Collaborative filtering techniques have been studied extensively during the last decade. Many open source packages (Apache Mahout, LensKit, MyMediaLite, rrecsys etc.) have implemented them, but typically the top-N rec...
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Collaborative filtering techniques have been studied extensively during the last decade. Many open source packages (Apache Mahout, LensKit, MyMediaLite, rrecsys etc.) have implemented them, but typically the top-N recommendation lists are only based on a highest predicted ratings approach. However, exploiting frequencies in the user/item neighborhood for the formation of the top-N recommendation lists has been shown to provide superior accuracy results in offline simulations. In addition, most open source packages use a time-independent evaluation protocol to test the quality of recommendations, which may result to misleading conclusions since it cannot simulate well the real-life systems, which are strongly related to the time dimension. In this paper, we have therefore implemented the time-aware evaluation protocol to the open source recommendation package for the R language - denoted rrecsys - and compare its performance across open source packages for reasons of replicability. Our experimental results clearly demonstrate that using the most frequent items in neighborhood approach significantly outperforms the highest predicted rating approach on three public datasets. Moreover, the time-aware evaluation protocol has been shown to be more adequate for capturing the life-time effectiveness of recommender systems.
We study the problem of recovering both K communities and their features from a labeled graph observation. We assume that the edges of an observed graph are generated as per the symmetric Stochastic Block Model (SBM),...
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ISBN:
(纸本)9781538665961
We study the problem of recovering both K communities and their features from a labeled graph observation. We assume that the edges of an observed graph are generated as per the symmetric Stochastic Block Model (SBM), and that the label of each node is a noisy and partially-observed version of the corresponding community feature. We characterize the information-theoretic limit of this problem, and then propose a computationally efficient algorithm that achieves the information-theoretic limit.
The search for unfamiliar experiences and novelty is one of the main drivers behind all human activities, equally important with harm avoidance and reward dependence. A recommender system personalizes suggestions to i...
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ISBN:
(纸本)9783030038403;9783030038397
The search for unfamiliar experiences and novelty is one of the main drivers behind all human activities, equally important with harm avoidance and reward dependence. A recommender system personalizes suggestions to individuals to help them in their exploration tasks. In the ideal case, these recommendations, except of being accurate, should be also novel. However, up to now most platforms fail to provide both novel and accurate recommendations. For example, a well-known recommendation algorithm, such as matrix factorization (MF), tries to optimize only the accuracy criterion, while disregards the novelty of recommended items. In this paper, we propose a new model, denoted as popularity-based NMF, that allows to trade-off the MF performance with respect to the criteria of novelty, while only minimally compromising on accuracy. Our experimental results demonstrate that we attain high accuracy by recommending also novel items.
Recently, recommendation algorithms have been widely used in many e-commerce platforms to recommend items to users on the basis of their preferences to improve selling efficiency. Matrix factorization methods which ex...
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Recently, recommendation algorithms have been widely used in many e-commerce platforms to recommend items to users on the basis of their preferences to improve selling efficiency. Matrix factorization methods which extract latent features of users and items by decomposing the rating matrix have achieved success in rating prediction. But almost all of these algorithms are designed to fit the rating matrix directly to get the latent features and ignore the user-item relationship in feature space. To this end, in this paper, we propose a recommendation in feature space sphere (RFSS) which takes into account the relationship between users and items in feature space. Different from the conventional latent feature based recommendation algorithms, the proposed algorithm supposes that if a user likes an item, the user is close to the item in feature space. Meanwhile, the closer a user and an item are in feature space, the higher the predicted rating will be. And an adaptive user-dependent coefficient is introduced to map the user-item distances to the predicted ratings. Extensive experiments on four real-world datasets have been conducted, the results of which show that our proposed method outperforms the state-of-the-art recommendation algorithms. (C) 2017 Elsevier B.V. All rights reserved.
algorithms and especially recommendation algorithms play an important role online, most notably on YouTube. Yet, little is known about the network communities that these algorithms form. We analyzed the channel recomm...
