1 Introduction
This paper considers quality estimation from pairwise comparisons, which is a common method of preference elicitation from users. For example, the preference of a customer for one product over another can be thought of as the outcome of a comparison. Because customers are idiosyncratic, such outcomes will be noisy functions of the quality of the underlying items. A similar problem arises in crowdsourcing systems, which must strive for accurate inference even in the presence of unreliable or errorprone participants. Because crowdsourced tasks pay relatively little, errors are common; even among workers making a genuine effort, inherent ambiguity in the task might lead to some randomness in the outcome. These considerations make the underlying estimation algorithm an important part of any crowdsourcing scheme.
Our goal is accurate inference of true item quality from a collection of outcomes of noisy comparisons. We will use one of the simplest parametric models for the outcome of comparisons, the BradleyTerryLuce (BTL) model, which associates a realvalued quality measure to each item and posits that customers select an item with a probability that is proportional to its quality.
Given a “comparison graph” which captures which pairs of items are to be compared, our goal is to understand how accuracy scales in terms of this graph when participants make choices according to the BTL model.We focus on the regime where we perform many comparisons of each pair of items in the graph. In this regime, we are able to give a satisfactory answer to the underlying question. Informally, we prove that, up to various constants and logarithms, the relative estimation error will scale with the square root of measures of resistance in the underlying graph. Specifically, we propose an algorithm whose performance scales with graph resistance, as well as a matching lower bound. The difference between our upper and lower bounds depends only on the log of the confidence level and on the skewness of the item qualities. Additionally, we note that our performance guarantees scale better in terms of item skewness as compared to previous work.
1.1 Formal problem statement
We are given an undirected “comparison graph” , where each node has a positive weight . If , then we perform comparisons between and . The outcomes of these comparisons are i.i.d. Bernoulli and the probability that wins a given comparison according to the BTL model is
(1) 
The goal is to recover the weights from the outcomes of these comparisons. Because multiplying all
by the same constant does not affect the distribution of outcomes, we will recover a scaled version of the weight vector
.Thus our goal will thus be come up with a vector of estimated weights close, in a scaleinvariant sense, to the true but unknown vector^{1}^{1}1
We follow the usual convention of denoting random variables by capital letters, which is why
is capitalized while is not. . A natural error measure turns out to be the absolute value of the sine of the angle defined by and , which can also be expressed as (see Lemma A.1 in the Supplementary Information)(2) 
In other words, is the relative error to the closest normalization of the true quality vector . We will also discuss the connection between this error measure and others later on in the paper.
Following earlier literature, we assume that
for some constant . The number can be thought of as a measure of the skewness of the underlying item quality. Our goal is to understand how the error between and scales as a function of the comparison graph .
1.2 Literature Review
The dominant approach to recommendation systems relies on inferring item quality from raw scores provided by users (see (Jannach et al., 2016)). However, such scores might be poorly calibrated and inconsistent; alternative approaches that offer simpler choices might perform better.
Our starting point is the BradleyTerryLuce (BTL) model of Eq. (1), dating back to (Bradley & Terry, 1952; Luce, 2012), which models how individuals make noisy choices between items. A number of other models in the literature have also been used as the basis of inference, we mention the Mallows model introduced in (Mallows, 1957) and the PL and Thurstone models (see description in (Hajek et al., 2014)). However, we focus here solely on the BTL model.
Our work is most closely related to the papers (Negahban et al., 2012) and (Negahban et al., 2016)
. These works proposed an eigenvector calculation which, provided the number of comparisons is sufficiently large, successfully recovers the true weights
from the outcomes of noisy comparisons. The main result of (Negahban et al., 2016) stated that, given a comparison graph, if the number of comparisons per edge satisfied a certain lower bound, then it is possible to construct an estimate satisfying(3) 
with high probability, where are, respectively, the smallest and largest degrees in the comparison graph, is the spectral gap of a certain normalized Laplacian of the comparison graph, and both are normalized so that their entries sum to 1. It can be proved (see Lemma A.4) that the relative error on the lefthand side of Eq. (3) is within a factor of the measure provided that , so asymptotically these two measures differ only by factor depending on the skewness .
