Week 11 - Multilingual and Parallel Retrieval - Reading Note

This week I read:

1. Oard, D.W. and Diekema, A.R.  (1998)  Cross-Language Information Retrieval. Annual Review of Information Science and Technology, 33. 223-256.
2. Daqing He, Jianqiang Wang.  (2008)  Cross-Language Information Retrieval. In Ayse Goker, John Davies, Margaret Graham (eds) Information Retrieval, Wiley Publisher.
3. IES chapter 14 parallel information retrieval http://www.ir.uwaterloo.ca/book/14-parallel-information-retrieval.pdf

I was unable to obtain a copy of 1 online.
I was unable to obtain a copy of 2 online.

From 3, query processing can be parallelized.

• index partitioning - document vs. term partitioning - term partitioning allows for greater scalability by allowing the index to stay in memory - hybrid approach partitions by document and then by term
• replication - helps provide reliability and fault tolerance in addition to scaling benefits

MapReduce can be used to parallelize other tasks such as index building, link analysis, etc. MapReduce is able to handle massive collections of processors and huge data sets. Maps and Reduces can be sharded into smaller sets.

Week 10 - Web Information Retrieval - Muddiest Point

Why is HITS more susceptible to spamming than PageRank?

Week 10 - Web Information Retrieval - Reading Note

This week I read:
1. Christopher D. Manning, Prabhakar Raghavan, Hinrich Schutze, “Introduction to Information Retrieval”. Cambridge University Press. 2008. Chapters 19 and 21
2. J. Kleinberg. (1998) Authoritative sources in a hyperlinked environment. Proc. 9th ACM-SIAM Symposium on Discrete Algorithms, 1998. http://www.cs.cornell.edu/home/kleinber/auth.pdf
3. S. Brin, L. Page: (1998) The Anatomy of a Large-Scale Hypertextual Web Search Engine. WWW7 / Computer Networks 30(1-7): 107-117 (1998) http://dbpubs.stanford.edu:8090/pub/1998-8

• I like the term adversarial information retrieval.
• I did not realize that search engines used anchor text ... but it makes sense.
• It was interesting to read the paper from Google's founders but I imagine that Google has changed dramatically since those early days.

Week 9 - User Interaction and Visualization - Muddiest Point

Are there any any examples of mobile interfaces that provide document surrogates?

Week 9 - User Interaction and Visualization - Reading Note

This week I read:
1. Ricardo Baeza-Yates, Berthier Riberiro-Neto, “Modern Information Retrieval”, Addison Wesley, 1999. ISBN-10: 020139829X. chapter 10 (available at http://people.ischool.berkeley.edu/~hearst/irbook/)
2. Marti A. Hearst. Ch. 1: The Design Of Search User Interfaces. Search User Interfaces. (available at http://searchuserinterfaces.com/book/sui_ch1_design.html)
3. Marti A. Hearst. Ch. 11: Information Visualization For Text Analysis. Search User Interfaces. (available at http://searchuserinterfaces.com/book/sui_ch11_text_analysis_visualization.html)

According to Hearst, the simple user interfaces of search engines have not changed much since 1997 because search is a means to an end, but mentally challenging and so needs to be free of distraction, and it must make sense to all users. Web search engines must be simpler than historical search systems because the user base is so much broader. She goes on to enumerate many design aspects to consider when designing an interface. Hearst also covers Information Visualization from the perspective of text analysis (text mining).

Baeza-Yates & Riberiro-Neto, present an overview of the information retrieval interaction process and how the user interface must support all of those steps in the process. Many graphical examples are presented. It is surprising how the search interface has not drastically changed over the last 15 years.

Week 7 - Relevance Feedback and Query Expansion - Muddiest Point

My muddiest point for this week is: How are the original query vector, positive feedback, negative feedback back weights determined?