A Practical Divide-and-Conquer Approach for Preference-Based Learning to Rank

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A Practical Divide-and-Conquer Approach for Preference-Based Learning to Rank

Han-Jay Yang

Department of Computer Science and Information Engineering

National Taiwan University hanjayyang@gmail.com

Hsuan-Tien Lin Department of Computer Science

and Information Engineering National Taiwan University


Abstract—Preference-based learning to rank (LTR) is a model that learns the underlying pairwise preference with soft binary classification, and then ranks test instances based on pairwise preference predictions. The model can be viewed as an alternative to the popular score-based LTR model, which learns a scoring function and ranks test instances based on their scores directly.

Many existing works on preference-based LTR address the step of ranking test instances as the problem of weighted minimum feed- back arcset on tournament graph. The problem is somehow NP- hard to solve and existing algorithms cannot efficiently produce a decent solution. We propose a practical algorithm to speed up the ranking step while maintaining ranking accuracy. The algorithm employs a divide-and-conquer strategy that mimics merge-sort, and its time complexity is relatively low when compared to other preference-based LTR algorithms. Empirical results demonstrate that the accuracy of the proposed algorithm is competitive to state-of-the-art score-based LTR algorithms.


The problem of learning to rank (LTR) arises in many applications ranging from web search to recommendation systems [1]–[3]. Given a list of items, the goal of LTR is to rearrange the items in a certain order such that the more relevant items are ranked before the less relevant ones.

Because of its significance for vast applications, many different models are proposed to deal with the LTR problem [1]–[3].

There are two major categories of LTR models, namely score-based models and preference-based models. In score- based models, the learning algorithm in the training stage aims to produce a scoring function that maps each item to a real-valued score; then, the prediction algorithm produces the final ranking from the linear order induced from the scoring function. The training stage of score-based LTR models is in this sense similar to that of common regression models, which also map items to scores. Many works in tackling the LTR problem thus borrow the so-called pointwise ranking perspective from regression, such as PRanking [4] and large margin ordinal regression [5].

Nevertheless, score-based LTR models care about the good- ness of the final ranking while regression models care about the accuracy of scores themselves. Such difference makes pointwise ranking less satisfactory in producing a decent final ranking. Many other score-based LTR models therefore try to optimize different ranking-related loss functions. The loss functions often depend on the pairwise or listwise relations

among the whole ranking. For instance, the Kendal-Tau loss function calculates the number of mis-ranked item pairs in a ranking list and is considered in the pairwise ranking ap- proaches like RankBoost [6], RankSVM [7]–[9], and RankNet [10]. Other loss functions are designed based on common metrics used in information retrieval such as NDCG [11] and MAP [12], and depend on the whole ranking order rather than the order of item pairs. Those listwise loss functions are the core of listwise LTR approaches. Some of the approaches like SVM-MAP [13] exploit a smoothed surrogate of the loss func- tion, and others like AdaRank [14] and LambdaMART [15]

directly optimize the non-convex objective by approximating the gradient during optimization.

Overall the pairwise and listwise approaches have deliv- ered promising results [2], [16], boasting superior ranking performance and offering more versatility in dealing with different ranking metrics. Regardless of the approaches taken, however, score-based models are required to provide a point- wise (item-wise) score for an item. Such requirement makes optimizing against pairwise/listwise ranking objective difficult, leading to a usually more sophisticated training stage for pairwise/listwise score-based models.

Preference-based models have been considered in various works [17]–[19]. In preference-based models, instead of learn- ing the scoring function for each particular item, a preference function over pairs of items is learned in the training stage.

The preference function is then decoded to produce a ranking on a set of test items in the prediction stage. The decoding is generally done by minimizing the pairwise error (Kendall- Tau) subject to the preference function on the set of test items. Minimizing such pairwise error, however, is equivalent to a well-known NP-hard problem called weighted Min-FAST (weighted minimum feedback arc set on tournament graph) [20], [21].