The problem of recovering was further studied in (Rajkumar & Agarwal, 2014), where the comparison graph was taken to be a complete graph but with comparisons on edges made at nonuniform rates. The sample complexity of recovering the true weights was provided as a function of the smallest sampling rate over pairs of items.
A somewhat more general setting was considered in (Shah et al., 2016)
, which considered a wider class of noisy comparison models which include the BTL model as a special case. Upper and lower bounds on the minimax optimal rates in estimation, depending on the eigenvalues of a corresponding Laplacian, were obtained for absolute error in several different metrics; in one of these metric, the Laplacian semimetric, the upper and lower bounds were tight up to constant factors. Similarly to
(Shah et al., 2016), our goal is to understand the dependence on the underlying graph, albeit in the simpler setting of the BTL model.Our approach to the problem very closely parallels the approach of (Jiang et al., 2011), where a collection of potentially inconsistent rankings is optimally reconciled by solving an optimization problem over the comparison graph. However, whereas (Jiang et al., 2011)
solves a linear programming problem, we will use a linear least squares approach, after a certain logarithmic change of variable.
We now move on to discuss work more distantly related to the present paper. We mention that the problem we study here is related, but not identical, to the socalled noisy sorting problem, introduced in (Braverman & Mossel, 2009), where better items win with probability at least for some positive . This assumption does not hold for the BTL model with arbitrary weights. Noisy sorting was also studied in the more general setting of ranking models satisfying a transitivity condition in (Shah et al., 2017) and (Pananjady et al., 2017), where nearoptimal minimax rates were derived. Finally, optimal minimax rates for noisy sorting were recently demonstrated in (Mao et al., 2017).
There are a number of variations of this problem that have been studied in the literature which we do not survey at length due to space constraints. For example, the papers (Yue et al., 2012; Szörényi et al., 2015) considered the online version of this problem with corresponding regret, (Chen & Suh, 2015) considered recovering the top ranked items, (Falahatgar et al., 2017; Agarwal et al., 2017; Maystre & Grossglauser, 2015) consider recovering a ranked list of the items, and (Ajtai et al., 2016) consider a model where comparisons are not noisy if the item qualities are sufficiently far apart. We refer the reader to the references within those papers for more details on related works in these directions.
1.3 Our approach
We will construct our estimate by solving a logleastsquares problem described next. We denote by the fraction of times node wins the comparison against its neighbor , and we further set . As the number of comparisons on each edge goes to infinity, we will have that approaches with probability one. Our method consists in finding as follows:
(4) 
This can be done efficiently by observing that it amounts to solving the linear system of equations
in the least square sense. Let to be the incidence matrix^{2}^{2}2Given an directed graph with nodes and edges, the incidence matrix is the matrix whose ’th column has a corresponding to the source of edge , a corresponding to the destination of node , and zeros elsewhere. For an undirected graph, an incidence matrix is obtained by first orienting the edges arbitrarily. of the comparison graph. Stacking up the into a vector , we can then write
Leastsquare solutions satisfy
or equivalently , where is the graph Laplacian. Finally, a solution is given by
(5) 
where is the MoorePenrose pseudoinverse. By using the classic results of (Spielman & Teng, 2014), Eq. (5) can be solved for to accuracy in nearly linear time in terms of the size of the input, specifically in iterations for some constant . We note that, for connected graphs, all solutions of (4) are equal up to a multiplicative constant and are thus equivalent in terms of criterion (2).
1.4 Our contribution
We will find it useful to view the graph as a circuit with a unit resistor on each edge; will denote the resistance between nodes and in this circuit, denotes the largest of these resistances over all pairs of nodes and similarly denotes the average resistance over all pairs. We will use to denote the set of edges lying on at least one simple path starting at and terminating at , with denoting the largest of the . Naturally, is upper bounded by the total number of edges in the comparison graph. The performance of our algorithms is described by the following theorem.
