MusicBox: Personalized Music Recommendation Based on Cubic Analysis of Social Tags

Νανόπουλος, Αλέξανδρος; Rafailidis, Dimitrios; Symeonidis, Panagiotis; Manolopoulos, Yannis

doi:10.1109/tasl.2009.2033973

Cited by 94 publications

(37 citation statements)

References 10 publications

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“…Thus these risks are advertised on social networks, etc. A hybrid music recommendation system which handles the issues encountered with collaborative and reclusive approaches has been reported [133]. The authors have utilized the rating as well as content of data by using a Bayesian network.…”

Section: Other Hybrid Recommender Systems Using Reclusive Methodsmentioning

confidence: 99%

http://www.jestr.org/downloads/Volume10Issue4/fulltext171042017.pdf

Sohail

Siddiqui

Ali

2017

JESTR

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This paper presents the state of art techniques in recommender systems (RS). The various techniques are diagrammatically illustrated which on one hand helps a naïve researcher in this field to accommodate the on-going researches and establish a strong base, on the other hand it focuses on different categories of the recommender systems with deep technical discussions. The review studies on RS are highlighted which helps in understanding the previous review works and their directions. 8 different main categories of recommender techniques and 19 sub categories have been identified and stated. Further, soft computing approach for recommendation is emphasized which have not been well studied earlier. The major problems of the existing area is reviewed and presented from different perspectives. However, solutions to these issues are rarely discussed in the previous works, in this study future direction for possible solutions are also addressed.

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Section: Other Hybrid Recommender Systems Using Reclusive Methodsmentioning

confidence: 99%

http://www.jestr.org/downloads/Volume10Issue4/fulltext171042017.pdf

Sohail

Siddiqui

Ali

2017

JESTR

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“…Some of these advanced technologies, such as tensor factorization, can also be used for item recommendation in social tagging systems; the research problem explored in this article. For instance, Nanopoulos et al [2010] exploited the HOSVD model combined with music similarity based on audio features to leverage the latent ternary structure of social tagging systems for personalized music recommendation. Guy et al [2010] proposed a personalized item recommendation algorithm based on people and tags with an enterprise social media application suite that included blogs, bookmarks, communities, wikis, and shared files.…”

Section: Tag-based Recommendationmentioning

confidence: 99%

“…The proposed approach is based on random walks applied to the associations among the user-item, user-tag, and item-tag bipartite graphs. It is different from tensor factorization applied to the 3rd-order tensor representation of social tagging systems in Nanopoulos et al [2010]. Similarly, the social relation (e.g., friendship, organizational relation etc.)…”

Section: Tag-based Recommendationmentioning

confidence: 99%

A Random Walk Model for Item Recommendation in Social Tagging Systems

Zhang

Zeng

Abbasi

et al. 2013

ACM Trans. Manage. Inf. Syst.

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Social tagging, as a novel approach to information organization and discovery, has been widely adopted in many Web 2.0 applications. Tags contributed by users to annotate a variety of Web resources or items provide a new type of information that can be exploited by recommender systems. Nevertheless, the sparsity of the ternary interaction data among users, items, and tags limits the performance of tag-based recommendation algorithms. In this article, we propose to deal with the sparsity problem in social tagging by applying random walks on ternary interaction graphs to explore transitive associations between users and items. The transitive associations in this article refer to the path of the link between any two nodes whose length is greater than one. Taking advantage of these transitive associations can allow more accurate measurement of the relevance between two entities (e.g., user-item, user-user, and item-item). A PageRank-like algorithm has been developed to explore these transitive associations by spreading users' preferences on an item similarity graph and spreading items' influences on a user similarity graph. Empirical evaluation on three real-world datasets demonstrates that our approach can effectively alleviate the sparsity problem and improve the quality of item recommendation. Social tagging, as a novel approach to information organization and discovery, has been widely adopted in many Web 2.0 applications. Tags contributed by users to annotate a variety of Web resources or items provide a new type of information that can be exploited by recommender systems. Nevertheless, the sparsity of the ternary interaction data among users, items, and tags limits the performance of tag-based recommendation algorithms. In this article, we propose to deal with the sparsity problem in social tagging by applying random walks on ternary interaction graphs to explore transitive associations between users and items. The transitive associations in this article refer to the path of the link between any two nodes whose length is greater than one. Taking advantage of these transitive associations can allow more accurate measurement of the relevance between two entities (e.g., user-item, user-user, and item-item). A PageRank-like algorithm has been developed to explore these transitive associations by spreading users' preferences on an item similarity graph and spreading items' influences on a user similarity graph. Empirical evaluation on three real-world datasets demonstrates that our approach can effectively alleviate the sparsity problem and improve the quality of item recommendation. Disciplines Finance and Financial Management | Social and Behavioral Sciences

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“…Yet these different frameworks considerably overlap in applications. Among those: finding co-regulated genes over gene expression data (Madeira and Oliveira 2004;Besson et al 2005;Barkow et al 2006;Tarca et al 2007;Hanczar and Nadif 2010;Kaytoue et al 2011;Eren et al 2013), prediction of biological activity of chemical compounds (Blinova et al 2003;Kuznetsov and Samokhin 2005;DiMaggio et al 2010;Asses et al 2012), summarization and classification of texts (Dhillon 2001;Cimiano et al 2005;Banerjee et al 2007;Ignatov and Kuznetsov 2009;Carpineto et al 2009), structuring websearch results and browsing navigation in Information Retrieval (Carpineto and Romano 2005;Koester 2006;Eklund et al 2012;Poelmans et al 2012), finding communities in two-mode networks in Social Network Analysis (Duquenne 1996;Freeman 1996;Latapy et al 2008;Roth et al 2008;Gnatyshak et al 2012) and Recommender Systems (Boucher-Ryan and Bridge 2006;Symeonidis et al 2008;Ignatov and Kuznetsov 2008;Nanopoulos et al 2010;Ignatov et al 2014).…”

Section: Introduction and Related Workmentioning

confidence: 99%

Triadic Formal Concept Analysis and triclustering: searching for optimal patterns

et al. 2015

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This paper presents several definitions of "optimal patterns" in triadic data and results of experimental comparison of five triclustering algorithms on real-world and synthetic datasets. The evaluation is carried over such criteria as resource efficiency, noise tolerance and quality scores involving cardinality, density, coverage, and diversity of the patterns. An ideal triadic pattern is a totally dense maximal cuboid (formal triconcept). Relaxations of this notion under consideration are: OAC-triclusters; triclusters optimal with respect to the leastsquare criterion; and graph partitions obtained by using spectral clustering. We show that searching for an optimal tricluster cover is an NP-complete problem, whereas determining the number of such covers is #P-complete. Our extensive computational experiments lead us to a clear strategy for choosing a solution at a given dataset guided by the principle of Pareto-optimality according to the proposed criteria.

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MusicBox: Personalized Music Recommendation Based on Cubic Analysis of Social Tags

Cited by 94 publications

References 10 publications

http://www.jestr.org/downloads/Volume10Issue4/fulltext171042017.pdf

http://www.jestr.org/downloads/Volume10Issue4/fulltext171042017.pdf

A Random Walk Model for Item Recommendation in Social Tagging Systems

Triadic Formal Concept Analysis and triclustering: searching for optimal patterns

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