(sowemine workshop) "#nowplaying on #spotify: leveraging spotify information on twitter for...

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The University of Innsbruck was founded in 1669 and is one of Austria’s oldest universities. Today, with over 28.000 students and 4.000 staff, it is

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#nowplaying on #Spotify: Leveraging Spotify

Information on Twitter for Artist RecommendationsMartin Pichl, Eva Zangerle and Günther Specht

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Agenda

• Why Music Recommendations?

• Dataset Creation & Recommendation Approach

• Discussion and Future Work

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Recent Trends

• Rise of the web enabled new distribution channels

• Online Stores

• Music Streaming Platforms

• …

• These new distribution channels

– Exploit a word-wide market

– Virtually no inventory costs

→ More and more dives music is available

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Why (Music) Recommender Systems?

• The user is confronted with more and more diverse music

– on streaming platforms

– in online stores

– on mobile devices

• and has a free choice

• Users often do not know what to listen to

→ Information Overload

• Recommender Systems

– Helps users finding music they like

→ Increase usability

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Research on Music Recommender System

• Publicly available data necessary

• Twitter

– People share what they are listening at the moment

• Get additional information from Spotify

– Additional listening events

– Additional information about the tracks and the artists

– Additional information about the listening context

• The additional information is necessary to build a more

specialized recommender system

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Example Tweets

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The Dataset

• Generated dataset based Tweets that contains

– <UserID, ArtistID, TrackID>-triples

– Boolean preferences (listened/not listened)

• Cleaning

– Removed duplicates

– Removed certain accounts i.e. @SpotifyNowPlaying

– Removed “Various Artists”

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Dataset Snapshot

• Dataset contains

– 513,489 listening events

– by 68,045 unique users

– listening to 97,586 unique tracks

– by unique 40,593 artists

• Distribution

– In average 4.77 tweets per user (SD= 30.02)

– Median of 2

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Artist Recommendations using this Dataset

• No content based information

– Recommendations are computed using collaborative filtering

• Collaborative Filtering (CF)

– CF recommends items that the most similar users of a user

listened to (and are new to the user)

• CF relies on

– A user similarity measure

– A number of nearest neighbors 𝑘

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User Similarity

• Boolean Preferences

– Jaccard Coefficient is suitable

– 𝐽𝑎𝑐𝑐𝑎𝑟𝑑𝑖,𝑗 =𝑆𝑖 ∩ 𝑆𝑗

𝑆𝑖 ∪ 𝑆𝑗

• Include all the information available

– Compute Jaccard Coefficient using the artist listening history

– Compute Jaccard Coefficient using the track listening history

– Combined using an weighted average

• 𝑢𝑠𝑒𝑟𝑆𝑖𝑚 = 𝑤𝑎 ∗ 𝑎𝑟𝑡𝑖𝑠𝑡𝑆𝑖𝑚 + 𝑤𝑡 ∗ 𝑡𝑟𝑎𝑐𝑘𝑆𝑖𝑚

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Parameter Tuning

• Input Parameters

– 𝑤𝑎, 𝑤𝑡, 𝑘

– Optimized using a Genetic Algorithm (GA)

– Fitness = Precision of the recommender system

– In average a good solution was found after 4.14 iterations

(SD=2.27)

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Genetic Algorithm

• 𝑤𝑎,𝑤𝑡, 𝑘 are float point genes between 0 and 1 and form a

individual

• Random initial distribution

• The fitness of each individual is measured using the

precision

• Crossover and mutations of the best individual

• Terminate if the precision is 1 or a certain number of

generations is reach

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The Big Picture

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Evaluation Setup

• Offline Evaluation

– From each user we removed 1/3 of the listening events for

testing

– Recommended 𝑝 ∗ 𝑆𝑖𝑧𝑒 𝑜𝑓 𝑡ℎ𝑒 𝑇𝑒𝑠𝑡𝑠𝑒𝑡 items

– Varied 𝑝 between 0 and 1

– Computed precision and recall for each 𝑝

• Parameters used for the Evaluation

– 𝑤𝑎 = 0.21

– 𝑤𝑡 = 0.94

– 𝑘 = 59

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Evaluation Metrics

• Hit: Item found in the testset

• 𝑝𝑟𝑒𝑐𝑖𝑠𝑜𝑛 =ℎ𝑖𝑡𝑠

𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑟𝑒𝑐𝑜𝑚𝑚𝑒𝑛𝑑𝑎𝑡𝑖𝑜𝑛𝑠

• Relevant Items: All items in the testset

• 𝑟𝑒𝑐𝑎𝑙𝑙 =ℎ𝑖𝑡𝑠

𝑠𝑖𝑧𝑒 𝑜𝑓 𝑡ℎ𝑒 𝑡𝑒𝑠𝑡𝑠𝑒𝑡

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Performance of the optimized Recommender

Systemn Precision Recall

1 0.4656 0.0228

2 0.3622 0.0547

3 0.3137 0.0782

4 0.2812 0.1003

5 0.2531 0.1195

6 0.2315 0.1286

7 0.2170 0.1396

8 0.2170 0.1396

9 0.1871 0.1583

10 0.1871 0,1583

0

0,1

0,2

0,3

0,4

0,5

Pre

cisi

on

/ R

ecal

l

Number of Recommendations (% of the Testset)

Precision

Recall

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Discussion

• Heading into the right direction

• Performance is limited for a high number of

recommendations

– Data sparsity

– Too general approach

• Performance improvements with

– Reducing data sparsity

– Specialized algorithm that fits more to music

recommendation

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Next Steps towards a more specialized RS

• Match Spotify and Twitter Users

– Early experiments show that we can match ~ 10% of the

dataset

– Better matching than using the username and played tracks?

• Extract listening context from playlist names, i.e.

– Christmas

– Workout, training

– Driving

– …

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Next Steps towards a more specialized RS

• The offline evaluation is rather limited

• Create an intuitive webinterface

• Conduct a live user experiment

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Acknowledgments

(sowemine workshop) "#nowplaying on #spotify: leveraging spotify information on twitter for...

Internet

information recommendations

artists additional information

music recommendations

information visit

music recommender systems

user cf

leveraging spotify information

dataset snapshot dataset