Theorem 1.
Let . There exist absolute constants constants such that, if and and , then we have, with probability at least , that
The main feature of this theorem is the favorable form of the bound in the setting when is large. Then only the leading term
dominates the expression on the righthandside. Taking square roots, it follows that, asymptotically,
where the notation hides logarithmic factor in .
Our other main result is that, in the regime when is large, there is very little room for improvement.
Theorem 2.
For any comparison graph , and for any algorithm, as long as for some absolute constant , we have that
where as before is the graph Laplacian.
Comparing Theorem 1 with Theorem 2, we see that the performance bounds of Theorem 1 are minimax optimal, at least up to the logarithmic factor in the confidence level and dependence on the skewness factor . We can thus conclude that the square root of the graph resistance is the key graphtheoretic property which captures how relative error decays for learning from pairwise comparisons. This observation is the main contribution of this paper.
We also provide a lower bound proving that we cannot get rid entirely of the dependence in .
Theorem 3.
There exists a graph for which, for any algorithm, there holds
1.5 Comparison to previous work
Table 1 quantifies how much the bound of Theorem 1 expressed in terms of improves the asymptotic decay rate on various graphs over the bound (Negahban et al., 2016). The notation ignores logfactors. Both random graphs are taken at a constant multiple threshold which guarantees connectivity; for ErdosRenyi this means and for a geometric random graph, this means connecting nodes at random positions at the unit square when they are apart.
Graph  Eq. (3)  Theorem 1 

Line  
Circle  
2D grid  
3D grid  
Star graph  
2 stars joined at centers  
Barbell graph  
Geo. random graph  
ErdosRenyi 
Most of the scalings for eigenvalues of normalized Laplacians used in Table 1 are either known or easy to derive. For an analysis of the eigenvalue of the barbell graph^{3}^{3}3Following (Wilf, 1989), the barbell graph refers to two complete graphs on vertices connected by a line of vertices., we refer the reader to (Landau & Odlyzko, 1981); for mixing times on the geometric random graph, we refer the reader to (Avin & Ercal, 2007); for the resistance of an ErdosRenyi graph, we refer the reader to (Sylvester, 2016).
In terms of the worstcase performance in terms of the number of nodes, our bound grows at worst as using the observation that . By contrast, for the barbell graph, the bound of (Negahban et al., 2016) grows as , and it is not hard to see this is actually the worstcase scaling in terms of the number of nodes.
1.6 Notation
The remainder of this paper is dedicated to the proof Theorem 1 (Theorem 2 and 3 are proved in the Supplementary Information). However, we first collect some notation we will find occasion to use.
As mentioned earlier, we let be the empirical rate of success of item in the comparisons between and ; thus so that the previously introduced can be expressed as . We also let , to which should converge asymptotically.
We will make a habit of stacking any of the quantities defined into vectors; thus , for example, denotes the vector in which stacks up the quantities with the choice of and consistent with the orientation in the incidence matrix . The the vectors and are defined likewise.
2 Proof of the algorithm performance (Theorem 1)
We begin the proof with a sequence of lemmas which work their way to the main theorem. The first step is to introduce some notation for the comparison on the edge .
Let be the outcome of a single coin toss comparing coins and
. Using the standard formula for the variance of a Bernoulli random variable, we obtain
(6)  
where we have defined . Observe that is always upper bounded by , where we remind .
We first argue that all are reasonably close to their expected values. For the sake of concision, we state the following assumptions about the constants, , and the quantity . Note that some of the intermediate results hold under weaker assumptions, but we omit these details for the sake of simplicity.
Assumption 1.
We have that , , and .
The following lemma is a standard application of Chernoff’s inequality. For completeness, a proof is included in Section D of the Supplementary Information.
Lemma 1.