Despite for some works that are based on heuristics [22], ex- isting works on solving the weighted Min-FAST problem often focus on the theoretical perspective. For instance, [18] proves that a simple approach [17], which is of time complexity quadratic to the number of test instances, is a 2-approximation of the optimal solution for a special ranking task called bipartite ranking. [19], [23] make improvements by using a quick-sort-like approach to achieve 3-approximation within


sub-quadratic time complexity, and [24], [25] further achieve (1 + )-approximation within sub-quadratic time. Given the theoretical nature of the works, though many of them have discussed the possibility to employ preference-based LTR [19], [25], [26], few have yet to design algorithms that work well in practice or examine them properly on real-world data.

This work is dedicated to resolve the issue above. We design FUZZY-SORT, a divide-and-conquer approach for the prediction stage of preference-based LTR. FUZZY-SORT is inspired by the quick-sort routine within [19], [23], and employs a bottom-up routine to recursively sort items in the query list, actively querying more important pairwise preferences in the process. The divide-and-conquer scheme enables FUZZY-SORTto return a ranking list in sub-quadratic complexity. Experiments demonstrate that FUZZY-SORT is indeed more efficient than existing approaches for solving the Min-FAST task for preference-based LTR. In addition, experiments against several preference-based and score-based ranking algorithms on real-world data validate that FUZZY- SORT delivers competitive accuracy in most of the data sets. The results make FUZZY-SORT a first-hand choice for preference-based LTR and a promising approach for LTR problems.


We first consider the general notion of ranking. Given a set of N items numbered from 1 to N , a ranking of the items is a permutation π ∈ SN, where SN is the permutation group of order N , so the i-th ranked (or i-th relevant) item is π(i). The goal of learning to rank (LTR) is to learn from training data a ranking model that is capable of producing accurate rankings for future unseen lists of items.

The process of LTR could generally be broken down into two stages. In the training stage, a set of items with indices X = {1, 2, . . . , N } and their corresponding feature vectors x1, x2, . . . , xN are given as input; also given are K queried subsets of items Xk ⊆ X along with their true rankings πk being a permutation of items in Xk for k = 1, 2, . . . , K.

Note that the true rankings usually correspond to the responses taken from different queries in real world applications, so it is possible for true rankings in different queried lists to disagree in some of the item orders. The task of a ranking algorithm in the training stage, using the given information, is to train a model capable of reconstructing a good ranking over an arbitrary given set of items.

In the prediction stage, again a (possibly different) set of items with indices X0= {1, 2, . . . , N0} and their correspond- ing feature vectors x01, x02, . . . , x0N0 are given. The task is to produce a ranking π over X0that is as close to the (unknown) true ranking π as possible.

For the preference-based LTR framework, a preference function h : X2 → [0, 1], where X is the domain of feature vectors, is trained in the training stage. The preference function attempts to dictate the relative precedence of two given items softly. A larger h(xi, xj) indicates that one would prefer xi to be ranked before xj. The preference function h is

then given as an “oracle” in the prediction stage, where the ranking is constructed from the oracle’s judgment over pairwise preferences of the items.

One final note is that depending on how the preference function h is produced, the symmetric relation h(xi, xj) = 1−h(xj, xi) may not necessarily hold. Asymmetric preference functions can always be symmetrized by hsym(xi, xj) =


h(xi,xj)+h(xj,xi) if either h(xi, xj) or h(xj, xi) is non-zero, or hsym(xi, xj) = 12. We will drop the sym subscript and consider symmetric preference functions only in this work.

There are various different measures to evaluate the ranking performance [27]. Some popular ones are pairwise error [28], NDCG [11] and MAP [12]. For the scope of this paper, we consider the pairwise error given its close connection to preference-based LTR. For a given ranking π on N items, its pairwise error is simply the proportion of wrongly ordered pairs L(π) =



N (N −1)/2 , where ≺ denotes the precedence specified in the true ranking π, and ≺π denotes the prece- dence induced from π.

