Week 12 - Intelligent Information Retrieval - Muddiest Point

I do not have a muddiest point for this week.

Week 12 - Intelligent Information Retrieval - Reading Note

This week I read,

1. Susan Gauch, Mirco Speretta, Aravind Chandramouli and Alessandro Micarelli. User Profiles for Personalized Information Access. Chapter 2 in  Brusilovsky, P., Kobsa, A., Neidl, W. (eds.) (2007) The Adaptive Web: Methods and Strategies of Web Personalization. Lecture Notes in Computer Science, Vol. 4321. Springer-Verlag, Berlin Heidelberg New York.
2. Michael J. Pazzani and Daniel Billsus. Content-Based Recommendation Systems. Chapter 10 of  Brusilovsky, P., Kobsa, A., Neidl, W. (eds.) (2007) The Adaptive Web: Methods and Strategies of Web Personalization. Lecture Notes in Computer Science, Vol. 4321. Springer-Verlag, Berlin Heidelberg New York.
3. Ahn, J., Brusilovsky, P., He, D., Grady, J., and Li, Q.  (2008). Personalized web exploration with task models. In Proceeding of the 17th international Conference on World Wide Web (Beijing, China, April 21 - 25, 2008). WWW '08. ACM, New York, NY, 1-10. DOI= http://doi.acm.org/10.1145/1367497.1367499.
4. He, D., Brusiloviksy, P., Grady, J., Li, Q., and Ahn, J. (2007). How Up-to-date should it be? the Value of Instant Profiling and Adaptation in Information Filtering. In Proceedings of the IEEE/WIC/ACM international Conference on Web intelligence(November 02 - 05, 2007). Web  Intelligence. IEEE Computer Society, Washington, DC, 699-705. DOI= http://dx.doi.org/10.1109/WI.2007.135

From 1, user profiling is a way to narrow the scope of information avaliable based on some known information about the user. However, this requires the system to collect information about users, a potential privacy concern. Many people choose to opt out of these systems when given a chance.

From 2, content based recommendation systems are used to pair down the set of results to just the best to show a user. Think Netflix recommendations. Recommendations are usually driven from a user profile. 

Many techniques for making recommendations.

From 3, an alternate model for personalized search is task-based search. System attempts to gather information about the task, or information need, that the user is currently exploring over several searches. 

From 4, examines how often to refresh personalized results and user profiles. Real-time as a user is searching or just occasionally. Research shows that the real-time updates allow users to complete tasks faster. However, the speed comes at the cost of accuracy and user satisfaction.

Week 11 - Multilingual and Parallel Retrieval - Muddiest Point

I did not have a muddiest point this week.

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?