There exist absolute constants constants such that, under Assumption 1, we have
The next lemma provides a convenient expression for the quantity in terms of the “measurement errors” . Note that the normalization assumption is not a loss of generality since is defined up to a multiplicative constant, and is directly satisfied if is obtained from (5).
Lemma 2.
Suppose is normalized so that . There exist absolute constants such that, under Assumption 1, there holds with probability
(7) 
and
(8) 
where is a diagonal matrix whose entries are the , for all edges .
Proof.
By definition
which we can write as It follows that
since is assumed normalized so that . Combining this with Eq. (5), we obtain
(9) 
We thus turn our attention to analyzing the vector . Our analysis will be conditioning on the event that for all
(10) 
which, by Lemma 1, holds with probability at least . We will call this event .
We begin with one implication that comes from putting together event and our assumption (in Assumption 1) for a constant that we can choose: that we can assume that
(11) 
Indeed, from Eq. (10) for this last equation to hold it suffices to have Observing that
we see that assuming is sufficient for Eq. (11) to hold conditional on event .
Our analysis of begins with the observation that since
we have that
Next we use Taylor’s expansion of the function , for which we have
to obtain that can thus be expressed as
(12) 
where lies between and (and lies thus between and ). We can rewrite this equality in a condensed form
(13) 
where corresponds to the second terms in (12), which we will now bound. Because we have conditioned on event , which, as discussed above implies , we actually have that and that lying between and belongs to . Hence
for , and where we have used (10) for the last inequality. Plugging this into Eq. (13) and (9) completes the proof, and Eq. (8) follows from the last equation combined with the fact that for all . ∎
The following lemma bounds how much the ratios of our estimates differ from the corresponding ratios of the true weights . To state it, we will use the notation
where is the standard notation for the ’th basis vector. Furthermore, we define the product
(14) 
Observe that the matrix is positive semidefinite, which implies by standard arguments that
holds for all vectors .
Lemma 3.
Suppose is normalized so that . There exist absolute constants such, under Assumption 1, with with probability , we have that for all pairs ,
(15) 
and
Proof.
Having proved Lemma 3, we now analyze each of the terms in the righthand side of Eq. (15). We begin with the second term, i.e., with . To bound it, we will need the following inequality.
Lemma 4.
For any , we have that
where, recall, is the resistance between nodes and , and is the set of edges belonging to some simple path from to .
Proof.
The result follows from circuit theory, and we sketch it out along with the relevant references. The key idea is that the vector has a simple electric interpretation. We have that and the ’th entry of is the current on edge when a unit of current is put into node at removed at node . For details, see the discussion in Section 4.1 of (Vishnoi, 2013).
This lemma follows from several consequences of this interpretation. First, the entries of are an acyclic flow from to ; this follows, for example, from Thompson’s principle which asserts that the current flow minimizes energy (see Theorem 4.8 of (Vishnoi, 2013)). Moreover, Thompson’s principle further asserts that . Finally, by the flow decomposition theorem (Theorem 3.5 in (Ahuja et al., 2017)), we can decompose this flow along simple paths from to ; this implies that .
With these facts in mind, we apply CauchySchwarz to obtain
and then conclude the proof using Holder’s inequality
∎
As a corollary, we are able to bound the second term in Eq. (15). The proof follows immediately by combining Lemma 4 with Lemma 3.
Corollary 1.
There exist absolute constants such that, under Assumption 1, with probability , we have that for all pairs ,
We now turn to the firstterm in Eq. (15), which is bounded in the next lemma.
Lemma 5.
There exist absolute constants such that, under Assumption 1, with probability we have that for all pairs ,
Proof.
The random variable (where, recall, is the outcome of a single comparison between nodes and ) is zeromean and supported on an interval of length , and consequently it is subgaussian^{4}^{4}4A random variable is said to be subgaussian with parameter if for all . with parameter (see Section 5.3 of (Lattimore & Szepesvári, 2018)). By standard properties of subgaussian random variables, it follows that is subgaussian with . It follows then from Theorem 2.1 of (Hsu et al., 2012) for subgaussian random variables applied to , that for any there is a probability at least that
where we have used , and . We now compute this trace.