Because the preference function h obtained from the train- ing stage of preference-based LTR can be inconsistent, the prediction stage needs to solve an optimization problem that minimizes the pairwise error subject to h. Such a problem is equivalent to the min-FAST (minimum weighted feedback arc set on tournament graph) problem. Specifically, given a weighted tournament graph G = (V, W ) the Min-FAST problem seeks to find a permutation of vertices π such that the quantity P

i≺πjWji is minimized, where ≺π denotes precedence induced by the permutation π. The prediction stage of preference-based LTR can simply be done by letting the nodes in graph G correspond to the items to be ranked, and Wij = h(xi, xj). Then, minimizing P

i≺πjWji is the same as minimizing the pairwise error with respect to the soft predictions h(xi, xj).

Theoretically, the weighted min-FAST problem is NP-hard to solve [23]. Thus, currently, approximation is required to realistically solve it. Two approaches on solving the problem are highly relevant to this work, as discussed below.

a) FAS-PIVOT: [23] showed that simply applying QUICK-SORT using h(xi, xj) as a soft comparison function results in expected 3-approximation to the weighted min-FAST problem [23]. That is, if we perform QUICK-SORT multiple times with random pivots, and put i before j with probability h(xi, xj), the expected pairwise error is no larger than 3 times the optimal error. The resulting FAS-PIVOT approach demonstrates that repetitively running a sorting algorithm of query complexity O(N lg N ) on N items may lead to a practical way to solve min-FAST (and thus the prediction stage problem of preference-based LTR). Nevertheless, because the convergence rate of the approach is not clear, FAS-pivot potentially needs many iterations to converge, and it is yet to be studied whether FAS-pivot works reasonably well for preference-based LTR in practice.

b) SORT-BY-DEGREE: Long before FAS-PIVOT was proposed, SORT-BY-DEGREE [17] is a popular heuristic used


to solve the weighted min-FAST problem (for ranking). For- mally, for each node i, we denote its net outgoing degree as

δnet(i) =X


h(xi, xj) − h(xj, xi)

SORT-BY-DEGREE simply sorts the nodes by δnet(·) as the solution. [18] showed that Sorting-by-Degree is 2-optimal for the non-weighted, bipartite ranking version of the min- FAST problem, and [29] extended the theoretical result to the weighted, bipartite ranking version of the problem. The approach, however, requires calculating δnet(i) for each node, which takes O(N2) time for N items, which can be slow if N is large.


As discussed, existing approaches for solving min-FAST suffer from efficiency. FAS-PIVOTmay need potentially many iterations; SORT-BY-DEGREE requires quadratic amount of calculation. We hope to design an approach that is efficient enough for practice. Our idea lies somewhere in the middle of FAS-PIVOT and SORT-BY-DEGREE, combining the stronger suit of both approaches.


Consider first the MERGE-SORT alternative of the FAS- PIVOT approach. Instead of sorting the items like QUICK- SORT in a top-down fashion, we sort in a bottom-up order.

Recall that MERGE-SORT recursively partitions the list of items to be ranked into two almost-equal-sized sublists until very short sublists are reached and trivially sorted. Two sorted sublists are then repeatedly merged from bottom to top, where the merging is done by making a single-direction pass on both-sublists to “zip” the items in proper order. During the pass, relation over two items would be determined by a single comparison, deciding their order in the merged list.

For the weighted min-FAST problems, the preference re- lations h between two items are some real values between 0 and 1. FAS-PIVOT treats those values probabilistically for comparison in QUICK-SORT. The probabilistic treatment results in instability, which is one of the reasons that FAS- PIVOTmay need many iterations.

