程序代写代做代考 algorithm information retrieval distributed system COMP6714 Lecture 2
COMP6714 Lecture 2
Information Retrieval and Search Engines
Lecturer: Wei Wang Date:
1 Preliminaries
Before studying the MaxScore algorithm, it is beneficial to review and have a deeper understanding of the
vanilla DAAD query processing algorithm. In particular, find out the similarities and differences between
the DAAD algorithm and the algorithm to process the disjunctive Boolean query (i.e., if the query is A B
C, the disjunctive query is A or B or C).
The DAAD algorithm, at a high-level, does two things:
1. Candidate generation: it gets a set of candidate documents, which is technically the union of all
query keywords’ inverted lists, and
2. Soring: for each candidate, it computes its score.
To further improve the algorithm, we need to reduce the potentially huge amount of candidates generated
by the DAAD algorithm. Consider the following example: the query is A B C, and we denote the documents
in A’s inverted list as CA. The candidates generated by the DAAD algorithm is S1 := CA ∪CB ∪CC . Now,
can we reduce it to, say, S2 := CA ∪ CB?1
Notice that the only set of candidates we will miss by using S2 instead of S1 is CC (CA∪CB), or in other
words, those documents that only contain C. What is the maximum possible score for these documents? It
is at most idf (C) · maxd∈C2{tf (d, C)}. If even this score is no larger than the currently found k-th highest
score, then we can safely use S2 instead of S1.
2 MaxScore
2.1 Description
For every term t, we can quickly compute the maxscore from precomputed information stored in its postings
list L. E.g., in VSM, it is just idf (t) ·maxdi∈L{tf (di, t)}.2
Without loss of generality, we assume that we have already rearranged the query terms in the decreasing
order of their maxscores.3
The basic idea of the maxscore algorithm is based on the following observations:
• If we know a document does not appear in a set of terms’ posting lists, its maximum possible score is
the sum of the maxscores of the rest of the terms.
• Let τ ′ be the minimum score of the currently top-k scoring documents, we do not need to process a
document whose maximum possible score is no larger than τ ′.
1It is important to note that we still allow the algorithm to access the inverted list of C in the scoring phrase.
2Find out which part is precomputed.
3Think why we make such an assumption?
2-1
Hence, the maxscore algorithm optimizes the basic DAAT algorithm by trying to gradually remove the
last query term (possibly repeatedly) from the so-called required term set.4 Only documents in the postings
lists of required term set are used to drive the DAAT algorithm. Other postings lists are only used to score
a document (via skipping).
We show the pseudo code of the algorithm in Algorithms 1 to 4.
Algorithm 1: MaxScore({L1, L2, . . . , Lm}, k)
Description: The main difference from the standard DAAT algorithm is that the algorithm is driven only by
postings in RTLists.
Data: Postings lists Lis are ordered by their maxscore in decreasing order
1 Initialize min-heaps H and topk ; /* weights are docID and score, respectively. We push k negative
values into topk initially. */;
2 RTLists ← {L1, L2, . . . , Lm};
3 PTLists ← ∅;
4 forall Li do
5 H .push(Li.curPosting(), Li.curPosting().docID); Li.next(); /* curent posting contains all the
necessary info for scoring plus the list id. */;
6 while H.isEmpty() 6= true do
/* score another doc */
7 (score, docID)← calcScore(H,PTLists);
/* update the top-k heap and the top-k bottom score */
8 topk .push(docID , score);
9 topk .pop();
10 τ ′ ← topk .peep().score;
/* update RTLists and PTLists based on τ ′ */
11 update(τ ′,RTLists,PTLists, H);
Algorithm 2: calcScore(H,PTLists)
Description: Collect all postings of docID in H into info, and call calcScore2() to compute the final score for
docID .
1 Initialize info;
2 (info, docID)← H.pop();
3 i← info.listID ;
4 H.push(Li.curPosting(), Li.curPosting().docID); Li.next();
5 while H.peep().docID = docID do
6 (tempInfo, docID)← H.pop();
7 info.add(tempInfo);
8 i← tempInfo.listID ;
9 H.push(Li.curPosting(), Li.curPosting().docID); Li.next();
10 score ← calcScore2(docID , info,PTLists);
Note that we just illustrate a naive version of the pseudo-code which illustrates the main ideas. In actual
implementation, there are many optimizations that must be added. For example, one can maintain the last
τ ′ value and avoid calling the update function if τ ′ didn’t change. The partitioning of all m lists into RTLists
and PTLists can be done incrementally.
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Algorithm 3: calcScore2(docID , info,PTLists)
Description: Collect possible postings of docID by seeking on PTLists, and then compute the final score.
1 forall Li ∈ PTLists do
2 id ← Li.skipTo(docID);
3 if id = docID then
4 info.add(Li.curPosting();
5 s← compute score of docID based on info;
Algorithm 4: update(τ ′,RTLists,PTLists, H)
Description: Update RTLists and PTLists, and also remove items in H if it belongs to lists being moved to
PTLists.
