Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks
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This document summarizes a research paper on personalizing session-based recommendations with hierarchical recurrent neural networks (HRNNs). The paper proposes using HRNNs to decouple user and session representations, with a user RNN that evolves the user's latent state across sessions and a session RNN that generates personalized recommendations for each session. Experiments on job posting and online video datasets show the HRNN approach outperforms baselines and other RNN methods, particularly for users with longer histories, by up to 28% in recall and 41% in MRR. The HRNN approach effectively transfers cross-session knowledge to improve session-based recommendations.
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Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks
- 1. Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks Massimo Quadrana (Politecnico di Milano) Alexandros Karatzoglou (Telefonica R&D) Balázs Hidasi (Gravity R&D) Paolo Cremonesi (Politecnico di Milano) 29/08/2017 Como
- 8. • Whole user history as a single sequence • Trivial implementation but limited effectiveness Naïve solution: concatenation User 1 Session 1 Session 2 Session N … R N N R N N R N N R N N R N N R N N R N N R N N R N N R N N
- 9. Hierarchical RNN • Decouple user and session representations • User RNN (GRUusr) • Relays & Evolves the user latent state across sessions • Session RNN (GRUses) • Generates personalized session-based recommendations • Seamlessly personalize Session RNN with cross-session information transfer
- 10. s1 i1,3 c0 i1,4i1,2 i1,3 i1,1 i1,2 s1,0 prediction input item id session-level representation Architecture Session 1 User 1 GRUses (first session only): • Initialization: • Update: 𝑠",$ = 0 𝑠",' = 𝐺𝑅𝑈+,+ (𝑖",', 𝑠",'0") GRUusr: • Initialization: 𝑐$ = 0
- 11. Architecture User 1 Session 1 s1 i1,3 c0 c1 i1,4i1,2 i1,3 i1,1 i1,2 s1,0 user-level representation session-level representation GRUusr: • Update: 𝑐3 = 𝐺𝑅𝑈4+5 𝑠3, 𝑐30" previous user-state last session-state
- 12. Architecture – HRNN Init User 1 Session 1 Session 2 s1 s2 i2,4i1,3 c0 c1 i2,3i2,1 i2,2 i2,5 i2,4 i2,2 i2,3i1,4i1,2 i1,3 i1,1 i1,2 s1,0 session initialization user-level representation GRUses (from the 2nd session on): • Initialization: • Update: 𝑠3,' = 𝐺𝑅𝑈+,+ (𝑖3,', 𝑠3,'0") 𝑠3,$ = tanh ( 𝑊;';< 𝑐30" + 𝑏;';<)
- 13. Architecture – HRNN All User 1 Session 1 Session 2 s1 s2 i2,4i1,3 c0 c1 i2,3i2,1 i2,2 i2,5 i2,4 i2,2 i2,3i1,4i1,2 i1,3 i1,1 i1,2 s1,0 user representation propagation user-level representation session initialization GRUses (from the 2nd session on): • Initialization: • Update: 𝑠3,' = 𝐺𝑅𝑈+,+ (𝑖3,', 𝑠3,'0", 𝑐30") 𝑠3,$ = tanh ( 𝑊;';< 𝑐30" + 𝑏;';<)
- 14. Architecture - Complete User 1 s1 s2 i2,4i1,3 c2c0 c1 user representation propagation i2,3i2,1 i2,2 prediction i2,5 i2,4 i2,2 i2,3 input item id i1,4i1,2 i1,3 user-level representation session-level representation session initialization i1,1 i1,2 s1,0 Session 1 Session 2
- 15. Architecture - Complete User 1 s1 s2 i2,4i1,3 c2c0 c1 user representation propagation i2,3i2,1 i2,2 prediction i2,5 i2,4 i2,2 i2,3 input item id i1,4i1,2 i1,3 user-level representation session-level representation session initialization i1,1 i1,2 s1,0 Session 1 Session 2 Two identical sessions from different users will produce different recommendations
- 16. Training • Based on GRU4Rec [Hidasi et al., 2016] • Gated Recurrent Units • Ranking losses (Cross-entropy, BPR, TOP1) • Output sampling • Dropout regularization • Adagrad w/ Momentum • User-parallel mini-batches 𝑖"," 𝑖",? 𝑖",@ 𝑖",A 𝑖?," 𝑖?,? 𝑖?,@ 𝑖"," 𝑖",? 𝑖"," 𝑖",? 𝑖?," 𝑖?,? Session1 Session2 Session1 Session1 Session2 … 𝑖"," 𝑖",? 𝑖",@ 𝑖"," 𝑖",? 𝑖"," 𝑖?," 𝑖?,? 𝑖",? 𝑖",@ 𝑖",A 𝑖",? 𝑖",@ 𝑖",? 𝑖?,? 𝑖?,@ Input Output Mini-batch1 Mini-batch2 … … … … User1User2User3 𝑖",A𝑖",@ 𝑖",@ 𝑖",? 𝑖",@ Hidasi B., Karatzoglou A., Baltrunas L. and Tikk D..Session-based recommendations with recurrent neural networks. International Conference on Learning Representations, 2016.
