Search continues to present many interesting problems, some focusing on new parameters, some others rewiring existing parameters, and a few others shielding these parameters from adversaries (e.g. spam). Through a blog post, Scott Huffman shares how Google evaluates improvements.

..but we are constantly evaluating everything, which can include:
- proposed improvements to segmentation of Chinese queries
- new approaches to fight spam
- techniques for improving how we handle compound Swedish words
- changes to how we handle links and anchortext
- and everything in between

Though spam and the web graph feature prominently, it is interesting to note how "internationalization" features in many of these evaluation examples, reflecting Google’s overall push in this direction. Evaluation is through click-through improvements, and statistically sound relevance metrics.

Evaluation metrics, I think, is one of those areas where academia can greatly influence and impact search. This is one of the more theory centric problems in search, not limited by the lack of large information retrieval data sets. Indeed, the recently concluded SIGIR conference featured many papers in this direction.

Score Standardization for Inter-Collection Comparison of Retrieval Systems [PDF]
W. Webber, A. Moffat and J. Zobel  (University of Melbourne)

The Good and the Bad System: Does the Test Collection Predict Users’ Effectiveness? [PDF]
A. Al-Maskari, M. Sanderson and P. Clough  (University of Sheffield)

Retrieval Sensitivity Under Training Using Different Measures [blog]
B. He, C. Macdonald and I. Ounis  (University of Glasgow)

Evaluation Over Thousands of Queries [PDF]
B. Carterette, V. Pavlu, E. Kanoulas, J. Allan, and J. A. Aslam  (University of Massachusetts Amherst/Northeastern University)

Novelty and Diversity in Information Retrieval Evaluation [PDF]
C. Clarke, M. Kolla, G. Cormack, O. Vechtomova, A. Ashkan, S. Büttcher, and I. MacKinnon  (University of Waterloo)

Relevance Assessment: Are Judges Exchangeable and Does it Matter [PDF]
P. Bailey, N. Craswell, I. Soboroff, P. Thomas, A. de Vries and E. Yilmaz  (NIST/Northeastern University/Microsoft/CWI/CSIRO ICT Centre)

Intuition-Supporting Visualization of User’s Performance Based on Explicit Negative Higher-Order Relevance [link]
H. Keskustalo, K. Jarvelin, A. Pirkola and J. Kekalainen  (University of Tampere)

Elsewhere, comments on the article  –

Seo Dialect:

The rest of the points are things we’ve been hearing from Google for a long time. We know they’re progressing on universal and personalization search efforts, all in their famous intent to create the best user experience.

Webtribution:

Anyone remotely involved in SEO or digital marketing should always take advantage of any information / insight Google opens to the public.

SearchEngineCaffe:

One of my biggest issues with TREC and similar environments is the single focus on relevance … for example, a spam post that is relevant to a topic would be acceptable, even if you would never want to read it in real life. It’s time we move beyond the basics and find ways to tackle the more challenging retrieval quality aspects…

Also, at webmasterworld.

For readers interested in the overall problem of IR evaluation, a paper by Kalervo & Jaana on "IR evaluation methods for retrieving highly relevant documents" offers an excellent introduction.