By Tie-Yan Liu

Due to the quick progress of the internet and the problems find wanted details, effective and potent details retrieval structures became extra vital than ever, and the hunt engine has develop into an important device for lots of people.

The ranker, a significant part in each seek engine, is answerable for the matching among processed queries and listed files. due to its principal position, nice realization has been paid to the learn and improvement of score applied sciences. additionally, rating can be pivotal for plenty of different info retrieval purposes, resembling collaborative filtering, definition score, query answering, multimedia retrieval, textual content summarization, and on-line commercial. Leveraging laptop studying applied sciences within the rating technique has ended in cutting edge and better rating versions, and finally to a totally new examine sector referred to as “learning to rank”.

Liu first provides a accomplished evaluate of the most important techniques to studying to rank. for every strategy he provides the fundamental framework, with instance algorithms, and he discusses its benefits and downsides. He maintains with a few contemporary advances in studying to rank that can not be easily labeled into the 3 significant ways – those comprise relational score, query-dependent rating, move score, and semisupervised score. His presentation is done through numerous examples that follow those applied sciences to unravel genuine details retrieval difficulties, and via theoretical discussions on promises for score performance.

This booklet is written for researchers and graduate scholars in either info retrieval and computing device studying. they are going to locate the following the one entire description of the state-of-the-art in a box that has pushed the new advances in seek engine development.

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**Additional info for Learning to Rank for Information Retrieval**

**Example text**

7) Ideally in the above methods, one requires bk (k = 0, . . , bk−1 ≤ bk . However, in practice, since there are no clear constraints on the thresholds in the optimization problem, the learning process cannot always guarantee this. To tackle the problem, Chu and Keerthi [26] proposed adding explicit or implicit constraints on the thresholds to the optimization problem. The explicit constraint simply takes the form of bk−1 ≤ bk , while the implicit constraint uses redundant training examples to guarantee the ordinal relationship among thresholds.

On the other hand, when all the pairs are correctly ranked, we can get the minimum loss of zero. , the Hedge algorithm, is used to learn the parameters in hypothesis h. Note that h is actually a preference function, which cannot directly output the ranked list of the documents. In this case, an additional step is needed to convert the pairwise preference between any two documents to the total order of all the documents. To this end, one needs to ﬁnd the ranked list π, which has the largest agreement with the pairwise preferences.

ListMLE is also based on the Luce model. For each query q, with the permutation probability distribution deﬁned with the output of the scoring function, it uses the negative log likelihood of the ground truth permutation as the listwise ranking loss. We denote this new listwise ranking loss as the likelihood loss for short. L(f ; x, πy ) = − log P (πy | ϕ(f (w, x))). 13) It is clear that in this way the training complexity can be greatly reduced as compared to ListNet, since one only needs to compute the 276 The Listwise Approach probability of a single permutation πy but not all the permutations.