Personalized Music Recommendation by Mining Social Media Tags

Su, Ja Hwung; Wei, Chang; Tseng, Vincent S.

doi:10.1016/j.procs.2013.09.107

Cited by 30 publications

(18 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Second, the fusion model is the best. Third, it could be seen from the second graph that RMSE is lower for all the three methods of the proposed model than the methods of existing method [12]. Therefore, it can be concluded that the proposed model provides better recommendations.…”

Section: Comparison Between Different Prediction Modelsmentioning

confidence: 81%

See 1 more Smart Citation

A Novel Method for Music Recommendation using Social Media Tags

Advani¹,

Soni²

2015

IJCA

View full text Add to dashboard Cite

Tremendous growth of online music data has given new opportunities for building more effective music recommender systems. These systems help users to find and categorize songs according to their likings. The main goal of Recommender Systems (RS) is to predict ratings of items that the users would be interested in. With the rapid development of the Collaborative Tagging approach, tags could be fascinating and helpful information to enrich RS systems. Attributes are the "global" depictions of items while tags are "local" depictions of items provided by the users.Explicit feedback and implicit feedback demonstrates distinct properties of users' preferences with both advantages and disadvantages. Combination of these in a user preference model not only exhibits a number of disputes but can also overwhelm the problems related with each other. Hence, to take advantage of tagging data and see whether better recommendations are generated or not, a novel method for music recommendation is proposed that combines implicit feedback and explicit feedback of the users. Also, both explicit types of feedbacks are normalized before transformation into ratings in order to provide the desired ratings in case of skewed play counts data.

show abstract

Section: Comparison Between Different Prediction Modelsmentioning

confidence: 81%

“…Ja-Hwung Su et al [12] have proposed a different music recommendation approach that makes use of social media tags instead of ratings to calculate the similarity between music tracks. They used tag-based similarity for finding the user preferences hidden in.…”

Section: Related Workmentioning

confidence: 99%

A Novel Method for Music Recommendation using Social Media Tags

Advani¹,

Soni²

2015

IJCA

View full text Add to dashboard Cite

show abstract

“…Based on Lemma 1, we safely remove Felicja and Gustaw having Div U ({Felicja})=0. 23 and Div U ({Gustaw})=0.23 both below minDiv as their super-groups cannot be diverse. So, the header table includes only the remaining 5 friends-sorted in some order (e.g., lexicographical order of friend names)-with their Freq D 1,2,3 ({ f i }).…”

Section: Our Dise-growth Algorithm Builds the Dise-tree Structurementioning

confidence: 99%

“…Several data mining techniques 18,23,33 have been developed to help users extract implicit, previously unknown, and potentially useful information from linked web data and/or social network data such as blogs, forums, and wikis. For instance, researchers have modelled, queried, and reasoned about these linked web and/or social data.…”

Section: Related Workmentioning

confidence: 99%

A Tree-based Algorithm for Mining Diverse Social Entities

Braun

Cuzzocrea

Leung

et al. 2014

Procedia Computer Science

View full text Add to dashboard Cite

DiSE-growth, a tree-based (pattern-growth) algorithm for mining DIverse Social Entities, is proposed and experimentally assessed in this paper. The algorithm makes use of a specialized data structure, called DiSE-tree, for effectively and efficiently representing relevant information on diverse social entities while successfully supporting the mining phase. Diverse entities are popular in a wide spectrum of application scenarios, ranging from linked Web data to Semantic Web and social networks. In all these application scenarios, it has become important to analyze high volumes of valuable linked data and discover those diverse social entities. We complement our analytical contributions by means of an experimental evaluation that clearly shows the benefits of our tree-based diverse social entity mining algorithm.

show abstract

“…In [12], the user profiles and tag information are fused to generate a framework of joint item-tag recommendation. Su et al [17,18] integrated information of tags, play counts, and artists to improve the recommendation quality. Cheng et al [3] integrated acoustic features and user personalities to conduct a personalized recommendation service.…”

Section: Music Recommendationmentioning

confidence: 99%

Mining and applications of repeating patterns

Hong

Chin

et al. 2018

Vietnam J Comput Sci

Self Cite

View full text Add to dashboard Cite

Mining the valuable knowledge from real data has been a hot topic for a long time. Repeating pattern is one of the important knowledge, occurring in many real applications such as musical data and medical data. In this paper, our purposes are to contribute an efficient mining algorithm for repeating patterns and to conduct a real application using the repeating patterns mined. In terms of mining the repeating patterns, although a number of past studies were made on this issue, the performance cannot still earn the users' satisfactions especially for large data sets. For this issue, in this paper, we propose an efficient algorithm named Fast Mining of Repeating Patterns, which achieves high performance of discovering the repeating patterns by a novel index called Quick-Pattern Index. In terms of applications, a music recommender system named repeating-patternbased music recommender system is proposed to deal with problems in music recommendation. Even facing a very sparse rating matrix, the recommendation can still be completed. The experimental results show that our proposed mining algorithm and recommender system outperform the previous works in terms of efficiency and effectiveness, respectively.

show abstract

Personalized Music Recommendation by Mining Social Media Tags

Cited by 30 publications

References 11 publications

A Novel Method for Music Recommendation using Social Media Tags

A Novel Method for Music Recommendation using Social Media Tags

A Tree-based Algorithm for Mining Diverse Social Entities

Mining and applications of repeating patterns

Contact Info

Product

Resources

About