By Bo Long, Yi Chang
In undeniable, basic language, and utilizing distinct examples to give an explanation for the major techniques, types, and algorithms in vertical seek score, Relevance Ranking for Vertical seek Engines teaches readers the right way to manage score algorithms to accomplish greater leads to real-world functions.
This reference ebook for professionals covers suggestions and theories from the elemental to the complex, comparable to relevance, question goal, location-based relevance rating, and cross-property rating. It covers the newest advancements in vertical seek rating purposes, similar to freshness-based relevance thought for brand new seek purposes, location-based relevance thought for neighborhood seek purposes, and cross-property rating thought for functions regarding a number of verticals.
- Foreword through Ron Brachman, leader Scientist and Head, Yahoo! Labs
- Introduces score algorithms and teaches readers the best way to control rating algorithms for the simplest results
- Covers suggestions and theories from the basic to the advanced
- Discusses the cutting-edge: improvement of theories and practices in vertical seek rating applications
- Includes particular examples, case experiences and real-world situations
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Additional resources for Relevance Ranking for Vertical Search Engines
However, the randomized nature of the Minhash generation method requires further checks to increase the chances of uncovering all pairs of related articles and removing articles that were brought together by chance. Thus, we resort to LSH to reduce chance pairings. Prior to performing LSH, the Minhash signatures can also be quickly used to detect exact duplicates. 3 Duplicate Detection Given each article and its 100-length Minhash signature, we use these signatures to identify articles that are duplicates of each other.
4 Scatter plot of CTR versus JRFL’s prediction. (a) Object function value update. (b) Pairwise error rate (PER) update. (c) Query weight α Q update. preference pairs into two sets, one with 90,000 pairs for training and the rest with 60,000 for testing. 0 (we also tried other settings for this parameter; smaller C would render us fewer iterations to converge, but the tendency of convergency is the same), relative convergency bound to be 10−5 , and maximum iteration step S to be 50 in the coordinate descent algorithm.
As a result, we collected only the top four URLs from this random bucket. In addition, we also asked editors to annotate the relevance and freshness in the August 9, 2011, query log immediately one day after, according to the editorial guidance given by Dong et al. . Simple preprocessing is applied on these click datasets: (1) filtering out the sessions without clicks, since they are useless for either training or testing in our experiments; (2) discarding the URLs whose publication time is after the query’s issuing time (caused by errors from news sources); and (3) discarding sessions with fewer than two URLs.