We were discussing the personal recommender yesterday. The recommender has access to more data sources than conventional web applications and can perform more correlation than ever before. when integrated with social engineering application such as Facebook or Twitter the recommender finds information about the friends of the owner. places that they have visited or activities that they have posted become relevant to the current context for the owner. In this case, the recommender super imposes what others have shared with the owner. The context may be a place or activity to be used in the search query to pull data from these social engineering applications. This is not  intrusive to others and does not raise privacy issues. Similarly it does not instigate movements or flash mob because the will to act on the analysis still rests with the owner. The level of information made available by the social engineering applications is a setting in that application and independent from the recommender. there are no HIPAA violations and whether a user shared his or her visit to a pharmacy or hospital is entirely up to the user. It does provide valuable insight to the owner of the recommender when she decides to find a doctor.
The recommender does not have to take any actions. whether the owner chooses to act on the recommendations or publish it on Facebook is entirely her choice. this feedback loop may be appealing to her friends and their social engineering application but it is an opt in.
The recommender is a personal advisor who can use intelligent correlation algorithms. For example, it can perform collaborative filtering using the owner's friends as data point. In this technique, the algorithm finds people with tastes similar to the owner and evaluates a list to determine the collective rank of items. While standard collaborative filtering uses viewpoints from a distributed data set, the recommender may adapt it to include only the owner's friends.
The recommender might get geographical location of the user, the time of the day and search terms from the owner. These are helpful to predict the activity the owner may take. The recommender does not need to rebuild the activity log for the owner but it can perform correlations for the window it operates on. If it helps to build the activity log for year to date, then the correlation can become easier by translating to queries and data mining over the activity log.