Presented by Li-Tal Mashiach Learning to Rank: A Machine Learning Approach to Static Ranking Algorithms for Large Data Sets Student Symposium Speaker: Li-Tal Mashiach

©Li-Tal Mashiach, Technion, References  Learning to Rank Using Gradient Descent  Learning to Rank Using Gradient Descent ICML, 2005, Burges et al  Beyond PageRank: Machine Learning for Static Ranking  Beyond PageRank: Machine Learning for Static Ranking WWW 2006, Brill et al

©Li-Tal Mashiach, Technion, Today’s topics  Motivation & Introduction  RankNet  fRank  Discussion  Future Work suggestion Predict Popularity Rank (PP-Rank)

©Li-Tal Mashiach, Technion, Motivation  The Web is growing exponentially in size  The number of incorrect, spamming, and malicious sites is also growing static ranking  Having a good static ranking is crucially important PageRank  Recent works showed that PageRank may not perform any better than other simple measure on certain tasks

©Li-Tal Mashiach, Technion, Motivation – Cont.  Combination of many features is more accurate than one feature PageRank is only link structure feature  It is harder for malicious users to manipulate the ranking in case of machine learning approach

©Li-Tal Mashiach, Technion, Introduction  Neural networks  Training Cost function Gradient Descent

©Li-Tal Mashiach, Technion, Neural Networks Like the brain, neural network is a massively parallel collection of small and simple processing units where the interconnections form a large part of the network's intelligence.

©Li-Tal Mashiach, Technion, Training neural network The task is similar to teaching a student  First, show him some examples  After that, ask him to solve some problems  Finally, correct him, and start the whole process again Hopefully, he’ll get it right after a couple of rounds

©Li-Tal Mashiach, Technion, Training neural network – cont.  Cost function  Cost function – Error function to minimize Sum squared error Cross entropy  Gradient Descent take the derivative of the cost function with respect to the network parameters change those parameters in a gradient- related direction

©Li-Tal Mashiach, Technion, Static ranking as a Classification problem  x i represents a set of features of a Web page i  y i is a rank  The classification problem  The classification problem - learn the function that maps all pages’ features to their rank  But all we really care about is the order of the pages

©Li-Tal Mashiach, Technion, RankNet order of objects values assigned to them  Optimize the order of objects, rather than the values assigned to them  RankNet is given Collection of pairs of items Z={ } Target probabilities that Web page i is to be ranked higher than j  RankNet learns the order of the items  Using probabilistic cost function (cross entropy) for training

©Li-Tal Mashiach, Technion, fRank  Uses RankNet to learn the static ranking function  Training according to human judgments query For each query, rating is assigned manually to a number of results The rating measures how relevant the result is for the query

©Li-Tal Mashiach, Technion, fRank – Cont.  Uses set of features from each page: PageRank PageRank Popularity Popularity – number of visits Anchor text and inlinks Anchor text and inlinks – total amount of text in links, number of unique words, etc. Page Page – number of words, frequency of the most common term, etc. Domain Domain – various averages across all pages in the domain – PageRank, number of outlinks, etc.

©Li-Tal Mashiach, Technion, fRank Results  fRank performs significantly better than PageRank  Page and Popularity feature sets were the most significant contributors  By collecting more popularity data, fRank performance continues to improve

©Li-Tal Mashiach, Technion, Discussion  The training for static ranking cannot be depend on queries Using human judgments for static ranking (?)  PageRank advantages protecting from spams  fRank is not useful for directing the crawl

©Li-Tal Mashiach, Technion, Future work – PP-Rank  Training the machine to predict popularity of Web Page  Using popularity data for training Amount of visits Amount of visits How long users stay in the page How long users stay in the page Did they leave by clicking back Did they leave by clicking back …  should be normalized to the pattern of each user

©Li-Tal Mashiach, Technion, PP-Rank - Advantages  Can predict popularity of pages that were just created (no page points to them yet)  Can be a measure for directing the crawler  The rank will be not according to what web masters find interesting (PageRank), but according to what users find interesting

©Li-Tal Mashiach, Technion, Summary  Ranking is the key to search engine  Learning-based approach for static ranking is a promising new field RankNet fRank PP-Rank

©Li-Tal Mashiach, Technion, ANY QUESTIONS?