The document describes a unified music recommendation system that combines users' listening habits and semantics of tags. It proposes generating three types of user profiles: listening habits-based, tag-based, and a hybrid approach. A tag and emotion ontology are used to preprocess tags and assign weights. A music recommendation algorithm finds similar users and calculates item scores. An evaluation of the approaches found the hybrid method achieved the best precision and recall based on F-measure, outperforming listening habits only or tag-based recommendations. Statistical analysis confirmed the hybrid approach performed significantly better.
The current revolution in the music industry represents great opportunities and challenges for music recommendation systems. Recommendation systems are now central to music streaming platforms, which are rapidly increasing in listenership and becoming the top source of revenue for the music industry. It is increasingly more common for a music listener to simply access music than to purchase and own it in a personal collection. In this scenario, recommendation calls no longer for a one-shot recommendation for the purpose of a track or album purchase, but for a recommendation of a listening experience, comprising a very wide range of challenges, such as sequential recommendation, or conversational and contextual recommendations. Recommendation technologies now impact all actors in the rich and complex music industry ecosystem (listeners, labels, music makers and producers, concert halls, advertisers, etc.).
Компания
J
’
son
&
Partners
Consulting
представляет результаты исследования рынка мобильных
музыкальных сервисов по итогам 2012 года и прогнозы его развития до 2016 года.
В рамках традиционного исследования рынков мобильной рекламы и маркетинга компания J’son & Partners Consulting представляет краткие результаты исследования рынка SMS-маркетинга и SMS-рассылок в России.
The current revolution in the music industry represents great opportunities and challenges for music recommendation systems. Recommendation systems are now central to music streaming platforms, which are rapidly increasing in listenership and becoming the top source of revenue for the music industry. It is increasingly more common for a music listener to simply access music than to purchase and own it in a personal collection. In this scenario, recommendation calls no longer for a one-shot recommendation for the purpose of a track or album purchase, but for a recommendation of a listening experience, comprising a very wide range of challenges, such as sequential recommendation, or conversational and contextual recommendations. Recommendation technologies now impact all actors in the rich and complex music industry ecosystem (listeners, labels, music makers and producers, concert halls, advertisers, etc.).
Компания
J
’
son
&
Partners
Consulting
представляет результаты исследования рынка мобильных
музыкальных сервисов по итогам 2012 года и прогнозы его развития до 2016 года.
В рамках традиционного исследования рынков мобильной рекламы и маркетинга компания J’son & Partners Consulting представляет краткие результаты исследования рынка SMS-маркетинга и SMS-рассылок в России.
Crowsourcing for Social Multimedia Task: Crowsorting Timed Comments about Musicmultimediaeval
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http://ceur-ws.org/Vol-1263/mediaeval2014_submission_78.pdf
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Presenters: Dmitry Bogdanov, Universitat Pompeu Fabra, Spain
Alastair Porter, Universitat Pompeu Fabra, Spain
Hendrik Schreiber, tagtraum industries incorporated
Paper: http://ceur-ws.org/Vol-1984/Mediaeval_2017_paper_6.pdf
Video: https://youtu.be/NpN2Fr3go_Y
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Abstract: This paper provides an overview of the AcousticBrainz Genre Task organized as part of the MediaEval 2017 Benchmarking Initiative for Multimedia Evaluation. The task is focused on content-based music genre recognition using genre annotations from multiple sources and large-scale music features data available in the AcousticBrainz database. The goal of our task is to explore how the same music pieces can be annotated differently by different communities following different genre taxonomies, and how this should be addressed by content-based genre recognition systems. We present the task challenges, the employed ground-truth information and datasets, and the evaluation methodology.
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4.6.16 AI&BigData Lab
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Это — рекомендательная система. Если взглянуть на нее со стороны, то она крепко застряла между Collaborative filtering и Content-based filtering. Используются рекомендательные системы уже давно, но рекомендации все еще не идеальны. Обычно проблемы — это выбор технологий или там фреймворка… А у нас — cold-start problem, semantic gap и др.!
