Innovative Music Recommendation Strategies for Diverse Audiences
Explore cutting-edge methods for diversifying music recommendations, featuring approaches like Jaccard Swap and Submodular diversity. Discover why varying music streams is essential, considerations for explicit clusters and user behavior, and the benefits of Amazon Prime Music. Uncover the significance of explanation-based diversity in recommender systems and the Jaccard diversity distance concept.
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Presentation Transcript
Diversifying Music Recommendations Houssam Nassif, Kemal Oral Cansizlar, Mitchell Goodman, S.V.N. Vishwanathan houssamn@amazon.com
Outline Motivation Jaccard Swap diversity method Submodular diversity method Experiment Diversifying Music Recommendations 2
Why diversify music stream? Diversifying Music Recommendations 3
Music considerations Explicit clusters: album, artist Same album: same meta-data (album cover graphic, title) User behavior: play album songs back-to-back Similar scores to same-album songs Diversifying Music Recommendations 4
About Amazon Prime Music Free benefit for prime members Millions of songs Thousands of expert-programmed playlists Upload your own music Create personal playlists Diversifying Music Recommendations 5
Amazon Prime Music mobile app Access your music from anywhere List-form recommendation Devices with limited interaction capability Diversifying Music Recommendations 6
Outline Motivation Jaccard Swap diversity method Submodular diversity method Experiment Diversifying Music Recommendations 7
Explanation-based diversity C. Yu, L. Lakshmanan, S. Amer-Yahia. It takes variety to make a world: Diversification in recommender systems. EDBT 2009. ?: user, ?: item ItemSim ?,? : similarity measure between two items Items(?): Set of items user ? interacted with Expl ?,? = ? ItemSim ?,? > ? & ? Items(?)} Diversifying Music Recommendations 8
Jaccard diversity distance Expl ?,? Expl ?,? Expl ?,? Expl ?,? ??(?,?) = 1 ???,? = 1 if explanation sets completely separate ???,? = 0 if explanation sets are identical Diversifying Music Recommendations 9
Algorithm Swap ??= ????,? ??(?,?) Explanatory set Recommender score 1.17 7 2/3 1.66 6.2 1/2 And if score < ? If diversity increases 1 1.5 6 1.5 5 1 Diversifying Music Recommendations 10
Outline Motivation Jaccard Swap diversity method Submodular diversity method Experiment Diversifying Music Recommendations 11
Diminishing returns Set utility +3 +5 Incremental utility tapers off +11 Cumulative number of explanation items in set Diversifying Music Recommendations 12
Submodular diversity mix +3 +5 +11 +2 +4 Diversified list: Diversifying Music Recommendations 13
Submodular diversity ?: category, ?: item, ?: diversified set, score(?): ? s recommender score Category utility: ??(?) = log 1 + score(?) ? ? ? Maximize sum of all category utilities: ??????? ? ? = ?? ? ? Greedy near-optimal solution: ??+1= ?? {???????\??? ?? {?} } Diversifying Music Recommendations 14
Outline Motivation Jaccard Swap diversity method Submodular diversity method Experiment Diversifying Music Recommendations 15
Experimental setup Baseline: Rank by recommender score Item-to-item collaborative filtering recommender provides item score and explanation set Artist and album as Jaccard explanation set features and submodular categories ( , , , ) Randomized controlled trial with equal customer allocation Diversifying Music Recommendations 16
Results Treatment comparison Increase in minutes streamed Submodularity vs Baseline 0.64% (p=0.03) Jaccard Swap vs Baseline 0.40% (p=0.18) Submodularity vs Jaccard Swap 0.24% (p=0.41) Diversity affects recommendation quality Submodularity method improvement is significant Diversifying Music Recommendations 17
Baseline vs Submodular Diversifying Music Recommendations 18
Submodular approach benefits Smoothness: Submodularity produces uniformly diverse set. All contiguous subsets are also diverse. Jaccard Swap doesn t. Relevance: Swap may not retain most relevant content. Submodularity ensures most relevant item is first, followed by mix of most relevant items within each category. Diversifying Music Recommendations 19
Takeaways Diversifying music recommendations improves recommendation quality and user engagement. Incorporate recommender score into diversity measure. Submodular approach produces relevant and uniformly diverse mix. Diversifying Music Recommendations 20
Thank you! Questions? houssamn@amazon.com We are hiring! Diversifying Music Recommendations 21
