NDLI: Sound and Music Recommendation with Knowledge Graphs

Please wait, while we are loading the content...

ACM Transactions on Intelligent Systems and Technology (TIST) : Volume 8

Issue 2, December 2016

Tensors for Data Mining and Data Fusion: Models, Applications, and Scalable Algorithms

Introduction to Intelligent Music Systems and Applications

A Joyful Ode to Automatic Orchestration

Getting Closer to the Essence of Music: The Con Espressione Manifesto

Harnessing Music-Related Visual Stereotypes for Music Information Retrieval

Sound and Music Recommendation with Knowledge Graphs

Tempo Driven Audio-to-Score Alignment Using Spectral Decomposition and Online Dynamic Time Warping

Towards Music Structural Segmentation across Genres: Features, Structural Hypotheses, and Annotation Principles

Learning Contextualized Music Semantics from Tags Via a Siamese Neural Network

Intelligent Process Adaptation in the SmartPM System

SMARTS: Scalable Microscopic Adaptive Road Traffic Simulator

Differential Flattening: A Novel Framework for Community Detection in Multi-Layer Graphs

Joint Structured Sparsity Regularized Multiview Dimension Reduction for Video-Based Facial Expression Recognition

Prediction and Simulation of Human Mobility Following Natural Disasters

A Supervised Learning Model for High-Dimensional and Large-Scale Data

Efficient Methods for Influence-Based Network-Oblivious Community Detection

Community Detection with Topological Structure and Attributes in Information Networks

Issue 1, October 2016

ACM Transactions on Intelligent Systems and Technology (TIST) : Volume 7

ACM Transactions on Intelligent Systems and Technology (TIST) : Volume 6

ACM Transactions on Intelligent Systems and Technology (TIST) : Volume 5

ACM Transactions on Intelligent Systems and Technology (TIST) : Volume 4

ACM Transactions on Intelligent Systems and Technology (TIST) : Volume 3

ACM Transactions on Intelligent Systems and Technology (TIST) : Volume 2

ACM Transactions on Intelligent Systems and Technology (TIST) : Volume 1

Sound and Music Recommendation with Knowledge Graphs

Content Provider	ACM Digital Library
Author	Serra, Xavier Sciascio, Eugenio Di Noia, Tommaso Di Ostuni, Vito Claudio Oramas, Sergio
Copyright Year	2016
Description	Author Affiliation: Pandora Media Inc., CA, USA(Sciascio, Eugenio Di; Polytechnic university of bari, Bari, Italy (Noia, Tommaso Di; Serra, Xavier); Universitat pompeu fabra, Barcelona, Spain (Oramas, Sergio; Ostuni, Vito Claudio))
Abstract	The Web has moved, slowly but steadily, from a collection of documents towards a collection of structured data. Knowledge graphs have then emerged as a way of representing the knowledge encoded in such data as well as a tool to reason on them in order to extract new and implicit information. Knowledge graphs are currently used, for example, to explain search results, to explore knowledge spaces, to semantically enrich textual documents, or to feed knowledge-intensive applications such as recommender systems. In this work, we describe how to create and exploit a knowledge graph to supply a hybrid recommendation engine with information that builds on top of a collections of documents describing musical and sound items. Tags and textual descriptions are exploited to extract and link entities to external graphs such as WordNet and DBpedia, which are in turn used to semantically enrich the initial data. By means of the knowledge graph we build, recommendations are computed using a feature combination hybrid approach. Two explicit graph feature mappings are formulated to obtain meaningful item feature representations able to catch the knowledge embedded in the graph. Those content features are further combined with additional collaborative information deriving from implicit user feedback. An extensive evaluation on historical data is performed over two different datasets: a dataset of sounds composed of tags, textual descriptions, and user’s download information gathered from Freesound.org and a dataset of songs that mixes song textual descriptions with tags and user’s listening habits extracted from Songfacts.com and Last.fm, respectively. Results show significant improvements with respect to state-of-the-art collaborative algorithms in both datasets. In addition, we show how the semantic expansion of the initial descriptions helps in achieving much better recommendation quality in terms of aggregated diversity and novelty.
Starting Page	1
Ending Page	21
Page Count	21
File Format	PDF
ISSN	21576904
e-ISSN	21576912
DOI	10.1145/2926718
Volume Number	8
Issue Number	2
Journal	ACM Transactions on Intelligent Systems and Technology (TIST)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-10-21
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Knowledge graphs Diversity Entity linking Music Novelty Recommender systems
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Theoretical Computer Science

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

A Semantic Hybrid Approach for Sound Recommendation

Beyond Relevance: Explicitly Promoting Novelty and Diversity in Tag Recommendation

Novelty and Diversity in Top-N Recommendation -- Analysis and Evaluation

A Novel Classification Framework for Evaluating Individual and Aggregate Diversity in Top-N Recommendations

Personalized Recommendation Based on Evolutionary Multi-Objective Optimization [Research Frontier]

On Unexpectedness in Recommender Systems: Or How to Better Expect the Unexpected (TIST, Vol.5, Issue 4)

Anytime Algorithms for Recommendation Service Providers

Reliable medical recommendation systems with patient privacy

Diversity, Serendipity, Novelty, and Coverage: A Survey and Empirical Analysis of Beyond-Accuracy Objectives in Recommender Systems

Sound and Music Recommendation with Knowledge Graphs

Similar Documents

A Semantic Hybrid Approach for Sound Recommendation

Beyond Relevance: Explicitly Promoting Novelty and Diversity in Tag Recommendation

Novelty and Diversity in Top-N Recommendation -- Analysis and Evaluation

A Novel Classification Framework for Evaluating Individual and Aggregate Diversity in Top-N Recommendations

Personalized Recommendation Based on Evolutionary Multi-Objective Optimization [Research Frontier]

On Unexpectedness in Recommender Systems: Or How to Better Expect the Unexpected (TIST, Vol.5, Issue 4)

Anytime Algorithms for Recommendation Service Providers

Reliable medical recommendation systems with patient privacy

Diversity, Serendipity, Novelty, and Coverage: A Survey and Empirical Analysis of Beyond-Accuracy Objectives in Recommender Systems

Sound and Music Recommendation with Knowledge Graphs