This class studies large-scale systems and applications on the World Wide Web. We will be studying topics around the architecture and functionality of modern search engines (e.g., Google). For example, collection and refreshing of Web pages (Web crawling), indexing, querying and retrieval, Hidden Web, result ranking, spam detection on the Web. We will also study topics around the organization and functionality of Social Networks (e.g., Facebook, Twitter, LinkedIn). For example: analysis and prediction of social graphs, community detetion, information diffusion in social networks, recommendations. The course involves presentations and study of different research topics as well as hands-on projects on these topics.

Spring semester 2024

Class: Wed 10:00-13:00

Instructor: Alexandros Ntoulas
antoulas -*at*- di -*dot*- uoa +*dot*+ gr

• 04/03: Class begins on Wednesday March 06. Please join eclass to see announcements.

Unless there's a name next to a topic it's not taken. Please email the instructor if you are interested in picking one of the topics for your term project.

• Tik Tok crawler: Write a crawler for downloading info from Tik Tok.
• Focused crawler: Implement a focused crawler that collects web pages of a given topic (e.g. cars).
  
• Link Ranking: Implement a set of link-based ranking algorithms (e.g. PageRank, HITS, SALSA) and compare them over a set of Web pages.
• Product specs: Implement a crawler that creates a database of product specs by crawling information from the web for comparison shopping.
• Location-aware Advertising Platform: Create a platform to support location-aware advertising
• Hotspot logger: Implement a mouse movement logger for a web page and overlay the hotspots on top of the content.
• Recipes: Create a crawler for recipes and create a search engine for recipes based on the ingredients in someone's fridge. Improvement: identify the fridge ingredients from a picture of the fridge.
• DEH: Implement a portal where people can compare all prices from different power suppliers and pricing policies (green/blue etc.) Collect historical data and make predictions on future prices.
• Trigger alerts: Implement a system for watching specific web sites and alerting the user on changes.
  

The course material that we will study in this class is mostly coming from literature and research papers from conferences such as the WWW, WSDM, WISE, VLDB, SIGMOD, Big Data, etc. The papers that we will go through are available in the schedule table below.

For more information and a more elaborate description of each topic you can also study the following books. However, these are not a requirement for the class:

• Introduction to Information Retrieval, Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, Cambridge University Press. 2008.
• Search Engines Information Retrieval in Practice, W. Bruce Croft, Donald Metzler, Trevor Strohman, Pearson, 2009.
• Networks, Crowds, and Markets: Reasoning About a Highly Connected World, David Easley and Jon Kleinberg, Cambridge University Press, 2010.