Efficient Query Suggestions in the Long Tail

Efﬁcient Query Suggestions in the Long Tail
Joint work:
R. Perego, F. Silvestri, H. Vahabi, R. Venturini, HPC Lab, Italy
F. Bonchi, Yahoo! Research, Spain

Wednesday, August 24, 2011

Query suggestion practices

• Use of the Wisdom of the Crowd mined
from Query Logs to recommend related
queries that are likely to better specify the
information need of the user
• shorten length of user sessions
• enhance perceived QoE


Queries in the Head


Queries in the Long Tail


?


?

?

?

Rare and never-seen

?
queries account for
more than 50% of
the trafﬁc!


Open issues
• Sparsity of models:
• query assistance services perform
poorly or are not even triggered
on long-tail queries
• Performance:
Popularity

• on-line process going in parallel
with query answering

Queries ordered by popularity


SoA: Query Flow Graph

• Query-centric approach
• Suggest queries by
computing Random Walks
with Restarts (RWRs) on
the query-flow graph
(QFG) by starting from
the current user query

P. Boldi, F. Bonchi, C. Castillo, D. Donato, A. Gionis, S.Vigna: The query-flow graph: model and applications. CIKM 2008: 609-618
P. Boldi, F. Bonchi, C. Castillo, D. Donato, A. Gionis, S.Vigna: Query suggestions using query-flow graphs. WSCD, 2009

Query-centric suggestions
Computing RWRs on a huge graph, e.g., built
from a QL recording 580,797,850 queries
(from Y! us):
• |V| 28,763,637
• |E| 56,250,874


Query-centric suggestions
Computing RWRs on a huge graph, e.g., built
from a QL recording 580,797,850 queries
(from Y! us):
• |V| 28,763,637
• |E| 56,250,874

• |{q: f(q)=1}| 162,221,967 (28%)


Term-centric opportunities
But, in the same Y! QL:
• queries 580,797,850
• Term occurrences 1,343,988,549


Term-centric opportunities
But, in the same Y! QL:
• queries 580,797,850
• Term occurrences 1,343,988,549

• |{t: f(t)=1}| 5,099,145 (0.04%)


The TQGraph
*#%+,)
"#%&'("')& /#)(
%-!&.'"#

!"##

"#%&'("')&
!"##$ /#)($*#%+,)
"#%&'("')&
*#%+,)$
%-!&.'"#
!"#$%&'

Term nodes are added to the
QFG which have only outgoing
links pointing at the query
nodes corresponding to the
queries in which the terms
occur.


TQGraph model
Suggestions for an incoming query q composed of
terms {t1,...tm} ⊆ T are generated from G by
extracting the center-piece subgraph starting from
the nodes corresponding to terms t1,...,tm
• perform m Random Walks with Restart from
each one of the m term nodes corresponding to
terms in q
• multiply component-wise the resulting m
stationary distributions


fro
100 queries on Yahoo! useful somewhat not useful M
α = 0.9 48% 11% 41% re

TQG effectiveness
α = 0.5
α = 0.1
41%
37%
20%
20%
39%
43%

Table 2: Effectiveness of TQGraph-based recommen-
• User study results comparing TQG and QFGand query
dations on the two different set of queries effectiveness
for two different testbeds (Y! US and MSN QLs).
logs, by varying the restart parameter α.

TREC on MSN useful somewhat not useful
TQGraph α = 0.9 57% 16% 27%
QFG 50% 9% 42% sid
is
100 queries on Yahoo! useful somewhat not useful sit
TQGraph α = 0.9 48% 11% 41% qu
QFG 23% 10% 67% lem
in
m
Table 3: User study results comparing effectiveness th
of our method with the baseline for the two different
testbeds. qu

queries on Yahoo! useful somewhat not useful MSN query
ing to outperform the effectiveness of QFG. The fact that log. The query is “lower heart rate”. Below we
0.9 48% 11% 41% report the top 5 recommendations.
0.5
we, instead, achieve such a remarkable result is, actually, an
41% 20% 39%
additional benefit of TQGraph-based 43% methods.

