Autumn Web Information retrieval (Web IR) Handout #1:Web characteristics Ali Mohammad Zareh Bidoki ECE Department, Yazd University

Autumn Outline Web challenges SE & Web IR challenges Web Structure (Graph) Web characteristics Zip law

Autumn Web Challenges Huge size of information –11.5 billions pages (2005) –64 billions pages (05 June, 2008) Proliferation and dynamic nature –New pages are created at the rate of 8% per week –Only 20% of the current pages will be accessible after one year –New links are created at rate 25% per week Heterogeneous contents –HTML/Text/Audio/… Users of web are growing exponentially

Autumn What is the success reason of the Web? A distributed system A simple protocol Production and generation is very simple

Autumn Information Retrieval Definition IR deals with the representation, storage, organization of, and access to information items (relevant to user query) Information retrieval (IR) is the science of searching for documents, for information within documents, and for metadata about documents An information retrieval process begins when a user enters a query into the system. Queries are formal statements of information needs, for example search strings in web search engines. In information retrieval a query does not uniquely identify a single object in the collection. Instead, several objects may match the query, perhaps with different degrees of relevancy.

Autumn Web Retrieval User Space Information Space Matching Retrieval Browsing Index terms Full text Full text + Structure (e.g. hypertext) Search Engine Search engine is an IR system!

Autumn IR vs Data Retrieval A data retrieval aims at retrieving all objects which satisfy clearly defined conditions in regular expression DR does not solve the problem of retrieving information about subject or object

Autumn Comparing IR to databases ( vs data retrieval ) DatabasesIR Data StructuredUnstructured Fields Clear semantics (SSN, age) No fields (other than text) Queries Defined (relational algebra, SQL) Free text (“natural language”), Boolean Query specification CompleteIncomplete Matching Exact (results are always “correct”) Imprecise (need to measure effectiveness) Error response SensitiveInsensitive

Autumn Main points in IR What is the definition of relevancy? Evaluation! –Subjective (opposite to hardware, network)

Autumn Web IR (SE) Challenges (1) The definition of Relevancy The connectivity with content in Web –A huge graph Different type of Queries –Narrow Needle in a haystack –Wide Overlapping with many areas User have Poor patience: they commonly browse through the first ten results (i.e. one screen) hoping to find there the “right” document for their query

Autumn Web IR (SE) Challenges (2) Spamming phenomenon –it is crucial for business sites to be ranked highly by the major search engines. –There are quite a few companies who sell this kind of expertise (also known as “search engine optimization”) and actively research ranking algorithms and heuristics of search engines, and know how many keywords to place (and where) in a Web page so as to improve the page’s ranking –SEO Books Content & Connectivity Spamming Anti Spamming solutions

Autumn Web IR (SE) Challenges (3) Rich-get-richer problem –It takes a long time for a young high quality web pages to receive an appropriate quality –Unfairness –Bad directions in growing web contents

Autumn Web IR (SE) Challenges (4) Crawling challenges –Huge size of information with dynamic nature –Freshness & converge Google covers only 70% of the Web –An suitable scheduling policy –Hidden web (600 times bigger) Using meta search engines to increase coverage –Merging and ranking problem

Autumn Web IR (SE) Challenges (5) User evaluation is subjective and changes in time –Relevancy between a query and document depends on user and time –Two users with the same query expect different results

Autumn Web IR (SE) Challenges (6) Query Ambiguity –Python –Car & automobile

Autumn Web Dynamics For each page p and each visit, the following information is available: –The access time-stamp of the page: visitp. –The last-modified time-stamp (given by mostWeb servers; about 80%-90%of the requests in practice): modifiedp. –The text of the page, which can be compared to an older copy to detect changes, especially if modifiedp –is not provided. –The following information can be estimated if the re-visiting period is short: –The time at which the page first appeared: createdp. –The time at which the page was no longer reachable: deletedp In all cases, the results are only an estimation of the actual values

Autumn Estimating freshness and age The probability that a copy of p is up-to- date at time t, u p (t) decreases with time if the page is not re-visited. When page changes are modeled as a Poisson process, if t units of time have passed since the last visit, then:

Autumn Characterization of Web page changes Age: visitp-modifiedp. Lifespan: deletedp-createdp. Number of changes during the lifespan: changesp. Average change interval: lifespanp/changesp.

Autumn Freshness && Age

Autumn

Autumn Web a Scale Free Network A scale-free network is characterized by a few highly-linked nodes that act as “hubs” connecting several nodes to the network. It follows Power Law

Autumn Random Vs Scale-Free

Autumn Distribution of Web Graph: Power- Law

Autumn Power-Law and Zipf Law

Autumn Zipf Law for Content

Autumn Macroscopic Structure of Web

Autumn User Sessions User sessions on the Web are usually characterized through models of random surfers The most used source for data about the browsing activities of users are the access log files of Web Servers, Proxies, SEs –Caching Modeling User behavior Eye tracking

Autumn Next Lecture Information Retrieval Models –Boolean –Vector Space –Realistic