BinRec: addressing data sparsity and cold-start challenges in recommender systems with biclustering

Applied In elligence (2025) 55:830
h ps://doi.o g/10.1007/s10489-025-06725-6
1 In oduc ion
Recommende sys ems (RSs) we e in oduced in he 90 s
o suppo use s in a gi en decision making si ua ion [1].
These so wa e ools, om he use ’s poin o iew, educe
he sea ch space when accessing a p oduc ca alog and p o-
cess he opinions o o he use s. Fo many companies dedi-
ca ed o selling p oduc s o any kind, p o iding mul imedia
con en , o simply being a da abase o mo ie ankings, he
RSs acili a e he use ’s selec ion and/o pu chase p ocess
while also ocusing a en ion on a di e en se o less well-
known and success ul p oduc s, inc easing hei chances o
sale. The use o RSs has become essen ial in many applica-
ions, including social ne wo ks [2], online educa ion [3],
and Big Da a ecosys em [4, 5].
Al hough he e a e a ious ypes o RSs, he mos
widely used app oach is Use Based Collabo a i e Fil e ing
(UBCF) [6]. UBCF sys ems p ocess a da abase o use a -
ings (’likes,"s a a ings,’ o nume ical da a) on any kind o
i em (selling p oduc s, books, mo ies, e c.) o gene a e i em
F ancisco Gómez-Vela, Au elio Lopez-Fe nandez, Miguel Ga cía-
To es and Fede ico Di ina con ibu ed equally o his wo k.
Domingo Rod íguez-Baena
[email p o ec ed]
F ancisco Gómez-Vela
[email p o ec ed]
Au elio Lopez-Fe nandez
[email p o ec ed]
Miguel Ga cía-To es
[email p o ec ed]
Fede ico Di ina
[email p o ec ed]
1 Compu e Science, Uni e sidad Pablo de Ola ide, C a.
U e a km 1, Se ille ES-41013, Se illa, Spain
2 Depa men o Compu e Languages and Sys ems,
Uni e sidad de Se illa, Se ille ES-41004, Se illa, Spain
3 Da a Science and Big Da a Lab, Uni e sidad Pablo de
Ola ide, C a. U e a km 1, Se illa ES-41013, Se illa, Spain
Abs ac
Recommende Sys ems help use s in making decision in di e en ields such as pu chases o wha mo ies o wa ch. Use -
Based Collabo a i e Fil e ing (UBCF) app oach is one o he mos commonly used echniques o de eloping hese so -
wa e ools. I is based on he idea ha use s who ha e p e iously sha ed simila as es will almos ce ainly sha e simila
as es in he u u e. As a esul , de e mining he nea es use s o he one o whom ecommenda ions a e sough (ac i e
use ) is c i ical. Howe e , he massi e g ow h o online comme cial da a has made his ask especially di icul . As a esul ,
Biclus e ing echniques ha e been used in ecen yea s o pe o m a local sea ch o he nea es use s in subg oups o
use s wi h simila a ing beha iou unde a subg oup o i ems (biclus e s), a he han sea ching he en i e a ing da abase.
Ne e heless, due o he la ge size o hese da abases, he numbe o biclus e s gene a ed can be ex emely high, making
hei p ocessing e y complex. In his pape we p opose BinRec, a no el UBCF app oach based on Biclus e ing. BinRec
simpli ies he sea ch o neighbou ing use s by de e mining which ones a e nea es o he ac i e use based on he numbe
o biclus e s sha ed by he use s. Expe imen al esul s show ha BinRec ou pe o ms o he s a e-o - he-a ecommende
sys ems, wi h a ema kable imp o emen in en i onmen s wi h high da a spa si y. The lexibili y and scalabili y o he
me hod posi ion i as an e icien al e na i e o common collabo a i e il e ing p oblems such as spa si y o cold-s a .
Keywo ds Recomende sys ems · Biclus e ing · Collabo a i e il e ing · Da a mining
Accep ed: 13 June 2025
© The Au ho (s) 2025
BinRec: add essing da a spa si y and cold-s a challenges in
ecommende sys ems wi h biclus e ing
DomingoRod íguez-Baena1· F anciscoGómez-Vela1· Au elioLopez-Fe nandez2· MiguelGa cía-To es3·
Fede icoDi ina3
1 3
D. Rod íguez-Baena e al.
ecommenda ions o a speci ic use , answe ing he ques-
ion: Wha is he mos popula among people who sha e my
as es? As a esul , hey a e based on he idea ha use s who
ha e p e iously sha ed simila as es will almos ce ainly
sha e simila as es in he u u e. To do so, a subse o use s
(nea es use s) who a e simila o he use o whom ecom-
menda ions a e sough (ac i e use ) is chosen, and hei a -
ings a e combined o p edic unseen i ems.
Howe e , UBCF echniques include impo an issues ha
a e s ill a challenge o esea che s, such as spa si y, pe o -
mance, and he cold-s a p oblem [7, 8]. When a ing da a-
bases a e la ge and include a signi ican numbe o i ems,
he e a e ew a ings a ailable o each use . This makes i
ha de when looking o nea es use s because he e is less
in o ma ion a ailable o look o simila beha iou in e ms
o a ing (spa si y p oblem). Fu he mo e, in hese cases,
UBCF app oaches pe o m poo ly in e ms o scalabili y
[9, 10]. Mo eo e , adding a new use o he a ings da a-
base may be p oblema ic because his use has an emp y o
e y small a ing p o ile, and as a esul , he sys em canno
accu a ely calcula e he simila i ies be ween such cold-s a
use s and o he s [11]. Thus, since he i s UBCF app oach
was p oposed by Resnick e al. [12], many pape s ha e been
published in an a emp o imp o e p edic ion esul s and
sol e he majo issues associa ed wi h hese echniques [13].