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Internet and E-commerce are the generators of abundant of data, causing information Overloading. The problem of information overloading is addressed by Recommendation Systems (RS). RS can provide suggestions about a new product, movie or music etc. This paper is about Music Recommendation System, which will recommend songs to users based on their past history i.e. taste. In this paper we proposed a collaborative filtering technique based on users and items. First user-item rating matrix is used to form user clusters and item clusters. Next these clusters are used to find the most similar user cluster or most similar item cluster to a target user. Finally songs are recommended from the most similar user and item clusters. The proposed algorithm is implemented on the benchmark dataset Last.fm. Results show that the performance of proposed method is better than the most popular baseline method.
The World Wide Web is moving from a Web of hyper-linked documents to a Web of linked data. Thanks to the Semantic Web technological stack and to the more recent Linked Open Data (LOD) initiative, a vast amount of RDF data have been published in freely accessible datasets connected with each other to form the so called LOD cloud. As of today, we have tons of RDF data available in the Web of Data, but only a few applications really exploit their potential power. The availability of such data is for sure an opportunity to feed personalized information access tools such as recommender systems. We will show how to plug Linked Open Data in a recommendation engine in order to build a new generation of LOD-enabled applications.
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Crowsourcing for Social Multimedia Task: Crowsorting Timed Comments about Musicmultimediaeval
This paper provides an overview of the Crowdsorting Timed Comments about Music Task, a new task in the area of crowdsourcing for social media offered by the MediaEval 2014 Multimedia Benchmark. Data for this task is a set of Electronic Dance Music (EDM) tracks, collected from online music sharing platform Soundcloud. Given a set of noisy labels for segments of Electronic Dance Music (EDM) that were collected on Amazon Mechanical Turk, the task is to predict a single `correct' label. The labels indicate whether or not a `drop' occurs in the particular music segment. The larger aim of this task is to contribute to the development of hybrid human/conventional computation techniques to generate accurate labels for social multimedia content. For this reason, participants are also encouraged to predict labels by combining input from the crowd (i.e., human computation) with automatic computation (i.e., processing techniques applied to textual metadata and/or audio signal analysis).
http://ceur-ws.org/Vol-1263/mediaeval2014_submission_78.pdf
Scala Data Pipelines for Music RecommendationsChris Johnson
Are you still building data pipelines with Java and Python? Are you curious about the current buzz in the Big Data community surrounding Scala as a data processing environment? In this talk I'll discuss how Spotify migrated its music recommendations pipeline from Python to Scala. I'll dive into the language specific features that make Scala the ideal candidate for big data processing as well as highlight the rich set of tools and APIs that we take advantage of to process music recommendations for our 50 Million active users including Scalding, Breeze, Kafka, Spark, Parquet, Driven and Zeppelin.
WWW2014: Long Time No See: The Probability of Reusing Tags as a Function of F...Dominik Kowald
WWW2014 - WebScience Track
Long Time No See: The Probability of Reusing Tags as a Function of Frequency and Recency
Dominik Kowald, Paul Seitlinger, Christoph Trattner, Tobias Ley
Improving Semantic Search Using Query Log AnalysisStuart Wrigley
Despite the attention Semantic Search is continuously gaining, several challenges affecting tool performance and user experience remain unsolved. Among these are: matching user terms with the searchspace, adopting view-based interfaces in the Open Web as well as supporting users while building their queries. This paper proposes an approach to move a step forward towards tackling these challenges by creating models of usage of Linked Data concepts and properties extracted from semantic query logs as a source of collaborative knowledge. We use two sets of query logs from the USEWOD workshops to create our models and show the potential of using them in the mentioned areas.