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algorithms and especially recommendation algorithms play an important role online, most notably on YouTube. Yet, little is known about the network communities that these algorithms form. We analyzed the channel recommendations on YouTube to map the communities that the social network is creating through its algorithms and to test the network for homophily, that is, the connectedness between communities. We find that YouTube's channel recommendation algorithm fosters the creation of highly homophilous communities in the United States (n = 13,529 channels) and in Germany (n = 8,000 channels). Factors that seem to drive YouTube's recommendations are topics, language, and location. We highlight the issue of homophilous communities in the context of politics where YouTube's algorithms create far-right communities in both countries.
The mobile application market and e-commerce sales have grown steadily, along with the growth of studies and product recommendation solutions implemented in e-commerce systems. In this context, this paper proposes a r...
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ISBN:
(纸本)9789897582479
The mobile application market and e-commerce sales have grown steadily, along with the growth of studies and product recommendation solutions implemented in e-commerce systems. In this context, this paper proposes a recommendation algorithm for mobile devices based on the COREL framework. The proposed recommendation algorithm is a customization of the COREL framework, based on the complexity of the implementation associated with iOS mobile applications. Therefore, this work aims to customize a gift recommendation algorithm in the context of mobile devices using as main input the user preferences for the gifts recommendation in the Giftr application.
Personalized recommendation approaches have received much attention over the years. In this paper, we propose a hybrid recommendation approach that integrates an item-based collaborative filtering, a user-based collab...
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ISBN:
(纸本)9781538627266
Personalized recommendation approaches have received much attention over the years. In this paper, we propose a hybrid recommendation approach that integrates an item-based collaborative filtering, a user-based collaborative filtering and a matrix factorization method. The approach considers the two objectives of recommendation's accuracy and diversity simultaneously. First, a set of items is created separately by each of the three methods. Then, items produced by the three methods are combined into a set of candidate items. Finally, a multiobjective genetic algorithm is adopted to choose a set of Pareto recommendation lists from the set. Experimental results show that the proposed approach is very effective and is able to produce better Pareto solutions than those comparative approaches.
Recently, recommendation algorithms have been widely used to improve the benefit of businesses and the satisfaction of users in many online platforms. However, most of the existing algorithms generate intermediate out...
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ISBN:
(纸本)9783319701394;9783319701387
Recently, recommendation algorithms have been widely used to improve the benefit of businesses and the satisfaction of users in many online platforms. However, most of the existing algorithms generate intermediate output when predicting ratings and the error of intermediate output will be propagated to the final results. Besides, since most algorithms predict all the unrated items, some predicted ratings may be unreliable and useless which will lower the efficiency and effectiveness of recommendation. To this end, we propose a Low-rank and Sparse Matrix Completion (LSMC) method which recovers rating matrix directly to improve the quality of rating prediction. Following the common methodology, we assume the structure of the predicted rating matrix is low-rank since rating is just connected with some factors of user and item. However, different from the existing methods, we assume the matrix is sparse so some unreliable predictions will be removed and important results will be retained. Besides, a slack variable will be used to prevent overfitting and weaken the influence of noisy data. Extensive experiments on four real-world datasets have been conducted to verify that the proposed method outperforms the state-of-the-art recommendation algorithms.
rrecsys is a novel library in R for developing and assessing recommendation algorithms. In this demo, we extend rrecsys with functions for visual analytics of recommendation performance, that is one of the strong capa...
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ISBN:
(纸本)9781450346528
rrecsys is a novel library in R for developing and assessing recommendation algorithms. In this demo, we extend rrecsys with functions for visual analytics of recommendation performance, that is one of the strong capabilities of the R environment. In particular, we show how the library can be used to depict dataset characteristics, train and test recommendation algorithms and to visually assess, for instance, their capability to exploit long-tail items for making correct predictions.
In recent years, social networking services and e-commerce have been developing rapidly. The research of recommending in e-commerce service mainly focused on using the collaborative filtering algorithm. But the algori...
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