To solve the instability problem, we do not treat the values probabilistically. Instead, we borrow ideas from SORT-BY- DEGREE as part of a more stable merging subroutine. The subroutine is called FUZZY-MERGE because of its nature.1 Consider in the merging step one is faced with two “roughly”

sorted lists and want to merge them into another “roughly”

sorted one. By “roughly” sorted we mean that most items in the lists are in order. It is then reasonable to assume that the (unknown) true item that should be ranked first is among the W/2-first items of both lists for some parameter W . We therefore use a fuzzy-merge window of size W that takes in the first W/2 items that are still available from both lists, and use SORT-BY-DEGREE to determine the item that should be

1We choose FUZZYhere to hint that the merging is inexact in nature, not to be confused with anything related to the fuzzy logic.

placed first in the merged list. Once the locally-first item is determined, the window takes in the next item from list and iteratively selects the following winners one by one.

The subroutine FUZZY-MERGE is shown in Algorithm 2, where the fuzzy window is denoted as the candidate set S which we will be selecting the locally-first item from. We start by initializing S with the first W/2 items from both lists.

While we have not depleted both lists, we continuously select from S the locally-first item x using SORT-BY-DEGREE. The item x is then appended to the end of the current merged list AM, and we update S to include the next possible item outside of S, depending on which list item x was taken from.

At the end of FUZZY-MERGE, we would have perform |AL|+

|AR| selections of locally-first item from the candidate set S using SORT-BY-DEGREE, and the resulting merged list AM is returned.

. . . . . . . . . merged “wisely”

(a) The MERGE-SORT-like framework

pairwise information between all elements in window is used

(b) Fuzzy merge window

Fig. 1. Shown in (a), FUZZY-SORTroughly follows the divide-and-conquer framework of MERGE-SORT. As demonstrated in (b), elements in the window for both lists are shaded, among which an elements is selected to be the

“locally-first”. Note that the elements in the window need not be continuous in a list, as elements in the middle could get picked prematurely by the sort- by-degree heuristic.

We hereby give our proposed approach for the prediction stage problem in Algorithm 1. The overall structure is pretty much the same with conventional MERGE-SORT, recursively splitting the lists in two, and fuzzily merging the returned (roughly) sorted lists.

Algorithm 1 FUZZY-SORT

1: Input: List of elements V , Oracle h : V × V → [0, 1], window size W

2: Return: Reordering of elements in V

3: if |V | ≤ W/2 then

4: return V

5: end if

6: W ← min(W, |V |)

7: m ← b|V |/2c

8: VL← Fuzzy-Sort(V [ 1 . . . m ], h, W )

9: VR← Fuzzy-Sort(V [ m + 1 . . . |V | ], h, W )

10: return Fuzzy-Merge(VL, VR, W )


Algorithm 2 Fuzzy-Merge

1: Input: Two FUZZY-SORTed lists AL, AR, Window size W

2: Return: Merged list

3: WL← bW/2c

4: WR← dW/2e

5: S ← {AL[1 . . . WL]} ∪ {AR[1 . . . WR]}

6: AL← AL[WL+ 1 . . . |AL|]

7: AR← AR[WR+ 1 . . . |AR|]

8: AM ← [ ]

9: while S 6= φ do

10: x ← argmaxv∈SδSnet(v)

11: S ← S\x

12: AM. Append(x)

13: if x came from AL then

14: if AL6= [ ] then

15: S ← S ∪ AL. Front()

16: AL. Pop-Front()

17: end if

18: else

19: if AR6= [ ] then

20: S ← S ∪ AR. Front()

21: AR. Pop-Front()

22: end if

23: end if

24: end while

25: return AM

For line 10 of Algorithm 2 (selection of the “locally-first”

element in window), a na¨ıve approach would need to examine the subgraph induced by all items in the window, in turn requiring time quadratic to window size. This is however not necessary. One can dynamically maintain δSnet(v) for any v ∈ S, and updating only when an item is added or removed from S in O(W ) operations. As there is only a fixed number of insertion and deletion of elements from S (linear to |AL| + |AR|), we see the time complexity and query com- plexity are both O (W (|AL| + |AR|)) for subroutine FUZZY- MERGE, and consequently O(W · N lg N ) for FUZZY-SORT. We will show in the experiment section that W is typically good enough at 50 or below, and this makes FUZZY-SORTa pretty realistic approach in terms of time and query complex- ity.

