The following paragraphs introduce the recommendation service system and indicate its shortcomings firstly. Second, we will probe into the related researches about Interactive Genetic Algorithm (IGA) and list the crucial points when using this method. Finally, we will explain why construct this system by adopting agents to satisfy the users’ preference and describe its advantages.
2.1 Recommendation system
The recommendation system recommends the data items that users may be interested in based on users’ predefined preference or user’s access history. Various items have been considered in these recommendation systems, such as music[1-3], WebPages[4-6], movies[7, 8], and books[9].
There are two major approaches of the personalized recommendation system.
One is the content-based filtering, which analyzes the content of items that the user preferred in the past and recommends the similar items. In other words, this approach recommends according to the connection of users’ preference and the content of items.
In this approach, the representation of data items and the records of users’ preference
are key issues to affect the function of the recommendation system.[10] However, the recommendation systems adopting the content-based filtering approach can only recommend the data items in which the user has indicated his/her interest. Other potential interesting data items cannot be explored in such recommendation systems if the users never access before.[11]
Different from the previous approach, the collaborative filtering approach makes the recommendation by grouping the users who have the same interests and sharing what they access in common. Broadly speaking, the main goal of the collaborative approach is to make the recommendation among the users in the same group. The recommending approach has a high possibility to recommend surprising items by the nature of information sharing, which cannot be achieved by the content-based filtering approach. However, the bootstrapping of this approach may sometimes be hard and take a long time. [1, 12]
2.2 Interactive Genetic Algorithm
In 1975, John Holland referred the mechanism about the evolution of the Nature and proposed genetic algorithm (GA), an artificial intelligent system invented for the optimal solution of the problem. Under the construction of GA, the chromosome structure of individuals will be designed according to the problem, and the genes of the chromosome will be generated randomly when the system initializes. The agents evaluate the individual’s performance to the unsolved problem by a fitness function and decide which one should be preserved or discarded in next run. The discarded ones will be replaced with new individuals whose genes are got from the preserved ones.
procedures of evolution until the optimal solution of the problem is figured out.
However, if we would like to solve the problem about Art by GA, such as
appreciating music or paintings, it is hardly to define an effective and clear fitness function which can substitute human beings’ subjective judgment. This kind of
problem which needs human beings’ subjective judgment is not only limited in art but in engineering and education, like database retrieval and writing education.[13, 14]
Interactive Genetic Algorithm(IGA) is an optimization method that adopts GA among system optimization based on human evolutionary[15]. In other words, it is simply a GA technique whose fitness function is replaced by a human user.
Because of users’ participation, IGA has more limitations than GA. The main factor affecting the evaluation of IGA is human beings’ emotion and fatigue. When processing the evaluation of each run, the users cannot make the fair judgment;
therefore, the result will be changed in the different occasion due to the people’s
emotion. Furthermore, people will feel tired and fail to process with large population.
Therefore, how to search for a goal with a smaller population size within a fewer number of searching generations is the important problem. Another problem is fluctuation of human evaluation which would result in the inconsistency of different generation. [16-18]
2.3 The user preference model
No matter what kinds of method the personalized recommendation system, the key point is how to adapt the system to the users’ preference. According to the
previous research[19], the users’ preference model can be constructed in two approach as follows[20, 21]:
(1) Implicit - Observing the user’s behavior (Machine-learning) or inferring from
domain knowledge or other user information (Knowledge-engineered)。
(2) Explicit- Using survey, dialog or any other methods to obtain the user knowledge directly (User-programmed).
Normally, the recommendation systems always belong to the 1st approach. Via analyzing the behavior of the user, like the access history or the category which the user feel more interested in, the system will construct the user preference model automatically and then will make the recommendation based on the user model. In this approach, the users will be unconscious that some software programs are gathering the information when operating the system.
In this study, the user needs to train a group of agents actively to be the
intermediary between human beings and data items. Obviously our system belongs to the second approach of the user modeling. Compared with the existing
recommendation systems, our system spends more time on training the agents in the beginning, but we can adapt the systems to the user’ preference in shorter time, and don’t need to waste time searching and collecting the users’ information in the accessed history. Furthermore, from the viewpoint of the interaction between human beings and the system, the users directly and actively adapt the system to his/her preference, resulting in our model is also more effective to satisfy the users than the above- mentioned indirect methods.