One o he mos impo an asks o UBCF p ocess-
ing is de e mining he nea es use (s) o he ac i e one by
calcula ing simila i y measu es be ween hem. Gi en he
huge amoun s o da a a ailable, his sea ch could be com-
plex and ime-consuming [14]. To deal wi h he di e si y
o use p o iles, educe dimensionali y and making easie
o ind he nea es use s, Clus e ing echniques ha e been
used ei he di ec ly o as a p ep ocessing s age in UBCF
sys ems [15]. These echniques gene a e disjoin g oups
o use s by aking in o accoun he a ings o all he i ems.
Howe e , in eal-wo ld scena ios, he e is usually a s ong
co ela ion be ween he p e e ences o subse s o use s on
subse s o i ems [16]. Thus, du ing he las yea s, UBCFs
based on Biclus e ing ha e been p oposed o educe he
consequences o spa si y issues in he inpu da ase [17].
Biclus e ing echniques g oup subse s o elemen s ha ha e
simila i ies unde a subse o a ibu es and ha e been used
success ully in many a eas o s udy [18]. So, ins ead o pe -
o ming a global sea ch o ind he nea es use s o he ac i e
one, Biclus e ing can be used o na ow he sea ch space.
The a ing da abase biclus e s, which a e subse s o use s
wi h simila a ing beha iou unde a subse o i ems, ep-
esen dense a eas whe e he nea es use s can be ound in
a local sea ch.
The gene a ion o biclus e s akes place in a phase p io
o he ecommenda ion, called he o line phase. Following
ha , and on demand, he nea es use s o he ac i e use a e
sea ched in he biclus e s o gene a e a speci ic ecommen-
da ion (online phase) [19]. Howe e , due o he la ge num-
be o biclus e s ha can be ex ac ed om he inpu a ing
da ase s, p ocessing hem o de e mine he simila i y mea-
su es when looking o he nea es use s can be challenging.
Fu he mo e, he e a e se e al impo an aspec s associa ed
wi h he use o Biclus e ing. Fo example, which Biclus-
e ing echnique applies, i he same se o biclus e s can
be used o e e y ac i e use , how o upda e he biclus e s
when new da a is added o he a ing da ase o how o a o d
he cold-s a p oblem.
This wo k p esen s BinRec, a no el UBCF app oach
ha uses biclus e s o make he sea ch o he nea es use s
mo e e icien in spa se da abases, while also op imizing he
compu a ional complexi y o such a sea ch h ough a simple
da a s uc u e. This s uc u e can also be used o add ess he
cold-s a p oblem, which is common in a ing da abases.
BinRec is lexible, adap ing o any Biclus e ing echnique.
Howe e , he use o he BiBi Biclus e ing algo i hm [20]
is p oposed, as i allows he inc emen al gene a ion o new
biclus e s as new use s a e added o he a ing da abase.
This ea u e, combined wi h he a o emen ioned e iciency,
makes BinRec well sui ed o managing la ge-scale da a-
bases wi h a high numbe o biclus e s.
The main con ibu ions o his wo k can be summa ized
as ollows:
●In oduc ion o BinRec, a no el collabo a i e il e -
ing app oach ha le e ages Biclus e ing echniques o
g oup use s wi h simila a ing p e e ences, he eby
educing he sea ch space o nea es neighbou s in
spa se a ing da abases.
●BinRec educes he compu a ion ime equi ed o
iden i y he nea es use s o a gi en one based on biclus-
e s, using a simple da a s uc u e ha s o es he numbe
o biclus e s sha ed be ween use s, while also achie ing
highly accu a e p edic ions o use ecommenda ions.
●The cold s a p oblem can also be add essed using he
same in o ma ion s o ed in he p e iously men ioned
da a s uc u e.
●BinRec adap s o any Biclus e ing echnique. How-
e e , he use o BiBi Biclus e ing algo i hm is p oposed
due o i s pe o mance and he possibili y o de eloping
an inc emen al biclus e s p ocessing when new use s a e
added o he a ing da abase.
All hese con ibu ions make BinRec an e icien and accu-
a e app oach ha can be applied o la ge a ing da abases
and a high numbe o biclus e s gene a ed om hem,
while also add essing he issues o spa si y and cold-s a .
To demons a e his, he s udy includes expe imen a ion
and a compa ison wi h o he benchma k collabo a i e
1 3
830 Page 2 o 17
BinRec: add essing da a spa si y and cold-s a challenges in ecommende sys ems wi h biclus e ing
il e ing app oaches and a biclus e -based ecommenda ion
echnique.
The es o he pape is o ganized as ollows: Sec ion 2
e iews he s a e o he a in FC-based ecommende sys-
ems and Biclus e ing. In Sec ion 3, he BinRec app oach is
desc ibed in de ail, ollowed by a se ies o expe imen s and
analysis o esul s in Sec ion 4. Finally, Sec ion 5 p esen s
conclusions and possible u u e lines o esea ch.