The MediaEval 2017 AcousticBrainz Genre Task: Content-based Music Genre Recog...multimediaeval
Presenters: Dmitry Bogdanov, Universitat Pompeu Fabra, Spain
Alastair Porter, Universitat Pompeu Fabra, Spain
Hendrik Schreiber, tagtraum industries incorporated
Paper: http://ceur-ws.org/Vol-1984/Mediaeval_2017_paper_6.pdf
Video: https://youtu.be/NpN2Fr3go_Y
Authors: Dmitry Bogdanov, Alastair Porter, Julián Urbano, Hendrik Schreiber
Abstract: This paper provides an overview of the AcousticBrainz Genre Task organized as part of the MediaEval 2017 Benchmarking Initiative for Multimedia Evaluation. The task is focused on content-based music genre recognition using genre annotations from multiple sources and large-scale music features data available in the AcousticBrainz database. The goal of our task is to explore how the same music pieces can be annotated differently by different communities following different genre taxonomies, and how this should be addressed by content-based genre recognition systems. We present the task challenges, the employed ground-truth information and datasets, and the evaluation methodology.
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MusicFX is an example of an active environment that uses a group preference arbitration system to allow the members of a fitness center to influence, but not directly control, the selection of music in that environment. The system contains a database of members' musical preferences, a badge system for determining who is working out, and a weighted random selection algorithm for selecting music to best suit the group inhabitants at any given time. MusicFX was deployed in the fitness center at Accenture Technology Park in Northbrook, IL (USA) from November 1997 through January, 2002. These slides are from the CSCW 98 presentation on the system. More info, including the CSCW 98 paper, can be found at http://interrelativity.com/joe/projects/MusicFX.html
AI&BigData Lab 2016. Игорь Костюк: Как приручить музыкальную рекомендательную...GeeksLab Odessa
4.6.16 AI&BigData Lab
Upcoming events: goo.gl/I2gJ4H
Это — рекомендательная система. Если взглянуть на нее со стороны, то она крепко застряла между Collaborative filtering и Content-based filtering. Используются рекомендательные системы уже давно, но рекомендации все еще не идеальны. Обычно проблемы — это выбор технологий или там фреймворка… А у нас — cold-start problem, semantic gap и др.!
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Internet and E-commerce are the generators of abundant of data, causing information Overloading. The problem of information overloading is addressed by Recommendation Systems (RS). RS can provide suggestions about a new product, movie or music etc. This paper is about Music Recommendation System, which will recommend songs to users based on their past history i.e. taste. In this paper we proposed a collaborative filtering technique based on users and items. First user-item rating matrix is used to form user clusters and item clusters. Next these clusters are used to find the most similar user cluster or most similar item cluster to a target user. Finally songs are recommended from the most similar user and item clusters. The proposed algorithm is implemented on the benchmark dataset Last.fm. Results show that the performance of proposed method is better than the most popular baseline method.
The World Wide Web is moving from a Web of hyper-linked documents to a Web of linked data. Thanks to the Semantic Web technological stack and to the more recent Linked Open Data (LOD) initiative, a vast amount of RDF data have been published in freely accessible datasets connected with each other to form the so called LOD cloud. As of today, we have tons of RDF data available in the Web of Data, but only a few applications really exploit their potential power. The availability of such data is for sure an opportunity to feed personalized information access tools such as recommender systems. We will show how to plug Linked Open Data in a recommendation engine in order to build a new generation of LOD-enabled applications.
(Lecture given @ the 11th Reasoning Web Summer School - Berlin - August 1, 2015)
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And...
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Charlie Greenberg, Host
1. A Unified Music Recommender System Using
Users’ Listening Habits and Semantics of Tags
Hyon Hee Kim
Department of Statistics and Information Science,
Dongduk Women’s University
2. Outline
• Motivation & Objectives
• Overview of the System
• Generation of User Profiles
• A Unified Music Recommendation
• Performance Evaluation
• Related Work
• Conclusions and Future Work
3. Motivation (1/3)
• In a Social Music Site
– Music recommendation is essential.