B. Philosophy behind the Algorithm

There are various ways to view our proposed algorithm under the preference-based LTR framework. Preference-based LTR has not yet gained much popularity as one of the many ranking schemes, mainly due to the instability on randomized approach and the unsatisfactory query complexity.

FUZZY-SORT can in a way be viewed as an attempt to trade some of the efficiency provided by a divide-and-conquer framework for the stability and performance provided by the deterministic heuristic, SORT-BY-DEGREE. The window size W is precisely the parameter that dictates the proportion of

mixture between the two standpoints. At W = 1, FUZZY- SORTis exactly MERGE-SORTin the raw; at W = N , FUZZY- SORT is simply SORT-BY-DEGREE.


This section will be roughly divided into two parts. In the first part we focus on the prediction stage optimization problem, i.e. where FUZZY-SORT stands among other pre- viously proposed methods to solve the min-FAST problem for preference-based ranking; this will mainly be done on an artificially generated network (of item preference rela- tion). In the second part we experiment and demonstrate the performance of FUZZY-SORT on real data sets, and provide comparison with other ranking algorithms, both preference- based and score-based.

For all of the following experiments and algorithms in- volved, we use pairwise ranking error as the evaluation mea- sure. All algorithms employed in the experiment also has pairwise ranking error as their optimization objectives.

A. Comparison of Prediction Schemes on Artificial Data We claimed through various perspective that FUZZY-SORT

is efficient in fully utilizing the allotted queries to produce good ranking, and we shall verify first under a more idealistic setting that it is indeed so.

We adopt the Bradley-Terry-Luce (BTL) model [30], [31]

for our artificial data generation. In the BTL model, each item is assumed to possess an (unknown) “true” score si that represents on a unified scale how high it should be ranked in a score-based model. Based on the score of items, the result of comparison between two items i and j is considered a Bernoulli variable with success probability related to the logistic of score difference, i.e. :

P [i ≺ j] = logistic (c · (si− sj)) = 1

1 + exp (c(sj− si)) Though somewhat idealistic (as it may not always be the case that pairwise preferences in real data follows from a given total order), in the case where we want to obtain a total ranking, BTL is a natural model to describe pairwise preference relations generated from such ranking.

Thus, we have the item scores randomly uniformly gener- ated over a designated interval, and use such scores as ground truth in the BTL model. To ensure the noise is “fixated” in the generated data, and precision of preference output does not get progressively better by querying a particular relation multiple times, the preference output corresponding to a particular pair of elements is fixed upon first query. The number of items is set to be 50000, while the logistic coefficient c is set so that c(smax− smin) = 0.82; In other words, the noise that arises for a single query is actually quite noticeable. The preference output given by oracle is the result of averaging a random number (no more than 15) of queries.

2As a matter of fact, the relative performance between different prediction algorithms was not greatly affected by the BTL model parameters. i.e. The plot stays similar even if we tweak the parameters, therefore we only show result under a fixed set of parameters here.


We compare FUZZY-SORT with several other counter- parts being FAS-PIVOT, averaged-MERGE-SORT3, a Markov chained based approach (RANK-CENTRALITY) by Negahban et al. [22], and the sort-by-degree heuristic in raw. Note the latter two algorithms query all quadratic pairs of preference relations before execution, while the previous three have performance that scale with the number of queries they are allotted to make. Therefore, To compare how each algorithm scales as they are given more “computational resource”, we let FUZZY-SORTtake the largest merge-window it could use under allotted amount of queries, while FAS-PIVOT and averaged-MERGE-SORT average over maximum number of iterations allowed.