2 Backg ound
As i has been commen ed in he p e ious Sec ion, se e al
UBCF echniques based on Biclus e ing ha e been p o-
posed in he las yea s [21]. Following, hey a e analyzed
om di e en poin s o iew. In i s place, he gene a ion o
he biclus e s: whe he he Biclus e ing echnique is applied
o he en i e da ase o only a po ion o i , which Biclus e -
ing echnique is used, and so on. Second, how he gene a ed
biclus e s a e p ocessed o ind he nea es use s. Finally,
how he cold-s a p oblem is handled is an impo an ac o
o conside .
In e ms o biclus e gene a ion, mos app oaches apply
Biclus e ing echniques o he en i e a ing da ase , ega d-
less o who is he ac i e use [17, 22–33]. In hese cases,
all o he gene a ed biclus e s a e used o p edic any ac i e
use ’s ecommenda ions. Howe e , i a new use and hei
a ings a e added o he inpu da ase , he Biclus e ing
echnique mus be applied again o include his new use .
O he esea ch wo ks only gene a e biclus e s ha include
he ac i e use . The gene a ion o he biclus e s is hus less
compu a ionally expensi e, bu i mus be epea ed o each
ac i e use . Fo example, in he wo ks [34, 35], a hie a -
chical Biclus e ing me hod is applied o gene a e biclus e s
in di e en laye s consis ing o co- a ed i ems among he
ac i e one and o he use s. In he i s laye , a biclus e is
c ea ed o e e y i em a ed by he ac i e use , including
also he es o use s ha ha e a ed he same i em. In he
second laye , pai s o biclus e s om he i s laye a e com-
bined, so he subse o i ems inc eases while he subse o
use s dec eases in he new biclus e s. The p ocess ends in a
laye in which he biclus e s con ains he maximum numbe
o i ems wi h he minimum numbe o use s.
Some esea ch wo ks a e e y lexible when i comes o
selec ing which Biclus e ing echnique o use [23, 30–33,
36], allowing you o use wha e e echnique you wan .
Ob iously, bo h he inal esul s and he pe o mance may
a y depending on he op ion chosen. In he six p e iously
ci ed wo ks, he Biclus e ing algo i hms selec ed o conduc
he expe imen a ion a e Bimax [37], QUBIC [38] o XMo i
[39]. In many o he cases, speci ic Biclus e ing algo i hms
ha e been designed o g oup oge he subse s o use s and
subse s o i ems. Fo example, BIC-aiNET, p oposed in
[22] and used again by Desai e al. [26], is an adap a ion o
he A i icial Immune Ne wo k model applied o Clus e -
ing echniques [40]. In he wo k o Elnaba awy e al. [27],
he Biclus e ing me hod BARTMAP is used [41], al hough
he au ho s don‘ cla i y i i is possible o use any o he
echnique.
A e ob aining he biclus e s, he ollowing s ep is o
look o he a ge biclus e s whe e he nea es use s will be
chosen. In gene al, o selec he a ge biclus e o biclus-
e s i is necessa y o calcula e simila i y measu es be ween
all he biclus e s and he ac i e use . This can be a cos ly
ask conside ing he la ge numbe o biclus e s ha can be
gene a ed. Some wo ks simpli y his ask, jus p ocessing
only he biclus e s ha con ain he ac i e use . Fo example,
in [22] a esidue is calcula ed only o hose biclus e s ha
include his use . The biclus e wi h he smalles esidue and
he i em o be a ed is chosen. Then, he ecommenda ion is
gene a ed di ec ly as an a e age o he a ings o his i em in
he biclus e . The biclus e s o which he ac i e use belongs
a e de e mined in i s place in he wo k o Yolda e .al. [33].
Then, he op-N ecommenda ions a e hen chosen based on
he join decision o hese biclus e s. Ano he in e es ing
example could be he wo k p esen ed by Huan-huan e al.
[42] whe e a scalable ecommende sys em based on biclus-
e ing and mo h lame op imiza ion algo i hm is p oposed.
Ano he ecen s udy, [17] p oposes a no el Biclus e ing
me hod based on modi ied uzzy adap i e esonance heo y.
This pape p oposes a new measu e o e lec he simila -
i y be ween use s ha conside s he e ec o he numbe o
common elemen s o he use s.
In mos cases, howe e , a simila i y calcula ion p ocess
is applied o all biclus e s. In some occasions, his p ocess
is applied o sea ch o he nea es use s bu also o he
nea es i ems o he one o be a ed. In [24], se e al nea es
i ems o he i em o be a ed a e selec ed om he biclus e s,
calcula ing a simila i y measu e in wo phases. The same
p ocess is ca ied ou o he nea es use s o he ac i e one.
Then, he p edic ion is ob ained by means o a o mula ha
combines he p e iously calcula ed simila i ies. The nea -
es K biclus e s o he ac i e use and he a ge i em a e
ob ained in [30], compu ing he CjacMD (Cosine- Jacca d-
Mean Measu e o Di e gence) simila i y measu e. In he
wo k o Symeonidis e al. [23] and Sing e al. [32], a simi-
la i y based on he numbe o common i ems is calcula ed
o ind he K nea es biclus e s o he ac i e use . Then, a
anking o he N op i ems is ex ac ed om hese biclus e s,
based on he appea ance equency o each i em. In o he
wo ks, he smalles biclus e o he ac i e use , ha is, he
biclus e ha con ains his use and he g ea es numbe o
i ems, is selec ed [25, 28]. To accomplish his, all biclus e s
mus be p ocessed ia in e sec ion. In a second phase, he
1 3
Page 3 o 17 830
D. Rod íguez-Baena e al.
The nea es use s a e calcula ed om wo di e en sou ces
o in o ma ion in he wo k o Fa yad e al. [52]. In a i s
s ep, a Cosine unc ion is used o calcula e he simila i y
be ween use s based on he use -i em a ings da ase . Sec-
ondly, demog aphic simila i ies be ween use s a e ob ained
h ough a weigh ed a e age o hei demog aphic da a. The
inal simila i ies be ween use s a e de e mined using a linea
combina ion o bo h a ings and demog aphic simila i ies.
