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2016 International Conference on Wireless Communication and Network Engineering (WCNE2016)

作者： Ben-jie FAN Ling CHEN Chen XU Department of Computer Science Yangzhou University State Key Lab of Novel Software Tech Nanjing University

Most of the traditional collaborative filtering recommendation algorithms do not take into account the factor the time the users evaluate the items, they calculate the similarity between the users only using the static data. In many real world applications, the user's interest may change with the time. In this paper, we present an efficient method for such dynamic recommendation. The method calculates the similarity between the users based on their evaluation scores and times on the items. A fading factor is defined to emphasis of the recent ratings. The experimental results show that the accuracy of the recommendation results by our method(UBCFT) is improved compared with the existing collaborative filtering algorithms.

关键词： recommendation algorithms Collaborative filtering Similarity between users

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Vliv umělé inteligence na digitální komunikaci a média a jejich budoucí vývoj

Vliv umělé inteligence na digitální komunikaci a média ...

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作者： Sukdol, Štěpán Charles University of Prague

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Improving Time-Aware recommendations in Open Source Packages

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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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Cornac: a comparative framework for multimodal recommender systems

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2020年第1期21卷 3803-3807页

作者： Aghiles Salah Quoc-Tuan Truong Hady W. Lauw Singapore Management University School of Information Systems Singapore

Cornac is an open-source Python framework for multimodal recommender systems. In addition to core utilities for accessing, building, evaluating, and comparing recommender models, Cornac is distinctive in putting emphasis on recommendation models that leverage auxiliary information in the form of a social network, item textual descriptions, product images, etc. Such multimodal auxiliary data supplement user-item interactions (e.g., ratings, clicks), which tend to be sparse in practice. To facilitate broad adoption and community contribution, Cornac is publicly available at https://***/PreferredAI/cornac, and it can be installed via Anaconda or the Python Package Index (pip). Not only is it well-covered by unit tests to ensure code quality, but it is also accompanied with a detailed documentation, tutorials, examples, and several built-in benchmarking data sets.

关键词： comparison multimodality recommendation algorithms software

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Human Decision Making and Recommender Systems

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ACM TRANSACTIONS ON INTERACTIVE INTELLIGENT SYSTEMS 2013年第3期3卷 17-17页

作者： Chen, Li de Gemmis, Marco Felfernig, Alexander Lops, Pasquale Ricci, Francesco Semeraro, Giovanni Hong Kong Baptist Univ Hong Kong Hong Kong Peoples R China Univ Bari Aldo Moro Bari Italy Graz Univ Technol Graz Austria Free Univ Bozen Bolzano Bolzano Italy

Recommender systems have already proved to be valuable for coping with the information overload problem in several application domains. They provide people with suggestions for items which are likely to be of interest for them;hence, a primary function of recommender systems is to help people make good choices and decisions. However, most previous research has focused on recommendation techniques and algorithms, and less attention has been devoted to the decision making processes adopted by the users and possibly supported by the system. There is still a gap between the importance that the community gives to the assessment of recommendation algorithms and the current range of ongoing research activities concerning human decision making. Different decision-psychological phenomena can influence the decision making of users of recommender systems, and research along these lines is becoming increasingly important and popular. This special issue highlights how the coupling of recommendation algorithms with the understanding of human choice and decision making theory has the potential to benefit research and practice on recommender systems and to enable users to achieve a good balance between decision accuracy and decision effort.

关键词： algorithms Design Experimentation Human Factors Recommender systems decision making decision psychology recommendation algorithms decision biases

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Recommender systems may enhance the discovery of novelties

JOURNAL OF PHYSICS-COMPLEXITY

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JOURNAL OF PHYSICS-COMPLEXITY 2024年第4期5卷

作者： De Marzo, Giordano Gravino, Pietro Loreto, Vittorio Univ Konstanz Universitaetstr 10 D-78457 Constance Germany Sony Comp Sci Labs Paris 6 Rue Amyot F-75005 Paris France Sony Comp Sci Labs Rome Joint Initiat CREF Sony Piazza Viminale 1 I-00184 Rome Italy Ctr Ric Enr Fermi Piazza Viminale 1 I-00184 Rome Italy Complex Sci Hub Vienna Josefstaedter Str 39 A-1080 Vienna Austria Sapienza Univ Rome Ple A Moro 2 I-00185 Rome Italy

Recommender systems are vital for shaping user online experiences. While some believe they may limit new content exploration and promote opinion polarization, a systematic analysis is still lacking. In this paper we present a model that explores the influence of recommender systems on novel content discovery. Surprisingly, analytical and numerical findings reveal that these techniques can enhance novelty discovery rates. Also, distinct algorithms with similar discovery rates yield different outcomes, with the matrix factorization algorithm producing opinion polarization. Our approach shed light on the interplay between algorithmic recommendations and novelties discovery, offering a framework to enhance recommendation techniques beyond accuracy metrics.

