ommendations. A more detailed love this fii Chek it an description of the system can be found in[8] If you we already seen It tell ne what you think Evolutionary algorithms are inspired by natural evolution. Populations of solutions are maintained, these are evaluated to find out how well each solution solves the problem. Fitness ★★★★ scores are given to each solution(where lower fitness values THE KOWARENAI dwcd by Kyok mean the solution is better )."Parents"are then selected from the population based on fitness- the fitter the solution, the ore likely it is to become a parent. Child solutions are enerated from the parents, employing crossover and mutation operators to ensure children resemble their parents with minor variations. The children replace the current population, the solutions are evaluated, parents are picked, and so on, for a YOUR OPINION number of generations. Evolution causes the solutions to mprove until they satisfy the current problem [7] O Love it O Not sure O Hate it This system uses an evolutionary algorithm in a slightly unusual way. Firstly, evaluation involves feedback from the users-as Figure 2: Lifestyle Recommender System Avatar interface they judge the quality of recommendations, their input is used help derive fitness scores for corresponding solutions in the 4. SUMMARY population. Secondly, initial solutions are not random as is As electronic commerce and on-line entertainment internet sites typical in such algorithms (a random matching function would multiply, and as the number of novice users of the internet be of little use in a recommender system). Thirdly, open-ended is a evolution is used -the system never trying to evolve software advisors. As described in this paper, recommender newer and better ways of matching systems provide such a solution for users. By employing how the evolutionary profile matcher is used within the system techniques such as evolutionary search and a 3D avatar, the Lifestyle Recommender System outlined here increases the 5. ACKNOWLEDGMENTS Thanks to Philip Treleaven for his guidance and Akira Sato for Matched profiles implementing the Avatar web interface. This work is funded by a scholarship provided by the Thai go 6. REFERENCES 1] Schafer, J B, Konstan, J. A and Riedl, J. January 2001.E- Commerce Recommendation Applications. Journal of Data Figure I Block diagram of the Lifestyle Recommender System [2] Schafer, J B, Konstan, J and Riedl, J Recommender Systems in E-Commerce 3.2 Consumer interaction the acm 1999 Conference on electron As we have seen mender systems can bring added value [3] Breese, I.S., Heckerman, D and Kadie, C.1998.Empirical to e-commerce websites by being able to understand the users analysis of predic references and needs. One possible way to take this further is to introduce a"to enhance the way in which both In Proceedings of the algorithms for collaborative filtering 14th Conference on Uncertainty in items can be recommended to, and feedback received from the Artificial Intelligence, pp 43-52 customer. This character or avatar would be a visual [4] Goldberg, K, Roeder, T, Gupta, D and Perkins, C representative of the website with which customers can interact. August 2000. Eigentaste: A Constant Time Collaborative The rationale behind this is that a user would find it easier to Filtering Algorithm. UCB ERL Technical Report M00/41 relate to another person or character than a standard web interface. There is then the possibility of a relationship being 5 Terveen, L and Hill, W. 2001. Beyond Recommender Systems: Helping People Help Each Other. In HCl In The established between user and avatar. From the point of view New Millenium, Carroll, J. ed. Addison-Wesley increased use of the website and allow more preference data to [6] Herlocker, J.L., Konstan, J. A. and riedl, J. 2000 be captured which would consequently lead to more accurate Explaining Collaborative Filtering Recommendations Proceedings of the ACM 2000 Conference on Computer upported Cooperative Work. The lifestyle recommender system will employ the use of a 3D avatar to interact with the user. Experiments will be conducted [7 Bentley, P J. 2001. Creative Evolutionary Systems to validate the assumption that users would participate more Morgan Kaufman Pub with the avatar. Figure 2 shows an early version of the avatar to [8] Ujin, S. 2001. An Adaptive Lifestyle Recommender be used for the lifestyle recommender system. System Using a Genetic Algorithm. Submitted to the Computation Conference 2001(GECCO 2001)generates personalised recommendations. A more detailed description of the system can be found in [8]. Evolutionary algorithms are inspired by natural