Howe e , in many cases, he p oblem o spa si y a ec s no
only he da a desc ibing use beha iou ( a ings), bu also
he da a desc ibing he use s (e.g. pe sonal in o ma ion).
To add ess his issue, a no el Induc i e He e ogeneous
G aph Neu al Ne wo k (IHGNN) model is p esen ed [53].
This model con e s new use s, i ems, and associa ed mul-
imodal in o ma ion in o a Modali y-awa e He e ogeneous
G aph (M-HG), which p ese es he ich and he e ogeneous
ela ionship in o ma ion among hem.
The app oach p esen ed in his pape , BinRec, educes
he complexi y o p ocessing biclus e s o ind he nea es
use s o he ac i e one, imp o ing pe o mance on la ge
da abases wi h high spa si y while main aining a high le el
o accu acy in i s ecommenda ions. To manage he cold-
s a issue, i is no necessa y o p ocess addi ional in o ma-
ion abou use s, bu a he he in o ma ion on a ings and
ela ionships be ween use s ob ained h ough biclus e s is
used. Also, BinRec is lexible o using any Biclus e ing ech-
nique, hough a speci ic one is p oposed o pa ially sol e
he p oblem o gene a ing he en i e se o biclus e s again
when new da a a e added o he inpu da ase . Finally, p io
o applying he Biclus e ing echnique, a p ep ocessing ask
is used o ine- une he p ecision o he ecommenda ions.
3 Ma e ials and me hods
In his sec ion, he new app oach p oposed by au ho s,
BinRec, is ully explained. The s a ing hypo heses a e he
ollowing:
●In a biclus e , a sub-g oup o use s sha e a simila opin-
ion abou a sub-g oup o i ems, so i is a e y in e es ing
sou ce o knowledge o gene a e new ecommenda ions.
●The use s wi h whom he same as es a e sha ed a e hose
wi h whom a g ea e numbe o biclus e s a e sha ed.
The p oposal p esen ed in his wo k is di ided in o wo
phases: o line and online. The o line phase comes be o e
he ecommenda ion and consis s mainly o p ocessing he
biclus e s ob ained om he a ing da abase. Du ing he
online phase, he ecommenda ion is ca ied ou . Fu he -
mo e, he o line and online phases a e sepa a ed in o wo
candida e se o i ems o a ecommenda ion is ex ac ed
om he biclus e neighbou hood o he smalles biclus-
e . The Mean Absolu e Di e ence measu e is calcula ed
o all he biclus e s in [29, 43] o ind he nea es biclus e
ha has a s ong pa ial simila i y wi h he p e e ences o
an ac i e use . Recen wo ks also apply speci ic algo i hms
o compu e he neighbou hood o he ac i e use , such as
Meme ic algo i hms in [34] o he Mo h Flame Op imiza-
ion algo i hm in [35]. In [44], he biclus e s a e p ocessed
and mapped in a squa e g id o ep esen di e en s a es in
a Ma ko decision p oblem. To do so, biclus e s a e so ed
by hei Scaling Mean Squa ed Residue (SMSR) alues and
hen me ged o i and place hem in he squa e ma ix. In
conclusion, as obse ed in he e iewed wo ks, educing he
sea ch space h ough he gene a ion o biclus e s in ol es
a massi e p ocessing e o o ind he nea es neighbou s.
This ask can be compu a ionally demanding, conside ing
he la ge numbe o biclus e s ha can be ob ained om
inc easingly ex ensi e a ing da abases.
Recen ad ancemen s in ecommende sys ems ex end
beyond biclus e ing, pa icula ly u ilising g aph-based
me hodologies o enhance pe o mance in spa se o in ica e
use -i em con ex s. These app oaches ep esen in e ac ions
as bipa i e o he e ogeneous g aphs. Fo ins ance, Li eGSR
[45] e ines social g aphs using PageRank-based cen al-
i y, educing agg ega ion o e head in GNN-based ecom-
menda ions. KGCFRec [46] in eg a es knowledge g aphs
wi h collabo a i e il e ing h ough a dual-channel GNN
and adap i e a en ion, ou pe o ming baselines on mul iple
da ase s. Simila ly, he G aph Con olu ional Recommen-
da ion Sys em wi h Bila e al A en ion [47] enhances pe -
o mance by combining use -i em and knowledge g aphs
ia a en ion mechanisms. Beyond g aph-based me hods,
Tu bo-CF [48] o e s as ecommenda ion h ough ma ix
decomposi ion- ee il e ing, while PolyCF [49] uses spec-
al g aph il e s o imp o e collabo a i e il e ing accu acy
and scalabili y.
Finally, he use s cold-s a p oblem appea s when he
exis ing a ing da a abou a use a e insu icien , so he sys-
em canno gene a e e icien ecommenda ions. I happens
whene e he e a e use s who ha e no a ed any i em (new
use s) o who ha e a ed a e y ew i ems (cold use s) [50].
