1. LYRIC-BASED ARTIST NETWORK METHODOLOGY Derek Gossi CS 765 Fall 2014

2. Topics Review The Music Recommendation Problem Existing Research A Quick Review The Dataset Methodology

3. REVIEW The Music Recommendation Problem

4. Approaches to Recommendation Collaborative Filtering Users that liked this artist/song also liked that artist/song Amazon, iTunes store, Spotify Tagging Categorization based on user-generated or pre-defined tags Calm, sad, romantic, cheerful, anxious, depressed Last.fm Content-based Look at the audio signal Not widely used in industry yet Pandora, Spotify (in progress) What can the lyrics tell us?

5. The Issue Collaborative Filtering works well when there is lots of user feedback, but it doesn’t scale well Content-based methods scale very well, because no user data is required Results are mixed and not widely used in practice What would a lyric-based recommendation network look like, and how would it compare to existing collaborative filtering networks?

6. EXISTING RESEARCH A Quick Review

7. Network Topology P. Cano, O. Celma, and M. Koppenberger. “The topology of music recommendation networks,” Feb 2008. Analyzes four music recommendation systems from a network perspective Directed edges n = 16,302 (Yahoo) to 51,616 (MSN) m = 158,866 (AMG) to 511,539 (Yahoo) Small-world properties in all networks Average shortest path < 8 Clustering coefficient from 0.14 (Amazon) to 0.54 (MSN)

10. THE DATASET The Million Song Dataset (MSD)

11. Million Song Dataset Open source dataset released in Feb 2011 Metadata and audio features for a million contemporary audio tracks Linked to separate datasets with lyrical data, Last.fm tags, and actual user data

12. METHODOLOGY

13. Main Idea What can lyrics tell us about music recommendation? Build a network of musical artists where edges represent lyrical similarity Build a comparable network based on actual user play count data utilizing a collaborative filtering methodology Analyze and compare topology of both networks Use a clustering algorithm on both networks to obtain clusters with strongly connected neighbors Figure out which tag categories, if any, are associated with these clusters What are users saying?

14. Data Cleaning Lyrics provided in bag-of-words format Stop words removed Porter2 stemming algorithm “Dictionary” limited to top 5,000 words 92% of complete set Terms outside limited list are generally noisy and unusable

15. Term Frequency Matrix Vector Space Model (VSM) Represent songs as sparse vectors in n-dimensional space n = 5,000

16. Term Frequency Matrix Could sum song vectors together for a particular artist to get term frequencies of an entire artist’s catalog However, this would lose important information about variance of individual song vectors for a given artist Reduce to artist level after song links are formed

17. TD-IDF Weighting Term frequency matrix can be used to directly compare similarity of two songs But what about frequently used and statistically unimportant words? The, at, which, on, by Idea: make words used frequently across all songs in the dataset less important Multiply term frequency (TF) by inverse document frequency (IDF)

18. Pairwise Similarity Matrix As songs are vectors, we can calculate similarity in terms of the angle between them in the vector space Cosine similarity is often used in document classification 0 implies two songs are orthogonal (completely different) and 1 implies two songs are identical

19. Threshold Selection We now have a [0,1] range of how similar each song in the dataset is to every other song Simplest idea: Pick some threshold in [0,1] and create an edge if threshold is exceeded Issues: Network topology can be significantly impacted by threshold selection, no intuitive explanation, some songs are left out of network (scale issues) Instead, fix outdegree by using a weighted k shared neighbors approach Given a user liked a given song, which k songs would be recommended based on lyrics?

20. Threshold Selection

21. Reduction to Artist Level How many songs from artist i to artist j have edges between them? Create an edge if 1 or greater, weight edge if strictly greater than 1 by summing existing weights

22. Collaborative Filtering Network Echo Nest Taste Profile Subset Play counts for 1,019,318 users and 48,373,586 user/song/count triples Item-based collaborative filtering Once again, song are represented as vectors For a given song, i th component of a song vector is number of plays by user i Pairwise similarity computed and network generated to match lyric network

23. Tag Data Last.fm tags are linked to the nodes in both networks Restricted to high-frequency tags representing: Genre Mood Musical Style

24. Topology Comparison We now have two networks with fixed outdegree of k and indegree of unknown distribution What are the most important nodes, and what are the tags associated with these nodes? Degree distribution How clustered is the network by genre, style, or mood?

25. Clustering We want to see what the strongest communities are in each of the two networks L. Erto ̈ z, M. Steinbach, V. Kumar. “Finding topics in collections of documents: a shared nearest neighbors approach” Main idea: avoid clustering two nodes together that are in different classes by ensuring every node shares strongly connected neighbors

26. Clustering Strong Link Threshold: Min # of shared neighbors Topic Threshold: Represents its neighborhood Merge Threshold: Nodes appear together in a cluster Noise Threshold: Nodes are not included Labeling Threshold: Scan clusters and add strong links

27. Clustering Which tags are associated with these clusters? How do the two networks compare to each other? What can this tell us about music recommendation? Collaborative filtering works well in practice

28. Conclusion How do we recommend music without user data? Content-based methods may work Do the lyrics correspond to what people say about music? What does this tell us about lyrical expression in general?

29. QUESTIONS?