– Music recommendation is different from other product recommendation
• Explicit information : Rating system
• Implicit information : the number of plays
• Listening habits-based User Profiling
– Cold Start Problem
• A new users with little information
• A new items with only a few ratings
– Data Sparsity Problem
• Data is very small compared to needed music items
4. Classic rock
british
pop
rock
• Collaborative Tagging
– A tool for users to represent their preferences about web resources
– Users add keywords which are freely chosen by themselves to web resources
– Using tag data for user profiling in personalized recommender systems
• Tag-based User Profiling
– More Easily added tags without listening to music
– Semantically meaningful tags
Motivation (2/3)
5. Motivation (3/3)
• In the case of last.fm
• Factual Tags
– 85% of tags
– genre, region, instrumentation
• Emotional Tags
– 10% of tags
– opinion, sentiment, mood
• Personal Tags
– 5% of tags
– to organize, to browse, etc.
6. Objectives
• A Novel Approach to Music Recommendation
– Combining listening habits and semantics of tags
• Using a Tag Ontology and an Emotion Ontology
– UniTag: Resolving semantic ambiguity of tags
– UniEmotion: Assigning weighted values to the emotional tags
→ Semantically Enhanced Music Recommendation
7. Outline
• Motivation & Objectives
• Overview of the System
• Generation of User Profiles
• A Unified Music Recommendation
• Performance Evaluation
• Related Work
• Conclusions and Future Work
9. Outline
• Motivation & Objectives
• Overview of the System
• Tag-based User Profiling
– Preprocessing of tags
– Algorithms for generating user profiles
– Preliminary experimental results
• A Unified Music Recommendation
• Performance Evaluation
• Related Work
• Conclusions and Future Work
10. Preprocessing of Tags (1/3)
• A tag does not have any pre-defined term or hierarchies of a term
• Problems of tag data
– Synonymy
• Different words represents the same meaning
• E.g., hiphop, hip-hop, hip hop/ R & B, Rhythm and Blues, Blues
– Polysemy
• A single word contains multiple meanings
• E.g., French => French rock, French pop, French artist
– Spelling variants
• misspelling
• Foreign language
12. Preprocessing of Tags (3/3)
• Rules for reasoning prefix
– French rock, progressive rock, post rock=> rock
(Tag (?t) ^ tagPrefix (?t, ?p) ^ Prefix(?p) ^ subTag(?t, ?s) ^ Rtags (?s) ->
classifiedAs (?t, ?s)
• Rules for reasoning expert knowledge
– Soul => rhythm and blues, rhythm and blues => blues then Soul => blues
(Tag (?t) ^ isKindof (?t, ?A) ^ isKindof (?A, ?B) -> isKindof (?t, ?B)
• Rules for reasoning synonym
– Hip-hop, hiphop => hip hop
(Tag(?t) ^tagVariation (?t, ?R) ^ istheSameAs (?t, ?s) -> tagVariation (?s, ?R)
13. Algorithm for Generating User Profiles (1/2)
Algorithm 1. Generation of A Tag-based Profile
Input: set of Representative tags Tr, set of a user’s tag Tu
Output: set of frequencey for each representative tag of the user FTr
var RTags[] = {rock, hiphop, electronic, metal, jazz, rap, funk, folk, blues, reggae}
var tagFrequency[] = { }, tempFrequency [] = { }
var RTag = null
while ∃next tag t in Tu do
RTag = FindRTag (t)
If Rtag == RTags [i] then
{ tempFrequency[i] = tempFrequency[i] + 1
tagFrequency [i] = tempFrequency [i] }
else
tagFrequency [i] = tempFrequency [i]
endwhile rock hiphop electronic metal jazz rap funk folk blues reggae
user1 6 2 2 3 2 4 3 1 1 1
user2 5 0 0 0 0 0 0 0 1 0
user3 2 2 1 1 1 1 2 0 0 1
user4 10 1 0 1 2 0 2 3 3 1
user5 1 4 0 0 0 4 1 0 0 0
Table 1. An example of tag-based profiles