evolution. Populations of solutions are maintained, these are evaluated to find out how well each solution solves the problem. Fitness scores are given to each solution (where lower fitness values mean the solution is better). “Parents” are then selected from the population based on fitness – the fitter the solution, the more likely it is to become a parent. Child solutions are generated from the parents, employing crossover and mutation operators to ensure children resemble their parents with minor variations. The children replace the current population, the solutions are evaluated, parents are picked, and so on, for a number of generations. Evolution causes the solutions to improve until they satisfy the current problem. [7] This system uses an evolutionary algorithm in a slightly unusual way. Firstly, evaluation involves feedback from the users – as they judge the quality of recommendations, their input is used to help derive fitness scores for corresponding solutions in the population. Secondly, initial solutions are not random as is typical in such algorithms (a random matching function would be of little use in a recommender system). Thirdly, open-ended evolution is used – the system never stops trying to evolve newer and better ways of matching profiles. Figure 1 shows how the evolutionary profile matcher is used within the system. Evolutionary Profile Profile Generator Matcher Best Feature Extractor Matched Profiles Profile Database Person Avatar Web Interface Generate/ Update/ Feedback Figure 1 Block diagram of the Lifestyle Recommender System. 3.2 Consumer Interaction As we have seen, recommender systems can bring added value to e-commerce websites by being able to understand the users' preferences and needs. One possible way to take this further is to introduce a “character” to enhance the way in which both items can be recommended to, and feedback received from the customer. This character or avatar would be a visual representative of the website with which customers can interact. The rationale behind this is that a user would find it easier to relate to another person or character than a standard web interface. There is then the possibility of a relationship being established between user and avatar. From the point of view of the website owner, such a relationship would encourage increased use of the website and allow more preference data to be captured which would consequently lead to more accurate recommendations. The lifestyle recommender system will employ the use of a 3D avatar to interact with the user. Experiments will be conducted to validate the assumption that users would participate more with the avatar. Figure 2 shows an early version of the avatar to be used for the lifestyle recommender system. Figure 2: Lifestyle Recommender System Avatar interface 4. SUMMARY As electronic commerce and on-line entertainment internet sites multiply, and as the number of novice users of the internet increases, there is a greater need for tailored and friendly software advisors. As described in this paper, recommender systems provide such a solution for users. By employing techniques such as evolutionary search and a 3D avatar, the Lifestyle Recommender System outlined here increases the capabilities of such systems further. 5. ACKNOWLEDGMENTS Thanks to Philip Treleaven for his guidance and Akira Sato for implementing the Avatar web interface. This work is funded by a scholarship provided by the Thai Government. 6. REFERENCES [1] Schafer, J.B., Konstan, J. A. and Riedl, J. January 2001. E￾Commerce Recommendation Applications. Journal of Data Mining and Knowledge Discovery. [2] Schafer, J.B., Konstan, J. and Riedl, J. 1999. Recommender Systems in E-Commerce. Proceedings of the ACM 1999 Conference on Electronic Commerce. [3] Breese, J.S., Heckerman, D. and Kadie, C. 1998. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, pp. 43-52. [4] Goldberg, K., Roeder, T., Gupta, D. and Perkins, C. August 2000. Eigentaste: A Constant Time Collaborative Filtering Algorithm. UCB ERL Technical Report M00/41 [5] Terveen, L. and Hill, W. 2001. Beyond Recommender Systems: Helping People Help Each Other. In HCI In The New Millenium, Carroll, J. ed. Addison-Wesley. [6] Herlocker, J.L., Konstan, J. A. and Riedl, J. 2000. Explaining Collaborative Filtering Recommendations. Proceedings of the ACM 2000 Conference on Computer Supported Cooperative Work. [7] Bentley, P. J. 2001. Creative Evolutionary Systems. Morgan Kaufman Pub. [8] Ujjin, S. 2001. An Adaptive Lifestyle Recommender System Using a Genetic Algorithm. Submitted to the Graduate Student Workshop, Genetic and Evolutionary Computation Conference 2001 (GECCO 2001)