Sequential and Diverse Recommendation with Long Tail

1. Sequential and Diverse Recommendation with Long Tail Aug 15 2019 Yejin Kim, Kwangseob Kim, Chanyoung Park and Hwanjo Yu University of Texas Health Science Center at Houston Kakao Corp. Pohang University of Science and Technology 23 August 2019 1

2. Problem: Diversity in Sequential Recommendation Black Swan Memento Inception Dark Knight Red Road The Pledge Following Quay Alice Black Swan Memento Inception Bob ? ? 23 August 2019 2 We propose a sequential and diverse recommendation model that recommends next general items and also long tail items. Popular General Less popular Long tail Diverse Twisted thrillers Christopher Nolan

3. Compare with other recommendation model G G G G G G G G G G G G G G G T Ordinary Recommendation Sequential Recommendation Diverse Recommendation Sequential and Diverse Recommendation (S-DIV) G General items T Tail items influences T T G G G G G TT T 23 August 2019 3 Reranking heuristic [ICDM11], Learning to rank [WWW17] Item nearest-neighbor, tensor factorization Session-based rec [ICLR16]

4. Method Overview 2 Ranking function: GRU 1 Pseudo ground truth: Ranking sequence of general and tail items 3 Optimization: Listwise loss 23 August 2019 4 0 Clusters of long tail items

5. Clusters of Long Tail Items We cluster tail items and replace each tail item with its representing cluster 23 August 2019 5 Top 20% items 𝜏 = 10 items #occurrences Tail items (texts) 𝑇& = (0.1, 0.2, 0.7) 𝑇- = (0.2, 0.5, 0.3) … 0𝑇- = 𝑇-, 𝑇1, … 0𝑇3 = 𝑇4, … … Weighted sum of word2vec K-means clu1 clu2 clu3 clu4 clu5 clu6 clu7 clu8 # occurrence ↑ Generalizability ↑

6. input labelinput label Pseudo Ground Truth: Ranking of General and Tail Items For learning to rank we derive a pseudo ground-truth ranking sequence 23 August 2019 6 G General items T Tail items 𝐺& → 𝐺3 → 𝐺7 𝑇- 𝑇1,𝑇4 Clustering by contents 0𝑇- = 𝑇-, 𝑇1, … 0𝑇3 = 𝑇4, … 𝐺& → 𝐺3 → 𝐺7 0𝑇- 0𝑇-, 0𝑇3 input Input sequence: 𝐺& → 𝐺3 → 𝐺7 → ⋯ Label sequence: < 𝐺3, 0𝑇- > → < 𝐺7, 0𝑇-, 0𝑇3 > → ⋯

7. Item ID one-hot vector 𝒙 GRU layer Subnet output Item content vector GRU layer Concatenated outputs Fully connected layer 𝒔 Fully connected layer Subnet output Ranking score vector of items 𝐺& <𝐺3, 0𝑇-> 𝐺3 <𝐺7, 0𝑇-, 0𝑇3> 𝐺7 <⋯> … Input Label Ranking function: Recurrent Neural Network (GRU) We define a ranking function f using GRU For specific user u and time 𝑗, 𝒔 = 𝑓 𝒙 𝑓 𝒙 Pseudo ground-truth ranking 𝝎 𝑓𝑓 𝒔 23 August 2019 7 𝝎 general itemranking scores of general & tail items

8. 𝝎 = <𝐺7, 0𝑇-, 0𝑇3> Possible permutations: 𝐺&, 0𝑇-, 𝐺3, 𝐺7, 0𝑇3 𝐺7, 𝐺3, 𝐺&, 0𝑇3, 0𝑇- 𝐺7, 0𝑇-, 0𝑇3, 𝐺3, 𝐺& 𝐺&, 𝐺3, 𝐺7, 0𝑇-, 0𝑇3 … Optimization: Listwise loss We assume the ranking order 𝝎 as one of all possible permutations • Likelihood of ranking function 𝒇 : Given the ranking order 𝝎 and ranking scores 𝒔, the probability that the items will be listed as 𝝎 out of all possible permutations: 𝑝 𝝎 𝒔 = B CD& E exp 𝑠JK ∑MDC E exp 𝑠JN + ∑JP∉𝝎 exp 𝑠JR Top-n Permutation probability (or Plackett-Luce model) • Loss = −log 𝑝 𝝎 𝒔 23 August 2019 8