14. Algorithm for generating User Profiles (2/2)
Algorithm 2. Generation of A Track-based Profile
Input: set of tracks of a usr TRu, set of Representative tags Tr
Output: set of number of a user’s tracks for each representative musical genre Tn
var RTags[] = {rock, hiphop, electronic, metal, jazz, rap, funk, folk, blues, reggae}
var numTrack[ ] = { }, tempnumTrack [ ] = { }
var RTrack = null
while ∃next tag t in Tu do
RTrack = FindGenre (t)
If Rtrack == RTags [i] then
{ tempnumTrack [i] = tempnumTrack[i] + 1
numTrack[i] = tempnumTrack [i] }
else
numTrack [i] = tempnumTrack [i]
endwhile rock hiphop electronic metal jazz rap funk folk blues reggae
User1 65 176 5 4 0 168 0 3 0 0
User2 411 8 11 109 3 5 8 1 0 0
User3 157 7 11 10 6 2 1 39 4 2
User4 257 20 9 18 2 5 0 9 0 0
User5 110 277 15 8 6 85 10 3 2 7
Table 2. An example of track-based profiles
15. Preliminary Experimental Results (1/3)
• 1,000 user data set from Last.fm
– Users, tags, music items
• Standardization
– To remove extensive preference
• K-Means clustering algorithm
– Canopy Clustering
– 6 centroid points and 6 clusters
17. Preliminary Experimental Results (3/3)
– T-test
• Mean of inter-cluster distances of tag-based profiles
• Mean of inter-cluster distances of track-based profiles
N Mean Std Dev t p-value
Tag-based profiles 15 0.8325 0.6834
2.55 0.0165
Track-based profiles 15 0.3785 0.0885
Table 5. T-test result for the means of inter-cluster distances
18. Outline
• Motivation & Objectives
• Overview of the System
• Generation of User Profiles
• A Unified Music Recommendation
– UniEmotion Ontology
– Generation of User Profiles
– Music Recommendation Algorithm
• Performance Evaluation
• Related Work
• Conclusions and Future Work
21. UniEmotion Ontology (3/5)
• Intensity of emotional tags
– Strong
• Positive value >= 0.75 or Negative value>= 0.75
– Middle
• 0.25 <= Positive value <= 0.75 or
• 0.25 <= Negative value <= 0.75
– Weak
• Positive value < 0.25 and Negative value < 0.25
22. UniEmotion Ontology (4/5)
• Assigning the weights to the tags
– Factual tags: 1
– Positive tags
• Strong: 2.5
• Middle: 2
• Weak: 1.5
– Negative tags
• Strong: -2.5
• Middle: -2
• Weak: -1.5
• Final score of an item => sum of the weights
23. UniEmotion Ontology (5/5)
• Two classes
– UniEmotion:Positive
• Emotional tags belonging to the positive emotional categories
• trust, surprise, anticipation, and happiness
– UniEmotion:Negative
• Emotional tags belonging to the negative emotional categories
• disgust, anger, fear, and sadness
• Two properties
– UniEmotion:Intensity
• Specifying the intensity of tags
– UniEmotion:Weight
• Specifying the weight of tags
24. Generation of User Profiles (1/2)
1. Listening habits-based User Profiles
– U1 = {u1, u2, …, um}, I1 = {i1, i2, …, in},
– <u, I, n>
• N: number of plays
2. Tag score-based User Profiles
– U2 = {u1, u2, …, um}, I2 = {i1, i2, …, in},
– <u, I, s>
• S: scores of tags assigned by UniEmotion ontology
3. Hybrid User Profiles
– U3 = {u1, u2, …, um}, I3 = I1 ∩ I2,
– <u, I, m>
• M = α * n +(1- α) * s; α = 0.5
25. Generation of User Profiles (2/2)
1. Listening habits-based
User profiles
2. Tag score-based
User profiles
3. Hybrid
User profiles
26. Music Recommendation Algorithm (1/2)
• Finding Similar Users
– Pearson Correlation Similarity
• Calculating scores of items
– Considering the similar users’ rates
• Recommending top n items
27. Music Recommendation Algorithm (2/2)
Input: a set of user profiles UP
Output: a set of recommended items RI
1. For all yi ∈ U
Compute a similarity s between X and yi.