- 17. Experiments • Datasets • Job postings (XING) • “Sessionized” RecSys Challenge 2016 dataset (30-min idle threshold) • No repetitions + “deletes” • 11K users, 60k items (min 5 sess/user, 20 events/item) • Training/Test: 78k sessions (488k events) / 11k sessions (58k events) • Online video site (VIDEO) • 13k users, 20k items (min 5 sess/user, 10 events/item) • Training/Test: 120k sessions (745k events) / 13k sessions (78k events)
- 18. Evaluation • Methods: • Personalized Popularity (PPOP) • Co-occurrence Item-kNN • Session-based RNN (RNN) • RNN on concatenated sessions (RNNConcat) • Hierarchical RNNs • HRNN Init (initialization only) • HRNN All (Initialization + propagation in input)
- 19. Evaluation • Procedure • Sequential next-item prediction (Recall/Precision/MRR @5) • RNNs: Avg. 10 iterations with different random seeds • Bootstrapped evaluation (RNNConcat and HRNNs) • Discarded first prediction of each session made by RNNConcat
- 20. Overall results - XING XING Method #Hidden units Recall@5 MRR@5 ItemKNN - 0.0697 0.0406 PPOP - 0.1326 0.0939 RNN 500 0.1317 0.0796 RNNConcat 500 0.1467 0.0878 HRNN All 500+500 0.1482 0.0925 HRNN Init 500+500 0.1473 0.0901 • PPOP strong baseline due to repetitiveness across sessions • Only personalized models work • +11% Recall vs RNN/PPOP (HRNN All) • Comparable MRR to PPOP (HRNN All) • No significant difference between HRNNs
- 21. Overall results - VIDEO • RNN-models outperform all baselines significantly • HRNNInit outperforms all baselines • +7% Recall vs RNN & RNNConcat (HRNN Init) • +19%/+2% MRR vs RNN/RNNConcat (HRNN Init) VIDEO Method #Hidden units Recall@5 MRR@5 ItemKNN - 0.4192 0.2916 PPOP - 0.3887 0.3031 RNN 500 0.5551 0.3886 RNNConcat 500 0.5582 0.4333 HRNN All 500+500 0.5191 0.3877 HRNN Init 500+500 0.5947 0.4433
- 22. Overall results - VIDEO • RNN-models outperform all baselines significantly • HRNNInit outperforms all baselines • +7% Recall vs RNN & RNNConcat (HRNN Init) • +19%/+2% MRR vs RNN/RNNConcat (HRNN Init) • HRNNs differ significantly • Forced propagation degrades performance of HRNN All VIDEO Method #Hidden units Recall@5 MRR@5 ItemKNN - 0.4192 0.2916 PPOP - 0.3887 0.3031 RNN 500 0.5551 0.3886 RNNConcat 500 0.5582 0.4333 HRNN All 500+500 0.5191 0.3877 HRNN Init 500+500 0.5947 0.4433
- 23. In-depth analysis • History length • # Sessions in the user profile • Short: ≤6 sessions • Long: >6 sessions • Position within session • Beginning [1,2] - Middle [3,4] - End [4,Inf) • Only session with length >4 • 6,736 sessions XING • 8,254 sessions VIDEO History length XING VIDEO Short 67% 54% Long 33% 46%
- 24. History length - XING Short Long 0.120 0.125 0.130 0.135 0.140 0.145 0.150 0.155 0.160 Recall 0.1304 0.1355 0.1449 0.1504 0.1464 0.1518 0.1457 0.1505 RNN RNN Concat HRNN All HRNN Init Short Long 0.080 0.085 0.090 0.095 0.100 0.105 0.110 MRR 0.0860 0.0824 0.0932 0.0916 0.0985 0.0957 0.0968 0.0929 RNN RNN Concat HRNN All HRNN Init
- 25. History length - VIDEO Short Long 0.44 0.46 0.48 0.50 0.52 0.54 0.56 0.58 Recall 0.4999 0.5167 0.4770 0.5025 0.4753 0.4763 0.5249 0.5535 RNN RNN Concat HRNN All HRNN Init Short Long 0.30 0.32 0.34 0.36 0.38 0.40 0.42 MRR 0.3388 0.3306 0.3491 0.3658 0.3440 0.3308 0.3820 0.3954 RNN RNN Concat HRNN All HRNN Init
- 26. Analysis within session - XING Beginning Middle End 0.120 0.125 0.130 0.135 0.140 0.145 0.150 Recall RNN RNN Concat HRNN All HRNN Init Beginning Middle End 0.0725 0.0775 0.0825 0.0875 0.0925 MRR RNN RNN Concat HRNN All HRNN Init
- 27. Session evolution - VIDEO Beginning Middle End 0.475 0.525 0.575 0.625 0.675 Recall RNN RNN Concat HRNN All HRNN Init Beginning Middle End 0.30 0.35 0.40 0.45 0.50 0.55 MRR RNN RNN Concat HRNN All HRNN Init
- 28. Experiments with a large dataset • Validate HRNNs effectiveness on large dataset (VIDEOXXL) • 810k users, 380k videos, 8.5M sessions, 33M events • Evaluation on top-50k popular items only • HRNN Init: +28% Recall / +41% MRR over RNN VIDEOXXL Method #Hidden units Recall@5 MRR@5 RNN 100 0.3415 0.2314 RNNConcat 100 0.3459 0.2368 HRNN All 100+100 0.3621 0.2658 HRNN Init 100+100 0.4362 0.3261
- 29. Summary • Cross-session knowledge transfer works! • Naïve concatenation is only partially effective • Both HRNN variants play well when personalization is “easy” • HRNN Init is significantly more effective in complex scenarios
- 30. Future works • Attention models • Multimodal models (user/item features) [Hidasi et al., 2016] • Enhanced GRU4Rec losses [Hidasi & Karatzoglou, 2017] • Other domains (music, e-commerce, etc.) • Code available at https://github.com/mquad/hgru4rec [Hidasi et al., 2016] Parallel Recurrent Neural Network Architectures for Feature-rich Session-based Recommendations. ACM RecSys 2016 [Hidasi & Karatzoglou, 2017] Recurrent Neural Networks with Top-k Gains for Session-based Recommendations. arXiv:1706.03847