Diversifying Music Recommendations

Diversifying Music Recommendations

Diversifying Music Recommendations Houssam Nassif, Kemal Oral Cansizlar, Mitchell Goodman, S.V.N. Vishwanathan [email protected] 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

: similarity measure between two items : Set of items user interacted with Diversifying Music Recommendations 8 Jaccard diversity distance if explanation sets completely separate if explanation sets are identical

Diversifying Music Recommendations 9 Algorithm Swap Recommender score Explanatory set 7 And if

= ( , ) (, ) 1.17 2/3 6.2 1/2 1

1.66 6 1.5 5 1.5 If diversity increases

1 Diversifying Music Recommendations 10 Outline Motivation Jaccard Swap diversity method Submodular diversity method Experiment Diversifying Music Recommendations

11 Diminishing returns Set utility +3 Incremental utility tapers off +5 +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: Maximize sum of all category utilities: Greedy near-optimal solution:

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 doesnt. 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? [email protected] We are hiring! Diversifying Music Recommendations

21

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