Effectiveness on rare queries
0.1 37% 20% Query: lower heart rate
Anecdotal evidence. We next show a few examplesSuggested Query of Score
query recommendations. We start from queries that have things to lower heart rate 2.9 e−14
e 2: Effectiveness of TQGraph-based recommen-
lower heart rate through exercise 2.6 e−14
nsnever been observed in the query log, i.e., the most difficult
on the two different set of queries and query
accelerated heart rate and pregnant 2.9 e−15
by varying the restart parameter α. is one among the eight
cases. The first query that we show

•
web md 2.0 e−16
on MSN Anecdotal evidence
from the TREC testbed that do not appear at all in the problems
ECMSN query log. useful query is “lower heart rate”. Below we
The somewhat not useful
heart 8.0 e−17

Graph α = 0.9 top 5 57%
report the recommendations. 27%
16% We can observe that all the top-5 suggestions can be con-
50% 9% 42% sidered pertinent to the initial topic. Moreover, even if this
Query: lower heart rate is not an objective in this paper, they present some diver-
queries on Yahoo! useful somewhat not useful
Suggested Query Score the first two are how-to queries, while the last three are
sity:
Graph α = things to lower heart rate
0.9 48% 11% 41% queries related to finding information w.r.t. possible prob-
2.9 e−14
23% 10%
lower heart rate through exercise 67%
2.6 e−14 Query not occurring
lems (with one very specific for pregnant women). The most
interesting recommendation is probably “web md”, which
e 3: Userweb mdresults comparing effectiveness
study
−15
in the training log
accelerated heart rate and pregnant 2.9 e makes perfect sense4 , and has a large edit distance from
2.0 e−16 original query.
the
r method with the baseline for the two different
heart problems 8.0 e−17The next query we present is a rare (i.e., rarely appearing
eds. query): “dog heat”; which appears only twice in the MSN
query log.
eas, we pair TREC queries up with the model built on can be con-
We can observe that all the top-5 suggestions
SN query log. In fact,to the initial topic. Moreover, even if this
sidered pertinent the period from which TREC Query: dog heat
come, is an objectiveperiod in paper, MSN queries some diver-
is not close to the in this which they present
ubmitted. first two are how-to queries, while the last three are Suggested Query Score
sity: the heat cycle dog pads 4.3 e−10
generated the top-5 recommendations for each query
queries related to finding information w.r.t. possible what happens when female dog is
prob-
Query occurring twice
ng both the QFG and the TQGraph with different pa-
lems (with one very specific for pregnant women). The most in heat & a male dog is around
ers setting. Using a web interface each assessor was 4.0 e−10
in the training log
nted a random query followed by the list of all the“web md”, boxer dog in heat
interesting recommendation is probably dif-
sense4 , Recommendations were
which
makes perfect produced.and has a large edit distancedog in heat symptoms
recommendations from
3.99 e−10
3.98 e−10
the original query.
nted shuffled, in order for the assessor to not be able to behavior of a male dog
which system produced them. We give (i.e., rarely appearing around a female dog in heat
guishThe next query we present is a rare assessors 3.95 e−10
ossibility to “dog heat”; search engine results for the in the MSN
query): observe the which appears only twice
al query and the recommended query that was being
query log. As in the previous example, the top-5 suggestions are
ated. The assessor was asked to rate a recommendation qualitatively good and present some diversity. Also, the
one of the following scores: useful, somewhat useful,
Wednesday, August 24, 2011 TQGraph-based method returns long queries, thus likely to

TQG pros

• provide query suggestions of quality
comparable/better than QFG even for rare
and unique queries
• several possible optimizations for achieving


TQG pros

• provide query suggestions of quality
comparable/better than QFG even for rare
and unique queries
• several possible optimizations for achieving
an efﬁcient on-line query
recommendation service


Indexing precomputed suggestions
!"#$%&'(

012"#3"4%014"5%#"6#"7"1389:1%:;%3<"%=>=7%
?:$6@3"4%:1%3<"%!AB#86<C%!<"%D"5-?:1%-7%$84"%
@6%:;%3"#$%1:4"7(%6:791E7%8#"%3<"%7389:18#F%
4-73#-G@9:1%28D@"7C%

)*%+,-%$.(/01*$023+,-(,4("35$.(6,751(0-(*'5(
!"#$%&'(%1(,2*%0-57(2.(%(898(4$,:(!5$:(;(

• recommendations for an incoming query+ are computed -by
!"#$%&% '&()*% ')()+*% ') (),*% ') ().-/&**%
processing the posting lists associated with the terms in the query
90*'0-(23<=5*1(>35$051(%$5(1,$*57(2.(*'50$(?/1@()<,$51(%$5(
%&&$,A0:%*57(2.(*'5(B$5%*51*(2,3-7C(0@5@(D0(4,$(%EE(0(F(G@(


Indexing precomputed suggestions
!"#$%&'(

012"#3"4%014"5%#"6#"7"1389:1%:;%3<"%=>=7%
?:$6@3"4%:1%3<"%!AB#86<C%!<"%D"5-?:1%-7%$84"%
@6%:;%3"#$%1:4"7(%6:791E7%8#"%3<"%7389:18#F%
4-73#-G@9:1%28D@"7C%

