Assignment 7 (Optional) Task: Topic-Oriented PageRank


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Web Search and Sense-Making
Assignment 7 (Optional)

Task: Topic-Oriented PageRank
In this assignment, we will implement the Topic-Oriented PageRank algorithm on the Wikipedia
100GB free disk space in your machine.
Write a TopicPageRank.scala file to calculate and sort the topic-oriented pagerank scores for
the English Wikipedia pages. The topic we select for this homework is “The Football League
Players”. The steps are the following:
1. Read in the output files of your Assignment 8, which contains the link graph in Wikipedia.
The format should be:
• One page per line
• In each line, you have the title of a page and a list of the titles of the outlinks in the page
• Each outline title is inside [[]], and separated by a tab “\t”.
• The title and the list of links is separated by a delimiter. We recommended “\t”.
2. Read in the titles of the pages that are on the topic of “The Football League Players” from
the provided file.
3. Implement the Topic-Oriented PageRank algorithm. Assign a jump probability of 100% to the
pages on a topic and assign the other pages 0%. The formula looks like:

where T is the set of pages that are on the topic. Alpha is a page.
COSC 589 – Web Search and Sense-Making
The algorithm looks like:
val links = // Load RDD of (page title, outlinks) pairs
var ranks = // Load RDD of (page title, rank) pairs
val topicPages = bTopicPages.value // get RDD of (page title) from a broadcast variable
for (i <- 0 to ITERATION) {
val contribs = links.join(ranks).flatMap {
case (title, (links, rank)) = = (dest, rank / links.size))
onTopicRank = contribs.reduceByKey( _+_ ).filter( x =
topicPages.contains(x._1)).mapValues(0.15 + 0.85 * _ )
offTopicRank = contribs.reduceByKey( _+_ ).filter( x = !
topicPages.contains(x._1)).mapValues(0.85 * _ )
rank = onTopicRank.union(offTopicRank)
4. Calculate the topic-oriented pagerank scores for all the pages in the English Wikipedia. Use
5. Sort the topic-oriented pagerank scores for the pages in the descending order.
6. Save the results. The format is like:
[[Association football]] 7.557400770279294
[[association football]] 6.509902882920945
[[Midfielder]] 5.322665101195386
[[FA Cup]] 5.275953121181659
[[Football League Cup]] 5.1405548413603395
[[Defender (association football)]] 4.781431725368732
[[Premier League]] 4.460411498158475
[[Football League Championship]] 4.138733764918339
[[Football League One]] 4.120904050031456
[[Football League Two]] 3.8326805491447398
[[Football League Trophy]] 3.689606029687729
[[Forward (association football)]] 3.245154610067562
6. You are welcome to use the following code template:
import scala.util.matching.Regex
import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.SparkContext._
import org.apache.spark.rdd.RDD
import org.apache.spark.broadcast.Broadcast
object TopicPageRank {
COSC 589 – Web Search and Sense-Making
def main(args: Array[String]) {
val sparkConf = new SparkConf().setAppName(“TopicPageRank”)
val sc = new SparkContext(sparkConf)
// load link graphs
val input = sc.textFile(“./linkgraph/*.gz”) // your output directory from the assignment 7
val links = // Load RDD of (page title, links) pairs
val ranks = // Load RDD of (page title, rank) pairs
// load topic pages
val football = sc.textFile(“./football/part*”).map(r = “[[” + r + “]]”).toArray
// create a broadcast variable to hold the football pages
val bTopicPages = sc.broadcast(football.toSet)

// Implement your Topic-Oriented PageRank algorithm
val ITERATION = 10

// Sort pages by their PageRank scores
ranks.sortBy …
// save the page title and pagerank scores in compressed format (save your disk
space). Using “\t” as the delimiter. = r._1 + “\t” + r._2).saveAsTextFile(“./topicpageranks”,
What to Submit:
– Your code
– Screen captures of the beginning of your pageranked pages, in descending order (e.g. the
first 20 lines on the screen. Hint: Use ‘gunzip part-00000.gz” to unzip, then view the
documents and screen capture)
What NOT to Submit:
– Your input or output files
Where to submit:
– Canvas

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