To add ess his issue, mos o he p oposed solu ions ind
di e en ways o associa e use s no using a ing da a bu
o he ypes o in o ma ion. Fo example, in [26], he inpu
da ase is clus e ed as a p ep ocessing s ep based on use s’
age and loca ion, no on hei a ings. O he wo ks, such as
[51], use bo h explici ( a ings) and implici da a (b ows-
ing his o y, pu chase and click ac i i y, e c.) abou use s o
gene a e ecommenda ions. To do so, au ho s combine a
P obabilis ic Ma ix Fac o iza ion based on a ing da a and
a Bayesian Pe sonalized Ranking based on explici da a.
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830 Page 4 o 17
BinRec: add essing da a spa si y and cold-s a challenges in ecommende sys ems wi h biclus e ing
encoding, i use u ecommends an i em i, hen (u, i) is equal
o 1; o he wise, i is equal o 0.
A de ailed illus a ion o he o line phase is p o ided in
Fig. 2 by means o an example. As i can be obse ed, he
o line phase is di ided in o h ee s eps: he bina iza ion o
he a ing ma ix, he gene a ion o he biclus e s and he
c ea ion o he Biclus e s ma ix.
The inpu da a o he example, A, is a 4x10 ma ix in
which 4 use s a e 10 i ems using sco es om 1 o 5. The
A(u, i)=−1 alue ep esen s ha i em i has no been a ed ye
by use u. As i has been said be o e, he bina iza ion o he
a ing ma ix is he i s s ep. This s ep is necessa y in o de
o apply he BiBi biclus e ing algo i hm, which ope a es
on bina y da abases. The bina iza ion p ocess—i.e., ans-
o ming he da a in o ones and ze os— equi es a h eshold
ha de e mines whe he an elemen will be con e ed o 1
o 0. Ra ings da abases a e usually based on a lis o dis-
c e e alues, o example, a ings om 1 o 5 s a s. Thus,
he h eshold will be a speci ic alue wi hin ha lis . Fo
ins ance, i he h eshold is se o 3, i means ha all a ings
equal o o g ea e han 3 will be con e ed o he alue 1,
and he emaining alues o 0. The esul ing da abase high-
ligh s hose a ings equal o o g ea e han 3, which will
be conside ed posi i e ecommenda ions. So, i he possible
sco e alues o he a ings ange om x o y, hen o each
alue
S ∈[x, y]
, a new bina y ma ix
B_S
can be gen-
e a ed, in which
B_S (u, i)=1
i in he o iginal da ase
A(u, i)≥S
and i means ha i em i is ecommended by
use u;
B_S (u, i)=0
o he wise. In he example o Fig.
2,
S =2
, so a new bina y ma ix is c ea ed (
B_2
). As i
will be explained in Sec ion 4, his p ep ocessing o e s he
possibili y o in luencing he sensi i i y o he ecommende
sys em.
s ages (see Fig. 1). Following, e e y phase, along wi h i s
s ages, is desc ibed.
3.1 O line phase
The goal o he o line phase is o use he local beha iou
pa e ns, ega ding use ecommenda ions, ex ac ed by he
biclus e s o gene a e a ma ix, M, ha e lec s he ela ion-
ships be ween di e en use s based on he simila i y o hei
a ings. This ma ix will con ain, o each use , he numbe
o biclus e s sha ed wi h o he s and will be used o quickly
and easily iden i y he nea es use s o he ac i e one. When
wo use s appea in he same biclus e , i means ha hey
ha e ecommended he same subse o i ems. The e o e,
i hey sha e many biclus e s, i indica es a highe le el o
a ini y. As we ha e seen in he s a e-o - he-a e iew, he
sea ch o nea es neighbou s in ol es applying complex
measu es o all he biclus e s gene a ed o each ecom-
menda ion, esul ing in a high compu a ional load. Wi h his
new p oposal, a single da a s uc u e will be gene a ed, he
ma ix M, which, wi hou complex calcula ions, will con-
ain he necessa y in o ma ion o de e mine he simila i y
be ween use s o all he new ecommenda ions.
As i can be obse ed in Fig. 1, Biclus e ing is applied
o he a ings da ase in he o line phase o gene a e biclus-
e s, ha is, subse s o use s wi h simila a ing beha iou
unde subse s o i ems. Al hough any Biclus e ing echnique
can be used in ou p oposal, in his wo k, bina y Biclus e -
ing app oaches ha e been p oposed. This kind o echnique
adap s pe ec ly o he classi ica ion o ecommenda ions
based on labels: no ecommended and ecommended. In
addi ion, as will be seen below, he inpu da a can be p e-
p ocessed in a ious ways due o hei bina iza ion. So,
when wo king wi h use s and i ems o be a ed in a bina y
Fig. 1 This is he gene al schema o he p oposal. I is di ided in o wo
di e en phases: o line and online. Biclus e ing is applied o he a ing
da ase in he o line phase o gene a e a se o biclus e s. A biclus e
is de ined as a subg oup o use s wi h simila a ing beha iou unde a
subg oup o i ems. Nex , hese biclus e s a e used o de ine he biclus-
e s ma ix, M, ha will be used in he online phase du ing he selec ion
o he nea es use s. In he online phase, he nea es use selec ion and
ecommenda ion p ocess ake place
1 3
Page 5 o 17 830

