Suggests which stocks to purchase based on an intelligent analysis/algorithms. This includes determining the probability of stock prices rising or falling, sensing current market trends/economy, digging data from a repository of previous successful buy ins, ability to customize it based on current needs. May involve some other non-trivial computations for arriving at decisions.

Given some past traces of your game playing history, this project would involve giving some CBR system suggestions while you are playing a new game. See D2 (http://home.cc.gatech.edu/ccl/3)

The idea is to use pairs of <document, summary> as case base for summarizing a new document given as query. The summarization can be thought of as evaluating the similarity in structure/domain/relationship to existing cases, then using the most similar case's summary to identify the focus points of the query document.

Given your social profiles (such as on Facebook, delicious, or LinkedIn) and search context, this project would involve generating matches of similar profiles with case based explanation/justification.

Given a picture as a query, this application will use a case base of pictures (using flickr api or google images api) and extract 'similar' pictures based on feature similarity. (Tags: Image Recognition, classification or clustering)

All the latest Web 2.0/Semantic Web stuff is heavily manifested in the music domain! Worth taking a look here: http://dbtune.org/ (and the RDF dump links - http://dbtune.org/jamendo/#dump). Also worth looking at the music ontology project - http://musicontology.com/
that drives the Pandora music site.

This project could involve creation of a song recommendation application that used a music ontology at the core for reasoning and driving the CBR system.

Identify the author of a piece of text/computer program based on statistical analysis of previous works. See http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.26.6652 or http://portal.acm.org/citation.cfm?id=100385 for articles on identifying authors of programs. For general stories/novels we could use N-gram analysis or Markov chains to identify authors of texts

The main goal is to assist architects and engineers to design more sustainable buildings. Sustainable building depends on the tuning of various features such as location, spatial configuration, solar orientation, shapes, size, construction materials, main activities, energy type, etc. For that purpose case retrieval will be based on extensive data sets provided by Energy Information Administration, which is a government department responsible for generating official energy statistics. Case adaptation is planned to be done within CAD environments using parametric modeling techniques.

http://www.netflixprize.com//rules

We have papers with CBR-based A* where you retrieve and re-use old A* plans. What hasn't been addressed in the existing literature is the use of CBR in high-dimensional motion planning cases, where standard approaches are computationally intensive and there is a big incentive to retrieve and re-use old plans. I propose that we approach this problem using a humanoid planning example (where the configuration space is huge due to the large number of joints) and developer a CBR-based rapidly exploring random tree (RRT)

The search engine would make use of the user's search history and past preferences and user characteristics to refine and personalize search retreivals

Anonymous Microsoft Web Data Database: (can be found here)
ftp://ftp.ics.uci.edu/pub/machine-learning-databases/anonymous

The data is created by sampling and processing the www.microsoft.com logs.The data records the use of www.microsoft.com by 38000 anonymous, randomly-selected users. For each user, the data lists all the areas of the web site that user visited in a one week timeframe.

One can auto suggest links to user based on his history and what page he is on at that moment.

Each case is a profile of a past user whose experience is precisely stored like GRE ,TOEFL,Past History,Papers etc. And the system would recommend a university and specialization to the new user based on the past cases and it adapts to the new user from the retrieved cases in suggesting a new solution which might be a combination of profile evaluation criteria of the new user with that of various other users.

There can also be evaluation criteria like specific specialisation in a particular area and related work going on in the universities..