1 upperBound ← 0;
2 for i = m to 1 do
3 upperBound ← upperBound + Li.maxscore;
4 if upperBound ≥ τ ′ then
5 break;
6 RTLists ← {L1, . . . , Li};
7 PTLists ← {Li+1, . . . , Lm};
8 Remove items in H that came from a list now in PTLists;
2.2 A Running Example
Consider the example in Table 1. We make many simplifying assumptions, including that the score contri-
bution of a term is just its tf , and the final score of a document is the sum of scores from all the query terms
it contains. We highlight the major event in each iteration of the algorithm below.
1. Initially, RTList is all the three lists.
2. 1st iteration: H gives D1, and we collect all its postings from H, and calculate its score as 2 + 1 = 3.
Hence, τ ′ = −1, and there is no need to update RTList .
3. 2nd iteration: H gives D2, and we collect all its postings from H, and calculate its score as 8 + 1 = 9.
Hence, τ ′ = 3. We shrink RTList to {A,B}.
4. 3rd iteration: H gives D4 (not D3), and we collect its postings from H, as well as finding its postings
in PTLists, and calculate its score as 2 + 4 + 1 = 7. Hence, τ ′ = 7. We shrink RTList to {A}.
5. 4th iteration: H becomes empty and we stop, and the final top-2 results are: (D2, 9) and (D4, 7).
2.3 Cost Analysis
The worst case complexity of the algorithm is the same as without the maxscore optimization. However,
as we can see from the running example, if we are able to obtain k documents with high scores (they do
not necessarily need to be the final results), the algorithm can be very efficient by scoring fewer number of
documents and making use of the skipping capability of postings lists.
[Strohman et al., 2005] reports that a basic maxscore algorithm improves query time of a baseline DAAT
algorithm by 40% and it scores only about 50% of the documents.
4We denote their postings lists as RTLists in the algorithms.
2-3
term maxscore postings
A 8 (D1 : 2), (D2 : 8), (D4 : 2)
B 4 (D1 : 1), (D4 : 4), (D10 : 1), (D11 : 4), . . .
C 2 (D2 : 1), (D3 : 2), (D4 : 1), (D10 : 2), (D11 : 2), . . .
Table 1: A Running Example (k = 2 and Each Posting only Contains (docID : tf ))
2.4 Bibliography Notes
We quote from [Shan et al., 2012]:
The original description of the MaxScore [Turtle and Flood, 1995] strategy does not contain
enough details, and it is different from a later implementation by Strohman [Strohman et al., 2005].
Jonassen and Bratsberg [Jonassen and Bratsberg, 2011] presented a more detailed MaxScore al-
gorithm which combines the advantage of both Strohman’s and Turtle’s implementations.
Our description of the maxscore algorithm is a simplified version without much optimization.
Another way to make use of the per list maxscore information is the WAND approach [Broder et al., 2003,
Tonellotto et al., 2010].
[Shan et al., 2012] demonstrates that the new block-max index can work with both maxscore and WAND
algorithms to further speed up query processing.
[Fontoura et al., 2011] contains a fairly recent survey of major query processing algorithms under both
DAAT and TAAT approaches.
[Strohman and Croft, 2007] also studies efficient query evaluation for memory-resident indexes.
References
[Broder et al., 2003] Broder, A. Z., Carmel, D., Herscovici, M., Soffer, A., and Zien, J. Y. (2003). Efficient
query evaluation using a two-level retrieval process. In CIKM, pages 426–434.
[Fontoura et al., 2011] Fontoura, M., Josifovski, V., Liu, J., Venkatesan, S., Zhu, X., and Zien, J. Y. (2011).
Evaluation strategies for top-k queries over memory-resident inverted indexes. PVLDB, 4(12):1213–1224.
[Jonassen and Bratsberg, 2011] Jonassen, S. and Bratsberg, S. E. (2011). Efficient compressed inverted
index skipping for disjunctive text-queries. In ECIR, pages 530–542.
[Shan et al., 2012] Shan, D., Ding, S., He, J., Yan, H., and Li, X. (2012). Optimized top-k processing with
global page scores on block-max indexes. In WSDM, pages 423–432.
[Strohman and Croft, 2007] Strohman, T. and Croft, W. B. (2007). Efficient document retrieval in main
memory. In SIGIR, pages 175–182.
[Strohman et al., 2005] Strohman, T., Turtle, H. R., and Croft, W. B. (2005). Optimization strategies for
complex queries. In SIGIR, pages 219–225.
[Tonellotto et al., 2010] Tonellotto, N., Macdonald, C., and Ounis, I. (2010). Efficient dynamic pruning with
proximity support. Large-scale Distributed Systems for Information Retrieval.
[Turtle and Flood, 1995] Turtle, H. R. and Flood, J. (1995). Query evaluation: Strategies and optimizations.
Inf. Process. Manage., 31(6):831–850.
4
Lecture 2 – Information Retrieval and Search Engines
Preliminaries
MaxScore
Description
A Running Example
Cost Analysis
Bibliography Notes
