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Improving Time-Aware recommendations in Open Source Packages

INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS 2019年第6期28卷

作者： Symeonidis, Panagiotis Coba, Ludovik Zanker, Markus Free Univ Bozen Bolzano Fac Comp Sci Piazza Domenicani 3 I-39100 Bozen Bolzano Italy

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.

关键词： recommendation algorithms evaluation collaborative filtering

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On the Joint Recovery of Community Structure and Community Features 56

On the Joint Recovery of Community Structure and Community F...

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56th Annual Allerton Conference on Communication, Control, and Computing (Allerton)

作者： Yoon, Jisang Lee, Kangwook Suh, Changho Kakaocorp R&D Ctr Sungnam South Korea Korea Adv Inst Sci & Technol Sch EE Daejeon South Korea

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.

关键词： Community recovery Stochastic Block Model Matrix completion recommendation algorithms Signal processing

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Novelty-Aware Matrix Factorization Based on Items' Popularity 17th

Novelty-Aware Matrix Factorization Based on Items' Popularit...

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17th International Conference of the Italian-Association-for-Artificial-Intelligence (AI*IA)

作者： Coba, Ludovik Symeonidis, Panagiotis Zanker, Markus Free Univ Bozen Bolzano I-39100 Bozen Bolzano Italy

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.

关键词： recommendation algorithms Evaluation Novelty Collaborative filtering Matrix factorization

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recommendation in feature space sphere

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ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS 2017年 26卷 109-118页

作者： Zhao, Zhi-Lin Wang, Chang-Dong Wan, Yuan-Yu Lai, Jian-Huang Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou Guangdong Peoples R China Nanjing Univ Dept Comp Sci & Technol Nanjing Jiangsu Peoples R China

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.

关键词： recommendation algorithms Matrix factorization Feature space

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Birds of a Feather Get Recommended Together: Algorithmic Homophily in YouTube's Channel recommendations in the United States and Germany

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SOCIAL MEDIA + SOCIETY 2020年第4期6卷

作者： Kaiser, Jonas Rauchfleisch, Adrian Harvard Univ Berkman Klein Ctr Internet & Soc 23 Everett StSecond Floor Cambridge MA 02138 USA Alexander von Humboldt Inst Internet & Soc Berlin Germany Suffolk Univ Boston MA 02114 USA Natl Taiwan Univ Grad Inst Journalism Taipei Taiwan

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.

关键词： algorithms recommendation algorithms homophily network analysis YouTube filter bubble

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Mobile Gift recommendation Algorithm 19

Mobile Gift Recommendation Algorithm

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19th International Conference on Enterprise Information Systems (ICEIS)

作者： Pereira, Caique de Paula da Costa, Ruyther Parente Canedo, Edna Dias Univ Brasilia Fac UnB Gama Gama DF Brazil

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.

关键词： recommendation algorithms e-Commerce Mobile Software Development iOS Giftr

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A Multiobjective Genetic Algorithm based Hybrid recommendation Approach

A Multiobjective Genetic Algorithm based Hybrid Recommendati...

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Wang, Pan Zuo, Xingquan Guo, Congcong Li, Ruihong Zhao, Xinchao Luo, Chaomin Beijing Univ Posts & Telecommun Sch Comp Sci Beijing Peoples R China Minist Educ Key Lab Trustworthy Distributed Comp & Serv Beijing Peoples R China Beijing Univ Posts & Telecommun Sch Sci Beijing Peoples R China Univ Detroit Mercy Dept Elect & Comp Detroit MI 48221 USA

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.

关键词： recommendation algorithms multiobjective optimization algorithms hybrid algorithm

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Low-Rank and Sparse Matrix Completion for recommendation 24th

Low-Rank and Sparse Matrix Completion for Recommendation

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24th International Conference on Neural Information Processing (ICONIP)

作者： Zhao, Zhi-Lin Huang, Ling Wang, Chang-Dong Lai, Jian-Huang Yu, Philip S. Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou Peoples R China Univ Illinois Dept Comp Sci Chicago IL USA Tsinghua Univ Inst Data Sci Beijing Peoples R China

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.

关键词： recommendation algorithms Low-rank Sparse

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Visual Analysis of recommendation Performance 17

Visual Analysis of Recommendation Performance

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11th ACM Conference on Recommender Systems (RecSys)

作者： Coba, Ludovik Symeonidis, Panagiotis Zanker, Markus Free Univ Bozen Fac Comp Sci Piazza Domenicani 3 I-39100 Bolzano Italy

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.

关键词： recommendation algorithms R evaluation Visualization

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A collaborative filtering recommendation method based on TagIEA expert degree model

A collaborative filtering recommendation method based on Tag...

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International Journal of Computational Science and Engineering

作者： Li, Weimin Wang, Bin Zou, Jianbo Sheng, Jinfang School of Information Science and Engineering Central South University 932 Lushan South Road Changsha Hunan410083 China Department of Information Science and Engineering Hunan University of Humanities Science and Technology Loudi Hunan China

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 algorithm had the limitations of data sparsity and cold start. This paper presents a model using TagIEA expert degree metrics in the context of social e-commerce services, where tag and expert degree information are integrated into the collaborative filtering algorithm. The comprehensive recommendation based on the TagIEA expert degree can effectively mitigate the problems of cold start and data sparsity. Finally, this paper verifies the effectiveness of the improved collaborative filtering algorithm by experiments. Copyright © 2017 Inderscience Enterprises Ltd.

关键词： Collaborative filtering Electronic commerce Signal filtering and prediction Social networking (online) Telecommunication services Collaborative filtering algorithms Collaborative filtering recommendations Data sparsity E commerce services Expert degree recommendation algorithms SNSs Social networking services

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