Each algorithm is ran 10 times on the data set. The mean and standard deviation of the pairwise error is shown in Figure 2, plotted against number of queries used.

Fig. 2. Performance in pairwise error under fixed number of total query the accuracy of RANK-CENTRALITY (RC) and SORT-BY-DEGREE (DEG) are plotted as a dashed line as they require a quadratic amount (more than 109) of queries, out of the x-axis bound of the plot. The two dashed lines actually overlap with each other. Error bars are also plotted but are slightly too small to be distinguishable.

As can be seen in Figure 2, FUZZY-SORT is dominant when compared to FAS-PIVOTand averaged-MERGE-SORT. Not only does FUZZY-SORTconverges much quicker to good accuracy, the end of the curves also hint that at convergence FAS-PIVOT and average-MERGE-SORT is unlikely to reach as low a pairwise error as Fuzzy-Sort has, at least not before an excessive amount of extra iterations.

Compared with RANK-CENTRALITY and na¨ıve SORT-BY- DEGREEwhich both uses full information of the pairwise rela- tions (that is, more than 109queries), FUZZY-SORTcan reach comparable pairwise error in significantly less queries. Also

3 We perform MERGE-SORTby comparisons whose result is probabilisti- cally decided by item preference relations, and average the result of several separate MERGE-SORTas in FAS-PIVOT.

at convergence, we see FUZZY-SORTessentially reaches what RANK-CENTRALITYand SORT-BY-DEGREE can achieve.

Note that the information of running time is omitted here as all these methods above are implemented such that the time complexity is the basically proportional to the query complexity, with the slight exception of RANK-CENTRALITY

which has a constant factor for doing matrix transition several times.

B. Performance on Real Data

In this section we shall move to some real world data sets to compare our methods with fellow ranking algorithms.

Data Sets: We incorporate several data sets for testing. bike, cadata, cpusmall are originally real world regression data sets from UCI repository [32] taken as a single-query ranking data sets [33]–[35]. MQ2007, MQ2008 are LTR data sets from the

“Million Query Track” of TREC 2007 and TREC 2008 [36], [37], which consist of queries with preference label from 0 through 2. YahooLTR1, YahooLTR2 are large-scale LTR data sets taken from Yahoo! Learning to Rank Challenge [38], both containing queries with preference label from 0 through 4.

In summary, in viewing the results shown in Table II, it is worth keeping in mind that bike, cadata, cpusmall are data sets with long query lists and a wide range of relevance score; MQ2007, MQ2008 are data sets with pretty short query lists and a small range of preference labels; YahooLTR1, YahooLTR2are large-scale data sets with large amount of items and features. The detailed data statistics are shown in Table I.

Compared Methods: We compare several state-of-the-art score-based ranking algorithms against FUZZY-SORT, along with other preference-based methods.

The three score-based algorithms we shall include are Rank- SVM [7], RankBoost [6] and LambdaMART [15]. Being the winner of Yahoo! Learning to Rank Challenge in 2010, LambdaMART is arguably the most powerful score-based ranking algorithm now, capable at handling different kind of optimization objectives and has shown promising performance across numerous data sets. Rank-SVM is as well one of the representative score-based linear model proposed in as early as 2002, still constantly being compared with in many survey of ranking approaches. RankBoost is another ranking model with some history that stems from the idea of AdaBoost, but has rather a non-linear scoring function.

For all preference-based ranking algorithm, we perform very simple and effortless training to form the oracle. We do simple subsampling over all item-pairs with different preference label, among which a random forests [39] is trained as the binary classification model. The number of sampled edges is around 1 to 25 times the number of items, with the ratio being smaller for large data sets (e.g. 50000 for MQ2008, 200000 for YahooLTR1). Random forests are trained with 50 trees and a bagging ratio of 0.5.

Other ranker specifics are given below in more detail:

FUZZY-SORT: Models are trained with a window size of 50.

