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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
Anonym	2
Time
Traditional	session-based	recommendation
Anonym	3
Anonym	1
Soccer
Anonym	1
Anonym	2
Time
Traditional	session-based	recommendation
Cartoons
NBA
Anonym	3
Soccer
User	2
Time
Personalized	session-based	recommendation
Cartoons
NBA
User	1
User	1
Soccer
Time
Personalized	session-based	recommendation
NBA
User	1
Sports!
User	1
Soccer
Time
Personalized	session-based	recommendation
Cupcakes
User	1
Sports!
User	1
Research	question
How	can	we	combine	long-term	(historical)	preferences	of	the	
user	with	her	short-term	(session)	intent	effectively?
• 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
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
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
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
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;';<)
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;';<)
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
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
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.
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)
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)
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
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
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
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
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%
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
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
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
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
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
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
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
Thanks!
Questions?

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