D. Rod íguez-Baena e al.
o bi s gene a ed as a esul o he AND ope a ion be ween
hese ows. The po en ial biclus e is hen de i ed om his
pai o ows and he columns in he pa e n ha a e equal
o 1. Finally, new ows a e added o he biclus e i hey
a e compa ible wi h ha pa e n. So, i a new use is added
o he a ing da ase , o example U5 (see Fig. 3), i will
no be necessa y o apply BiBi o he whole new bina y
ma ix. Ins ead, Bibi will be pa ially applied, jus c ea -
ing new pa e ns wi h he ow U5 and he es o ows (new
biclus e Bic6) and checking i ha ow is compa ible wi h
The p ocedu e o gene a ing biclus e s is he second
s ep. In he example, i e biclus e s ha e been ex ac ed
om he bina y ma ix. The Bibi Biclus e ing algo i hm
was used o accomplish his [20]. Se e al easons a e gi en:
Fi s and o emos , Bibi pe o ms g ea wi h bina y ma i-
ces o all sizes and shapes [54]. Besides, when a new use
is in oduced o he a ings da ase , Bibi o e s he op ion
o de eloping an inc emen al biclus e s p ocessing a he
han ha ing o p ocess he en i e inpu da ase again. BiBi
gene a es a pa e n o each pai o ows, which is a g oup
Fig. 3 In he example, a new use U5 has been added o he inpu a -
ing da ase A (enhanced in ed). The bina y Biclus e ing algo i hm
BiBi is applied only his new ow. Thus, Bibi gene a es a new pa -
e n wi h ha ow and e e y one o he p e ious ows and om hese
pa e ns a new biclus e is c ea ed (Biclus e Bic6). A he same ime,
Bibi checks i he new ow is compa ible wi h he pa e ns ob ained
in p e ious execu ions, modi ying he exis ing biclus e s i necessa y
(changes in ed in biclus e s Bic1, Bic2 and Bic5)
Fig. 2 In he i s s ep o he o line phase, he inpu a ings da ase , A,
is bina ized using a h eshold alue (
S =2
in he example). Then,
a new bina y e sion o A is c ea ed,
B2
. In he second s ep, a bina y
Biclus e ing algo i hm (BiBi ) is applied o
B2
, ex ac ing a g oup o
biclus e s. Fo example, he Biclus e Bic1 is composed by wo use s,
U1 and U2, bo h ecommending he i ems I4 and I10. Finally, in he
hi d s ep, a use s squa e ma ix, M, is c ea ed om he biclus e s
gene a ed
1 3
830 Page 6 o 17
BinRec: add essing da a spa si y and cold-s a challenges in ecommende sys ems wi h biclus e ing
sha es a conc e e numbe o biclus e s wi h UJ, ep esen ed
by he weigh o ha edge. In ou example, U2 is connec ed
wi h he es o use s and he sum o i s edges ep esen s he
highes alue, 5. The e o e, U2 will be chosen o ep esen
he a ing da ase ’s o e all opinion and his/he ecommen-
da ions will be assign o he new use s.
3.2 Online phase
The online phase is di ided in o wo s eps. The ac i e use ’s
nea es use s a e de e mined in he i s s ep. The ecom-
menda ion p ocess is hen ca ied ou based on he nea es
use s. Following, he examples o Figs. 5 and 6 a e used o
clea ly explain he online phase.
BinRec, unlike mos Biclus e ing-based ecommenda-
ion p oposals (see Sec ion 2), employs a simple measu e
based on he M ma ix a he han calcula ing complex simi-
la i y measu es ha en ail p ocessing all o he biclus e ele-
men s. Ins ead, i is assumed ha a use who sha es a la ge
numbe o biclus e s wi h ano he use can de e mine ha
his o he opinion is mo e impo an han o he use s’ opin-
ions. Le ’s suppose ha he ac i e use is U2. To ind i s
nea es use s, he column
#
o M, which con ains he a e -
age o he numbe o biclus e s ha a speci ic use sha es
wi h he es , is used (see Fig. 5). To calcula e ha a e -
age, only hose use s wi h whom e e y use sha es biclu -
e s a e aken in o accoun . So, he nea es use s o U2 a e
hose wi h whom U2 sha es a numbe o biclus e s g ea e
o equal han ha a e age (1.6), ha is, U3 (he/she sha es 2
biclus e s wi h U2) and U4 (he/she sha es 2 biclus e s wi h
U2). As i can be obse ed, he use U1 is disca ded because
he/she only sha es 1 biclus e wi h U2. Nex , he po en ially
ecommendable i ems a e selec ed, ha is, hose ha he
ac i e use had no a ed up o ha momen : I1, I2, I6 and
I8. To know which o hese i ems will be ecommended,
he pa e ns c ea ed in p e ious execu ions (modi ica ions in
Bic1, Bic2 and Bic5).