)*%+,-%$.(/01*$023+,-(,4("35$.(6,751(0-(*'5(
!"#$%&'(%1(,2*%0-57(2.(%(898(4$,:(!5$:(;(

• recommendations for an incoming query+ are computed -by
!"#$%&% '&()*% ')()+*% ') (),*% ') ().-/&**%
processing the posting lists associated with the terms in the query
90*'0-(23<=5*1(>35$051(%$5(1,$*57(2.(*'50$(?/1@()<,$51(%$5(
:) O(|T|) posting lists
%&&$,A0:%*57(2.(*'5(B$5%*51*(2,3-7C(0@5@(D0(4,$(%EE(0(F(G@(

:( O(|Q|) length of each posting list

Pruning posting lists
• sort postings by probability and prune them
at a reasonable threshold p, e.g. 20,000
me quality of those

Graph-based recom-
lity those produced
ar, TQGraph-based
very large fraction
as we shall present
QGraph can be pre-
d list”-based repre-
very fast generation

PH
ved online, a query
ciently, possibly in
n we introduce some
eneration of recom-
r, we show that the

Pruning posting lists
• sort postings by probability and prune them
at a reasonable threshold p, e.g. 20,000
me quality of those

Graph-based recom-
lity those produced
ar, TQGraph-based
very large fraction
as we shall present
QGraph can be pre-
d list”-based repre-
very fast generation

PH
ved online, a query
O(|T|) lists, each of size O(p) and no loss in quality!
ciently, possibly in
n we introduce some
eneration of recom-
r, we show that the

!"#$%&'(

Bucketing probabilities 012"#3"4%014"5%#"6#"7"1389:1%:;%3<"%=>=7%

• Most space used for storing probabilities
?:$6@3"4%:1%3<"%!AB#86<C%!<"%D"5-?:1%-7%$84"%
@6%:;%3"#$%1:4"7(%6:791E7%8#"%3<"%7389:18#F%
4-73#-G@9:1%28D@"7C%

• Given ε < 1, we can arrange postings in
buckets implicitly coding the approximate
probabilities )*%+,-%$.(/01*$023+,-(,4("35$.(6,751(0-(*'5(
!"#$%&'(%1(,2*%0-57(2.(%(898(4$,:(!5$:(;(

!"#$%&% '&()*% ')()+*% ')+(),*% ')-().-/&**%

90*'0-(23<=5*1(>35$051(%$5(1,$*57(2.(*'50$(?/1@()<,$51(%$5(
%&&$,A0:%*57(2.(*'5(B$5%*51*(2,3-7C(0@5@(D0(4,$(%EE(0(F(G@(


!"#$%&'(

Bucketing probabilities 012"#3"4%014"5%#"6#"7"1389:1%:;%3<"%=>=7%

• Most space used for storing probabilities
?:$6@3"4%:1%3<"%!AB#86<C%!<"%D"5-?:1%-7%$84"%
@6%:;%3"#$%1:4"7(%6:791E7%8#"%3<"%7389:18#F%
4-73#-G@9:1%28D@"7C%

• Given ε < 1, we can arrange postings in
buckets implicitly coding the approximate
probabilities )*%+,-%$.(/01*$023+,-(,4("35$.(6,751(0-(*'5(
!"#$%&'(%1(,2*%0-57(2.(%(898(4$,:(!5$:(;(

!"#$%&% '&()*% ')()+*% ')+(),*% ')-().-/&**%

90*'0-(23<=5*1(>35$051(%$5(1,$*57(2.(*'50$(?/1@()<,$51(%$5(
%&&$,A0:%*57(2.(*'5(B$5%*51*(2,3-7C(0@5@(D0(4,$(%EE(0(F(G@(

• Each entry coded with a few bits, e.g., 11-19 bits
• ~5x reduction!
• no loss in quality!

Caching posting lists
ided
both
• achieving in-memory query suggestion
are

d us-
e lost
ever,
sults.
qual-
en in
hat a
ed by
make
iﬀer-
which Figure 4: Miss ratio of our cache as a function of
t Wednesday, August 24, 2011
our

Conclusions
• TQG model to overcome limitations of current query
recommenders
• based on a principled, term-centric approach supporting rare and
never-seen queries
• deployment with a efﬁcient inverted index resulting in effectiveness
comparable/better to SoA approaches
• the pruning, bucketing, caching techniques proposed constitute a
independent contribution in the area of efﬁciency in large scale RWR
computations
• reduction of about 80% in the space occupancy w.r.t.
uncompressed data structures
• in-memory RWRs on huge graphs with 90+ % hit-ratio cache


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