now analyze the specific contribution to each approach ...
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Cross-domain recommender systems
... latent features, share latent feature spaces across domains, and transfer knowledge between domains through the latent feature matrices [50, 51, 52, ...latent features, explore correlations between latent ...
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Chapter 12: Recommender Systems
... the features, infer a similarity between items I This similarity is not based on rating information, but on the item itself say, the genre of a song/book I Example: Pandora Radio ...
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Active caching for recommender systems
... or type of distance matrix and performs well with variety of datasets. Partial order based active caching approach uses monotonicity amongst the cached information to estimate the answers for non-cached queries. This ...
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Recommender Systems for Online Dating
... be more or less the same. Correlations between attributes may cause latent features to be scored multiple times. For example, attributes a have children and a age are presumably quite correlated. Another headache ...
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