There has been much research published on personalization and recommendation agents regarding customer preference modeling (Schafer, Konstan et al. 2001) as they have become more prevalent in the Internet. Currently there are three main streams of research dependent on how customer preferences are modeled: vector similarity (collaborative and content-based filtering), probability (Bayesian), and association (correlation between user and item). The shortcomings of these researches are that they have agents for clusters of people, i.e. recommendations to a specific user based on the preferences of many different users (such as Movielens.umn.edu and Amazon.com), rather than tailor to the needs of an individual user. So far, the agent has always been on the server side (e.g. an online store) rather on the client side. As a consequence, these agents are not always acting for the better good of the customer. The retail agents are trying to convince the customers to buy more products than might be necessary for him. Furthermore, these agents have little insight on your real preferences, since they can only observe actions on their site related to their competence. We envision advocate agents, residing on the client side, which always supports and promotes the best interests of its master.
These advocate agents shall not just "simply" facilitate product or merchant search, they "get to know their master," meaning the human decision maker through initial profiling (sex, age, nationality, preferences, etc) and by observing the behavior of the decision maker while visiting an online retail site (time spent, links clicked, dates visited, website visited before, etc). Based on this information agents should recommend actions (buy this product, buy from this merchant, read this article, etc), and execute autonomously some of the decisions the consumer delegated to it (make a dentist appointment, buy airline ticket, bid on eBay.com, etc). In addition to reducing information overload customers that use software recommendation agents reduce effort, and improve the effectiveness and efficiency of their decision making (Haeubl and Trifts 2000). These agents will represent the preferences of the consumer as his or her highly tailored "software copy" in the Internet. An integral part of the advocate agents is the learning component which allows them to improve their performance from experience over time. The user's reaction of an advocate agent suggestion is recorded for long term behavioral analysis and fed back in real time to the agents as well.
We hypothesize that over time a human decision maker will delegate more and more decisions to an advocate agent. There is a switching point for the adaptation of new technologies by the individual users, and it is usually based on how smoothly the new technology integrates in daily life and how useful it is. In the first phase of this project we envision a non-intrusive proactive agent which will work according to the user preferences and gives suggestions even if it has no immediate assignment. Examples of pro-activeness include looking for new trends, latest news, informing about interesting ongoing auctions, such as eBay.com, good deals, etc.