Nex , in he hi d s ep, a Biclus e s ma ix M is c ea ed. M
is a squa e ma ix and i s dimension is he numbe o use s
in he da abase. The alue o M(I, J) e lec s he numbe
o biclus e s sha ed by use s UI and UJ, whe eas he main
diagonal ep esen s he numbe o biclus e s in which e e y
use UX occu s (see Fig. 2). This ma ix ul ils wo impo -
an unc ionali ies (see Sec ion 3.2): M will be used in he
online phase du ing he nea es use selec ion p ocess and M
also be used o de e mine he use who appea s in he la g-
es numbe o biclus e s. This use will be used in he online
phase o add ess he cold-s a issue [55]. The hypo hesis is
ha he use who appea s in he highes numbe o biclus-
e s is he one ha b ings oge he he g ea es numbe o
simila as es wi h he es o he use s. In Fig. 4, a g aph
gene a ed om M is shown. In his g aph, e e y node is
a use and e e y edge, om UI o UJ, de e mines ha UI
Fig. 5 The i s s age o online phase is ep esen ed in his
image, in which he nea es use s o he ac i e use , U2,
a e de e mined using he Biclus e s Ma ix M. M s o es,
o e e y use , he numbe o biclus e s sha ed wi h he
es o use . The las column,
#
, s o es he a e age o he
numbe o biclus e s sha ed o e e y use . In he case o
U2, he use sha es biclus e s wi h U1 (1 biclus e ), U3 (2
biclus e s) and U4 (2 biclus e s). Since he nea es use s
a e hose wi h whom a use sha es a numbe o biclus e s
equal o o g ea e han he a e age (1.6 in he case o U2),
he U3 and U4 use s a e selec ed. In he inpu da ase A, he
po en ially ecommendable i ems om U2 a e highligh ed
in ed
Fig. 4 This igu e shows a g aph gene a ed om ma ix M. E e y node
is a use and e e y edge, om UI o UJ, de e mines ha UI sha es a
conc e e numbe o biclus e s wi h UJ, ep esen ed by he weigh o
ha edge. U2, as i can be obse ed, is he use which appea s in he
la ges numbe o biclus e s
1 3
Page 7 o 17 830
D. Rod íguez-Baena e al.
in common wi h a ce ain use , he mo e aluable hei opin-
ion will be in gene a ing ecommenda ions. Fu he mo e,
i he Biclus e ing echnique is in eg a ed in o he BinRec
amewo k, his da a s uc u e can be gene a ed in pa allel
wi h he biclus e s. Besides ha , he ecommenda ion p o-
cess o a speci ic use can be ca ied ou independen ly,
esul ing in signi ican scalabili y. As a esul , because he
sea ch o nea es use s is execu ed du ing he online phase
(on demand), he pu pose o his p oposal is o educe p o-
cessing complexi y. Finally, he cold-s a p oblem is sol ed
by aking ad an age o he in o ma ion s o ed in he biclus-
e s ma ix M.
In he ollowing sec ion, he way BinRec beha es unde
di e en condi ions is analysed. Besides, a compa ison
be ween he new app oach and exis ing UBCF me hods is
ca ied ou .
3.3 Expe imen a ion
Following, he me hodology used in he expe imen a ion,
along wi h he desc ip ion o he da ase s and measu es
used, a e in oduced.
3.3.1 Expe imen s wo k low
E e y expe imen ollows he gene al schema shown in Fig.
7 and is di ided in o 4 di e en phases. The inpu consis s
o a a ing da ase A and a h eshold alue o bina iza ion,
S , chosen om he da ase ’s ange o possible sco es, wi h
ex eme alues disca ded. In ou case, because all o he
da ase s used in he expe imen a ion ha e sco es anging
om 1 o 5, S can be 2, 3, o 4 (1 and 5 a e disca ded). Fol-
lowing he selec ion o S , he aining and es da ase s a e
gene a ed. To do so, a subse o he inpu da ase is chosen
he second s ep is ca ied ou (see Fig. 6). I is an i e a i e
p ocess in which a new a ing alue, Final Rec, is gene a ed
o each i em selec ed in he p e ious s ep by calcula ing he
a e age o he a ing alues om he nea es use s. The a -
ing alues equal o
−1
a e igno ed. Finally, o decide i an
i em is ecommended o no , he new a ing alue mus be
equal o g ea e o he sco e alue used in he o line phase
o ans o m he inpu ma ix A in o a bina y ma ix,
B_S
(
S =2
in ou example).
The new a ing alues o ac i e use U2 a e gene a ed
in he example o Fig. 6. In he case o i em I1, he a e -
age o he opinions o he nea es use s,
A(U3,I1) = 5
and
A(U4,I1) = 1
, is used o gene a e a new a ing alue:
Final Rec =
(5 + 1)/2=3>=S
. As his new alue is
g ea e han
S =2
, he i em I1 is conside ed as ecom-
mended. On he con a y, in he case o I2, he use U3
has no a ed ye his i em,
A(U3,I2) = −1
, so i s opin-
ion is no aken in o accoun . Then, he new a ing alue
is Final Rec =
A(U4,I2)/1=1/1=1< S
, so I2 is no
ecommended.
Finally, he cold-s a issue is add essed. When a new use
is added o a ecommenda ion sys em, commonly i has no
i ems a ed ye . This is called a cold-s a p oblem [51] and
i implies ha his new use will no be pa o any biclus e .
In hese cases, he solu ion p oposed by his wo k is o use
he ecommenda ions om he mos connec ed use , ha is,
he use ha appea s in he la ges numbe o biclus e s (see
Sec ion 2). In ou example, U2 is he mos connec ed use ,
so i is selec ed as ep esen a i e o he opinions o he es
o use s.
As a conclusion, he use o he biclus e ma ix, M,
implies a oiding he calcula ion o a complex simila i y
measu e be ween he ac i e use and all he biclus e s gene -
a ed. The idea behind i is ha he mo e biclus e s you ha e
Fig. 6 The second s ep o he online phase is ep esen ed in
his image. Fo e e y i em no a ed ye by he ac i e use
U2, a new a ing alue, Final Rec, is gene a ed by he a e -
age o he a ing alues o he nea es use s (U3 and U4).
The i ems no a ed a e igno ed. I he new a ing alue is
g ea e o equal o S , he i em is ecommended
1 3
830 Page 8 o 17
BinRec: add essing da a spa si y and cold-s a challenges in ecommende sys ems wi h biclus e ing
6040 use s abou 39523 mo ies (1 million a ings). The
CiaoDVD da ase has 278,483 a ings on 99,746 i ems p o-
ided by 7,375 use s.
