Week 7 - Relevance Feedback and Query Expansion - Reading Note

I read:

1. Christopher D. Manning, Prabhakar Raghavan, Hinrich Schutze, “Introduction to Information Retrieval”. Cambridge University Press. 2008. Chapter 9.
2. Xu, J. and Croft, W. B. (2000). Improving the effectiveness of information retrieval with local context analysis. ACM Trans. Inf. Syst.18, 1 (Jan. 2000), 79-112. (DOI= http://doi.acm.org/10.1145/333135.333138)
3. Wang, X., Fang, H., and Zhai, C. (2008). A study of methods for negative relevance feedback. In Proceedings of the 31st Annual international ACM SIGIR Conference on Research and Development in information Retrieval (Singapore, Singapore, July 20 - 24, 2008). SIGIR '08. ACM, New York, NY, 219-226. (DOI= http://doi.acm.org/10.1145/1390334.1390374)
4. Donna Harman, (1992). Relevance feedback revisited. Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval. Pages: 1 - 10. Copenhagen, Denmark. 1992. (http://doi.acm.org/10.1145/133160.133167)

From 1:
I have never seen a search engine with a relevance feedback system. I wonder if there are any practical examples? ... Oh, ok Excite used it primarily as a way to link people to more results like a certain result.

The weakness of the Rocchio algorithm is that it requires relevant documents to cluster into a single group. Any factor that causes the vector-space model to breakdown or cause relevant documents for form multiple clusters decrease the effectiveness of this relevance feedback algorithm.

Query reformulation, especially for technical queries is a way to make the user's query more likely to return relevant results.

From 3:
It is more difficult to use negative relevance feedback to improve results than positive relevance feedback. However, negative feedback works better in the language model than a vector-space model. Most helpful for very difficult queries with poor results.

From 4:
They found that query expansion should be used with relevance reweighting with most of the gains coming from query expansion.

Week 6 - Evaluation - Muddiest Point

Please cover the differences between DCG and NDCG again.

Week 5 - Matching Models: Probabilistic and Language Model - Reading Note

I read:
Christopher D. Manning, Prabhakar Raghavan, Hinrich Schutze, “Introduction to Information Retrieval”. Cambridge University Press. 2008. Chapters 11 & 12.

Djoerd Hiemstra and Arjen de Vries. (2000) Relating the New Language Models of Information Retrieval to the Traditional Retrieval Models. Technical Report, TR-CTIT-00-09, Centre for Telematics and Information Technology

From IIR Chapter 11 & 12, what did I just read? Maybe my brain is not working today but I only was able to form the highest level of understanding. Maybe the chapters were too dense with equations. I'm hoping that the article will make more sense.

From Hiemstra and de Vries (2000), "[Vector space] is based on the similarity between query and document, the [probabilistic model] is based on the probability of relevance, using the distribution of terms over relevant and non-relevant documents."

Week 4 - Matching Models: Ranked Boolean and Vector Space - Muddiest Point

My muddiest point for this week is cosine similarity.

Week 4 - Matching Models: Ranked Boolean and Vector Space - Reading Note

I read:
Christopher D. Manning, Prabhakar Raghavan, Hinrich Schutze, “Introduction to Information Retrieval”. Cambridge University Press. 2008. Sections 1.3 and 1.4 & Chapter 6.

From 1.3 & 1.4, boolean retrieval vs ranked retrieval: user preference generally relates to experience. Power users usually find complex boolean queries preferable to ranked retrieval queries.

From Chapter 6:
Approaches to determining result quality:

• Parametric and Zone indexes: basic ranking and metadata filtering
• Term Weighting - higher score the more times a term appears in a doc
• vector space scoring

Vector Space Modeling:

• represent documents and queries as vectors in a common vector space. This allows scoring, document classification, document clustering
• many specific schemes and methods

I think that I will need the lecture before I truly understand all of this material.

Question:
Why are vectors the best way to model this?  

Week 3 - Index Construction and Compression - Muddiest Point

I do not have muddiest point for this week.