关键词： Heaps' law recommendation algorithms opinion polarization urn models

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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

作者： Pan Wang Ruihong Li Xingquan Zuo Xinchao Zhao Congcong Guo Chaomin Luo School of Computer Science Ministry of Education Beijng China School of Science Beijing University of Posts and Telecommunications Beijng China Department of Electrical and Computer Engineering University of Detroit Mercy MI USA

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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A Path-based recommendations Approach for Online Systems via Hyperbolic Network Embedding

A Path-based Recommendations Approach for Online Systems via...

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IEEE Symposium on Computers and Communications

作者： Nikolaos Papadis Eleni Stai Vasileios Karyotis Dept. of Elec. Eng. and YINS Yale University New Haven CT USA School of Electr. & Compo Engin Nat'l Technical University of Athens Zografou Athens Greece Ecole Poly technique Fédérale de Lausanne School of Compo & Commun. Sciences Lausanne Switzerland

In this paper we introduce and demonstrate new recommendation algorithms for large-scale online systems, such as e-shops and cloud services. The proposed algorithms are based on the combination of network embedding in hyperbolic space with greedy routing, exploiting properties of hyperbolic metric spaces. Contrary to the existing recommender systems that rank products in order to propose the highest ranked ones to the users, our proposed recommender system creates a progressive path of recommendations towards a final (known or inferred) target product using greedy routing over networks embedded in hyperbolic space. Thus, it prepares the user by intermediate recommendations for maximizing the chances that he/she accepts the recommendation of the target product(s). This casts the problem of locating a suitable recommendation as a path problem, where leveraging on the efficiency of greedy routing in graphs embedded in hyperbolic spaces and exploiting special network structure, if any, pays dividends. Two variants of our recommendation approach are provided, namely Hyperbolic recommendation-Known Destination (HRKD), Hyperbolic recommendation-Unknown Destination (HRUD), when the target product is known or unknown, respectively. We demonstrate how the proposed approach can be used for producing efficient recommendations in online systems, along with studying the impact of the several parameters involved in its performance via proper emulation of user activity over suitably defined graphs.

关键词： recommendation algorithms Online social networks E-commerce Network embedding Greedy routing Hyperbolic spaces Path problems

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Research on the principle, performance, and application of UCB algorithm in multi arm slot machine problems

Research on the principle, performance, and application of U...

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作者： Ruijie Huang Faculty of Science and Technology Beijing Normal University-Hong Kong Baptist University United International College

As Internet technology continues to evolve, recommender systems have become an integral part of daily life. However, traditional methods are increasingly falling short of meeting evolving user expectations. Utilizing survey data from the MovieLens dataset, a comparative approach was employed to investigate the efficacy, performance, and applicability of the UCB(Upper Confidence Bound) algorithm in addressing the multi-armed bandit problem. The study reveals that the UCB algorithm significantly impacts the cumulative regret value, indicating its robust performance in the multi-armed bandit setting. Furthermore, LinUCB—an enhanced version of the UCB algorithm—exhibits exceptional overall performance. The algorithm's efficiency is not just limited to the regret value but extends to handling high-dimensional feature spaces and delivering personalized recommendations. Unlike traditional UCB algorithms,LinUCB adapts more fluidly to high-dimensional environments by leveraging a linear model to simulate the reward function associated with each arm. This adaptability makes LinUCB particularly effective for complex, feature-rich recommendation scenarios. The performance of the UCB algorithm is also contingent upon parameter selection, making this an important factor to consider in practical implementations. Overall, both UCB and its modified version, LinUCB,present compelling solutions for the challenges faced by modern recommender systems.

关键词： recommendation algorithms Multi-Armed Bandit Algorithm UCB LinUCB algorithm

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Workshop on human decision making in recommender systems: [email protected]'13 13

Workshop on human decision making in recommender systems: [e...

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Proceedings of the 7th ACM conference on Recommender systems

作者： Li Chen Marco de Gemmis Alexander Felfernig Pasquale Lops Francesco Ricci Giovanni Semeraro Martijn C. Willemsen Hong Kong Baptist University Hong Kong Hong Kong University of Bari Aldo Moro Bari Italy Graz University of Technology Graz Austria Free University of Bozen-Bolzano Bolzano Italy Eindhoven University of Technology Eindhoven Netherlands

ISBN: (纸本)9781450324090

A primary function of recommender systems is to help their users to make better choices and decisions. The overall goal of the workshop is to analyse and discuss novel techniques and approaches for supporting effective and efficient human decision making in different types of recommendation scenarios. The submitted papers discuss a wide range of topics from core algorithmic issues to the management of the human computer interaction.

关键词： decision psychology decision making recommendation algorithms decision biases

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