FAS-PIVOT: The result of the average from 50 iterations.


Averaged-MERGE-SORT: The result of the average from 50 iterations.

RANK-CENTRALITY: The distribution after 20 iterations is considered as output. For RANK-CENTRALITY algo- rithm, iterations beyond 20-th hardly affects the perfor- mance so 20 iterations are in fact very sufficient (in terms of convergence).

Rank-SVM: We use Joachim’s implementation in package SVMRank [7], with parameter C chosen from:

{0.01, 0.02, 0.05, 0.1, 0.2, 0.5, 1, 2, 5, 10, 20, 50, 100, 200, 500, 1000}.

RankBoost: We use the implementation in RankLib [40]

with a small tweak in the code to support optimization against pairwise ranking error.

LambdaMART: As with RankBoost we use the imple- mentation in RankLib with a manual tweak to sup- port pairwise ranking error.4 LambdaMART models are trained with 1000 trees and learning rate 0.1.

The result of the experiment is shown in Table II; entries that represent best statistics for a data set are marked in bold- face, where closeness are determined under one-tailed t-test at 5% significance level. The running time for each algorithm is shown in Table III. For preference-based algorithms, the time required for training stage where a random forest is build and the time required for prediction stage where the ranking is generated from the random forest are separately listed. For score-based algorithms, since prediction stage generally takes minimal time (several seconds to a minute top depending on algorithm), we simply omit the prediction entries of score- based algorithm in the table.

1) FUZZY-SORT versus other preference-based prediction schemes: As shown in Table II, FUZZY-SORTessentially out- perform FAS-PIVOT and averaged-MERGE-SORT in all data sets. RANK-CENTRALITY, based on the idea of random-walk of a Markov Chain, while requiring much more computational resource and shown to be as-good-as in our artificial exper- iments, does not really outshine FUZZY-SORT in real data sets. In most data sets FUZZY-SORTand RANK-CENTRALITY

has really close performance, with FUZZY-SORT seemingly winning slightly in general.

Factoring in the time efficiency (to be discussed) for each preference-based algorithm, it is probably safe to say FUZZY- SORT is the best choice of all preference-based algorithms tested.

2) FUZZY-SORT versus score-based ranking algorithms:

Compared against conventional score-based ones, FUZZY- SORT performs reasonably well and is largely better than Rank-SVM and RankBoost, while falling slightly behind LambdaMART in most data sets. LambdaMART is undeniably good across all data sets; at least with the random forest model we are using as the binary prediction model, preference-based methods does not beat LambdaMART on most data sets.

4We initially gave the gbm package [41] in R a try due to its supporting pairwise ranking error by default, but the implementation in gbm seems somewhat inferior to that of RankLib

However the pairwise accuracy deficit is not that big on most data sets, and the training time on larger data sets is heavily in favor of FUZZY-SORT.

3) Running time comparisons: From Table III, it is evi- dent that out of the four tested preference-based algorithms, FUZZY-SORToutperform all others in efficiency. Under con- ditions where a short amount of time could be allowed before generating the prediction, FUZZY-SORT proves to be a very good choice in the class of preference-based algorithms, able to generate prediction mostly in within a minute or two. Most importantly, contrary to what was believed that preference- based algorithms have terrible prediction stage efficiency, the prediction time of FUZZY-SORT actually comes pretty close to that of LambdaMART, meaning FUZZY-SORT is hardly impractical if LambdaMART is being used.

Due to the training model we choose, the training time for preference-based algorithms is directly proportional to the number of features in the data and the number of sampled pairs. Arguably the training time is not most satisfiable, but it could be tuned down when necessary (while sacrificing accuracy) and is generally consistent and controllable.