(18)  
where the second equality uses the wellknown property of the MoorePenrose pseudoinverse: for any matrix (see Section 2.9 of (Drineas & Mahoney, 2018)); and last equality uses a wellknown relation between resistances and Laplacian pseudoinverses, see Chapter 4 of (Vishnoi, 2013). The result follows then from the application of (2) to . ∎
Having obtained the bounds in the preceding sequence of lemmas, we now return to Lemma 3 and “plug in” the results we have obtained. The result is the following lemma.
Lemma 6.
There exist absolute constants such, under Assumption 1, with probability , we have that for all pairs ,
Proof.
By putting together Lemma 3 with Corollary 1 and Lemma 5, we obtain that, with probability at least ,
(19) 
Observe that for a sufficiently large , if then the term is bounded by . Hence, if is also at least (which holds due to Assumption 1), equation (19) implies
(20) 
A particular implication is that . Applying the inequality to (20) leads then to
and now using , the proof follows by combining the last equation with Eq. (19). ∎
The next lemma demonstrates how to convert Lemma 6 into a bound on the relative error between and the true weight vector .
Lemma 7.
Suppose we have that
for all . Fix index . Then there hold
(21)  
(22) 
where .
Proof.
3 Experiments
The purpose of this section is twofold. First, we would like to demonstrate that simulations are consistent with Theorem 1; in particular, we would like to see error scalings that are consistent with the average resistance, rather than e.g., spectral gap. Second, we wish observe that, although our results are asymptotic, in practice the scaling with resistance appears immediately, even for small . Since our main contribution is theoretical, and since we do not claim that our algorithm is better than available methods in practice, we do not perform a comparison to other methods in the literature. Additional details about our experiments are provided in Section E in the Supplementary Information.
We begin with ErdosRenyi comparison graphs. Figure 1 shows the evolution of the error with the number of comparisons per edge. The error decreases as as predicted. Moreover, this is already the case for small values of .
Next we move to the influence of the graph properties. Figure 2 shows that the average error is asymptotically constant when grows while keeping the expected degree constant, and that it decreases as when the expected degree grows while keeping constant. This is consistent with our analysis in Table 1, and with the results (Boumal & Cheng, 2014) showing that the average resistance of ErdosRenyi graphs evolves as .
We next consider lattice graphs in Figure 3. For the 3D lattice, the error appears to converge to a constant when grows, which is consistent with our results since the average resistance of 3D lattice is bounded independently of . The trend for the 2D lattice appears also consistent with a bound in predicted by our results since the resistance on 2D lattice evolves as .
(a)  (b) 
(a)  (b) 
4 Conclusion
Our main contribution has been to demonstrate, by a combination of upper and lower bounds, that the error in quality estimation from pairwise comparisons scales as the graph resistance. Our work motivates a number of open questions.
First, our upper and lower bounds are not tight with respect to skewness measure . We conjecture that the scaling of for relative error is optimal, but either upper of lower bounds matching this quantity are currently unknown.
Second, it would interesting to obtain nonasymptotic version of the results presented here. Our simulations are consistent with the asymptotic scaling (ignoring the dependence on
) being effective immediately, but at the moment we can only prove this scaling governs the behavior as
.Finally, it would be interesting to further explore scalings on random graphs. For example, one might conjecture that, on connected ErdosRenyi graphs, a constant number of comparisons between neighbors suffice to determine with relative error that goes to zero when grows.
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Appendix A Supplementary Information: relation between different relative error criteria
Our relative error criterion of differs somewhat from the criterion used in (Negahban et al., 2016), which was
where both and and need to be normalized to sum to . To represent this compactly, we introduce the notation for positive vectors , defined as
so that the criterion of (Negahban et al., 2016) can be written simply as