2. Sort by similarity
3. Select top n neighbors
4.
5. For all
Compute a similarity t between x and
For all
preference +=t * pref
6. Rank by preference
7. Select top n items
28. Outline
• Motivation & Objectives
• Overview of the System
• Generation of User Profiles
• A Unified Music Recommendation
• Performance Evaluation
• Related Work
• Conclusions and Future Work
29. Performance Evaluation
• Implementation Environment: Apache Web Server
– User database : MySQL 5.0
– Listening habits collector, tag score generator: PHP
– Recommendation Engine: Apache Mahout
– UniTag and UniEmotion Ontology: JDK6.0
• Experimental Data
– 1, 000 user information from last.fm [http://mir.dcs.gla.ac.uk/]
– Containing 18,700 artist and 12,600 tags
– 70% training data, 30% test data
30. Performance Evaluation
• Evaluation Model
– Recommended items
• Items which users are interested in (True Positive, TP)
• Items which users are not (False Positive, FP)
– Items which are not recommended
• Items which users are interested in (False Negative, FN)
• Items which users are not interested in (True Negative, TN)
– Precision P = TP/ TP+ FP
• # of correct recommendation/# of all recommended items
– Recall R = TP / TP+FN
• # of correct recommendation/# of preferred items
– F-measure F = 2* P* R / P+R
• Harmonic average between precision and recall
31. Experimental Results (1/3)
• Precisions
[Number of similar users] [Number of recommended items]
A: Listening habits-based approach
B: Tag-based approach
C: Hybrid approach
32. Experimental Results (2/3)
• Recalls
[Number of similar users] [Number of recommended items]
A: Listening habits-based approach
B: Tag-based approach
C: Hybrid approach
33. Experimental Results (3/3)
• F-measure
[Number of similar users] [Number of recommended items]
A: Listening habits-based approach
B: Tag-based approach
C: Hybrid approach
34. Statistical Validation
• One-way ANOVA about three groups
– Method1: listening habits-based approach
– Method2: tag-based approach
– Method3: hybrid approach
• Tukey Multiple Comparison Test
– Asymmetric distributions
• Log transformation
– Different characters in case two groups have significant
difference
35. Method 1 2 3 F
Mean of log(prec) -3.962B -4.036B -2.879A 34.27***
Mean
Precision(SD)
0.020
(0.006)
0.020
(0.009)
0.068
(0.040)
N 24 24 24
Method 1 2 3 F
Mean of log(recall) -3.285B -4.099c -2.635A 26.80***
Mean
Recall (SD)
0.044
(0.023)
0.019
(0.010)
0.093
(0.056)
N 24 24 24
<Table1. test for precision> ***: p<0.001
<Table2. test for recall> ***:p<0.001
Method 1 2 3 F
Mean of log(F-measure) -3.748B -4.117c -2.894A 41.31***
Mean
F-measure (SD)
0.024
(0.006)
0.018
(0.008)
0.06
(0.034)
N 24 24 24
<Table2. test for F-measure> ***: p<0.001
36. Related Work
• MusicBox
– A personalized music recommender system based on social tags
– 3-order tensors model
– The method improves the recommendation quality
• Foafing the music
– Collecting music information in a semantic web environment
– User information, music information, concert information
– Recommendation of similar music items
• OntoEmotions
– An ontology of emotional categories covering the basic emotions
– Armeteo art portal
– New relations can be inferred by reasoning on the ontology of emotions
37. Conclusions
• Solution to Cold Start Problem
– It takes time to collect users’ listening habits.
– Adding tags is easily done
– Tags look like word-of-mouth
• Performance Enhancement
– Precision, Recall, F-measure
– Hybrid approach > listening habits-based approach, tag-based approach
38. Future Work
• Elaborating UniEmotion Ontology
– Emerging Internet Slangs
• Item Selection
– Product Network Analysis Considering Tags
– Analyzing short description