9. Offline Experiment Setting § Data: 263K blog articles, 2.2M users for 8 days from a commercial platform § Measures § Accuracy: Mean Average Precision@20 § Diversity: # items recommended at least once/total # items § Trade-off: F-score between accuracy and diversity § Baseline: Naïve item nearest neighbor, GRU4rec [ICLR16], GRU4rec + CB [Recsys17], GRU4rec+Reranking § General/Tail thresholds: top 10%, 20%, 50%, and 100% § GRU Hyperparameter tuning: lemb=900, lhid=550 23 August 2019 9

10. Offline Experiment Results At top 50% Tail threshold ★ ★ ★ 23 August 2019 10 Our proposed model increased diversity while maintaining accuracy

11. Offline Experiment Results Rank Recommend items by GRU4rec 1 Top 10 Men’s Watches 2 Top 5 Affordable Mechanical Watches 3 Top 4 Men’s Luxury Watches 17 Controversial World History – Imphal Battle 18 The Camping Trip Rank Recommend items by S-DIV+CB 1 Top 5 Affordable Mechanical Watches 2 Top 10 Men’s Watches 3 Top 4 Men’s Luxury Watches 17 Old Memories - An Everlasting Fragrance 18 World Best 11 Drinks Correct general item Long tail item Our proposed model recommended correct general item as well as a tail item

12. Online A/B Tests Results 23 August 2019 12 • Models • Human curators • GRU4rec, GRU4rec+CB • S-DIV, S-DIV + CB • Measures • Click-through rate (CTR) • Setting • General items: top 20% frequent items • Test • Randomly selected 5% of active users • A/B test lasts for 2 days on mobile and desktop sites CTR Ratio Diversity Human curators 1 0.090% GRU4rec 1.212 0.932% S-DIV 1.118 2.393% Human curators 1 0.082% GRU4rec+CB 1.002 3.452% S-DIV+CB 1.007 6.811%

13. Conclusions 23 August 2019 13 • We first proposed a sequential and diverse recommendation. • We learn temporal preference on general & tail items. • Our model significantly increases diversity while preserving accuracy in online & offline test.

14. Do you have any questions or comments? 23 August 2019 14 Yejin Kim Yejin.Kim@uth.tmc.edu https://yejinjkim.github.io/

15. Optimization: Listwise loss Toy example Suppose we have five general and tail items: 𝐺&, 𝐺3, 𝐺7, 0𝑇-, 0𝑇3, For current general item 𝐺3, let’s say the next ranking order is 𝝎 = <𝐺7, 0𝑇-, 0𝑇3> 𝑒XYZ 𝑒XYZ + 𝑒X[] + 𝑒X[^ + 𝑒XY_ + 𝑒XY^ Prob. of 𝐺7 being ranked 1st Conditional prob. of 0𝑇- being ranked 2nd when 𝐺7 is 1st Conditional prob. of 0𝑇3 being ranked 3rd when 𝐺7 is 1st & 0𝑇- is 2nd ⋅ 𝑒X[] 𝑒X[] + 𝑒X[^ + 𝑒XY_ + 𝑒XY^ ⋅ 𝑒X[^ 𝑒X[^ + 𝑒XY_ + 𝑒XY^ 𝑝 𝝎 𝒔 = 23 August 2019 15

Sequential and Diverse Recommendation with Long Tail

Sequential and Diverse Recommendation with Long Tail

Recommended

More Related Content

Similar to Sequential and Diverse Recommendation with Long Tail

Similar to Sequential and Diverse Recommendation with Long Tail(20)

Recently uploaded

Recently uploaded(20)

Sequential and Diverse Recommendation with Long Tail