Also, hese da ase s ha e been used in he expe imen a-
ion because o hei high spa si y le el. Spa si y e e s o
he phenomenon ha occu s when he numbe o use s and
i ems in a a ings da abase is e y la ge, bu he numbe o
a ings gi en by use s o i ems is e y low. The e o e, a a -
ings da abase wi h a spa si y le el o
X%
only includes ha
pe cen age o all possible a ings. Speci ically, Fig. 8 shows
he le el o spa si y o he h ee da ase s. I can be seen ha
CiaoDVD has he highes alue (99,9%). These le els o
spa si y make hem sui able o measu ing he pe o mance
o he p oposals p esen ed in he con ex o ecommenda-
ion sys ems.
3.3.3 Pe o mance me ics
A ecommende sys em’s pe o mance is ypically assessed
using wo ypes o me ics: label-based o alue-based [58].
In classi ica ion, label-based measu es a e used o de e mine
he p ecision wi h which a class is assigned. In ou case,
he e a e wo classes o i ems: ecommended by a use and
no ecommended by a use . Thus, like in simila wo ks
[35], P ecision and Recall measu es a e used in his pape .
To compu e hese me ics, a con usion ma ix mus be c e-
a ed [59], in which he ou di e en cases a e conside ed:
●TP ( ue posi i e): The algo i hm co ec ly ecom-
mends an i em I ha is ac ually ele an o he use U.
and s o ed in he o ma Use - I em - Value (Tes da ase , T).
The c i e ia o ha selec ion a e as ollows: he elemen s
a e chosen a andom, wi h he idea ha he inal selec ion
mus be balanced, wi h hal based on i ems ecommended
by use s ( a ing alue equals o exceeds S ) and he o he
hal based on i ems no ecommended by use s ( a ing alue
less han o equal o S ). Then, he a ing alues o T a e
eplaced in he inpu da ase wi h he alue
−1
and nex A
is bina ized using S as a h eshold, esul ing in he aining
da ase
A′
. To de e mine he numbe o elemen s in he es
da ase , he
20%
o he o al ecommenda ions p esen in he
o iginal da abase ha been used.
Following, he BiBi bina y Biclus e ing algo i hm is
applied o he aining da ase
A′
( see jus i ica ion in Sec-
ion 2). In phase 3, new a ing alues o he pai s Use -
I em s o ed in T a e p edic ed. Finally, in phase 4, hese new
a ing alues a e compa ed o hose s o ed in T, and se e al
pe o mance measu es a e ob ained o assess he quali y o
he new ecommenda ions.
3.3.2 Da ase s desc ip ion
The da ase s used in he expe imen a ion a e he Mo ieLens
100 K and 1M da ase s [56] and he CiaoDVD da ase [57].
These h ee da ase s a e conside ed he s anda d da ase s
in e alua ing he ecommenda ion echniques. They include
he use ’s a ings o mo ies using he 5-poin a ing scale;
ha is, he sco e alue 5 is highly liked, and he sco e alue 1
is mos disliked. The da ase Mo ieLens_100K is composed
o a ings om 943 use s abou 1682 mo ies (100,000 a -
ings). The da ase Mo ieLens_1M includes he opinion o
Fig. 7 This is he gene al schema ollowed by all he expe imen s. I
is di ided in o ou di e en phases. The inpu consis s o he inpu
a ings da ase , A, and he h eshold alue o bina iza ion, S . The
aining (
A′
) and es (T) da ase s a e gene a ed in he i s phase. Then,
biclus e s a e gene a ed and ecommenda ions a e p edic ed based on
he es da ase using he no el BinRec me hodology. Finally, se e al
pe o mance measu es a e calcula ed in phase 4
1 3
Page 9 o 17 830
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sonaliza ion, al hough hese me hods end o ha e a highe
demand on compu a ional esou ces. In his con ex , he use
o biclus e s o educe he sea ch space and acili a e ecom-
menda ion in spa se en i onmen s is shown o be scalable.
In addi ion, he e is a g owing end owa ds he c ea ion o
hyb id RSs ha combine he bene i s o Biclus e ing wi h
ma ix ac o ing and deep lea ning models, which could
o e a balance be ween accu acy and e iciency in u u e
ecommende sys ems.
Au ho con ibu ions Domingo S. Rod íguez-Baena was esponsible
o he concep ion and design o he s udy. Ma e ial p epa a ion, da a
collec ion and analysis we e pe o med by Domingo S. Rod íguez-
Baena. All au ho s w o e he i s d a o he manusc ip and com-
men ed on ea lie e sions o he manusc ip . All au ho s ead and ap-
p o ed he inal manusc ip .
Funding Funding o open access publishing: Uni e sidad Pablo de
Ola ide/CBUA.
Da a a ailabili y All da a used is a ailable a he ollowing u l: h p s : /
/ g o u p l e n s . o g / d a a s e s / m o i e l e n s / and h p s : / / w w w . c s e . m s u . e d u / a n g
j i l i / d a a s e c o d e / u s s u d y . h m BinRec sou ce code is a ailable a h p s
: / / g i h u b . c o m / d s o d b a e / B i n R e c.
Decla a ions
E hical and in o med consen o da a used The au ho s ha e no con-
lic s o in e es o e hics. The da a used in he documen a e public and
accessible o esea ch pu poses.
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o in e es .
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