Out of all algorithms tested, score-based methods across the board show some difficulties dealing with longer query lists (cadata,YahooLTR1), as their computational complexity still mostly scale with the amount of pairs in given query lists. Rank-SVM appears to have the most robust training efficiency, having a fast training time even on larger data sets (YahooLTR1,YahooLTR2). LambdaMART on the other hand is much slower in general and quickly becomes painfully slow as the data size gets larger, as could be seen in ca- data,YahooLTR1,YahooLTR2.

One last thing to note though, is that for the training time for preference-based algorithms is currently decided by the time to train a random forest on sampled preference pairs.

So the training time listed in the table, while it is indeed the time we spent to train our preference-based oracle, is by no means tied with given preference-based algorithms.

It is always possible to choose a different model to train as an oracle, with different training efficiency and prediction performance. Here the method we choose is and is intended to be somewhat more simplistic, in part to demonstrate the possibility of choosing just choosing a simple model to train against given preference pairs in the preference-based learning framework.


For the past few years, there have been scattered works that endorsed preference-based LTR; most however focused on the theoretical side, and it would seem that preference-based LTR is a framework more of theoretical interest rather than practical use.

In this work we show otherwise by demonstrating preference-based LTR could achieve promising performance in ranking scenario. Most importantly, in FUZZY-SORT the existing prediction-stage efficiency issue is met with active- sampling done in a recursive fashion, thereby making the



Data Set #samples #features #query #relevance-level

bike 7380 16 1 594

cadata 10000 8 1 3279

cpusmall 4000 12 1 54

MQ2007 13652 46 1700 3

MQ2008 2874 46 800 3

Yahoo1 165660 700 6983 5

Yahoo2 103174 700 3798 5





bike .0748±.0001 .0907±.0009 .0862±.0003 .1028 .1756 .2486 .0586 cadata .2251±.0001 .2386±.0009 .2251±.0003 .2287 .1643 .2902 .2158 cpusmall .0939±.0004 .0972±.0004 .0981±.0003 .0920 .1050 .1657 .0775 MQ2007 .0889±.0004 .0902±.0009 .0910±.0006 .0886 .0932 .1532 .0887 MQ2008 .0553±.0008 .0568±.0018 .0562±.0018 .0576 .0562 .1400 .0652 Yahoo1 .1782±.0004 .1849±.0005 .1805±.0005 .1805 .2031 .2142 .1626 Yahoo2 .1497±.0002 .1545±.0003 .1522±.0003 .1476 .1587 .1544 .1409



Data Set preference-based Algorithms RSVM BOOST LMART Training FZS FASP MERGE RC

bike 210 110 294 312 1932 2460 2244 26

cadata 211 167 305 347 3181 14763 2556 11823

cpusmall 264 52 97 109 667 162 351 108

MQ2007 288 106 1330 1438 18 4 153 624

MQ2008 127 2 17 17 3 2 33 108

Yahoo1 3195 131 1183 1320 222 569 21611 86400*

Yahoo2 2831 60 691 619 122 376 675 15602

prediction-phase efficiency much more reliable even against long query lists.

Our experimental results hints the possibility of an easier training phase under preference-based LTR framework. The classification task on pairwise preference could be done with accuracy, also number of pairs needed to train a decent oracle is surprisingly small.

In summary our work shines in the context that pairwise information are more readily accessible, or a pointwise model is somewhat harder to train. One could consider the analogy in real life that ever so often comparing between two items (e.g.

which topic interests you more?) are easier than giving them an actual score (e.g. how interested are you toward this topic?).

Such results are also more likely to be consistent as pointwise estimations are inevitably prone to relative shift in score. For these reasons preference-based LTR could be the favorable choice in such situations, and FUZZY-SORTis a solid, practical way to carry out preference-based predictions.


The work arises from the Master’s thesis of the first au- thor [29]. We thank Profs. Yuh-Jye Lee, Shou-De Lin, the anonymous reviewers and the members of the NTU Compu- tational Learning Lab for valuable suggestions. This work is partially supported by the Ministry of Science and Technology of Taiwan via the grant MOST 103-2221-E-002-148-MY3.


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