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
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%