Adaptive Information System

• Adaptive Navigation Support

The last part of this chapter captured my interests. In this part, the future work of adaptive navigation support for Virtual Environments was envisioned. I am also expecting the whole theory of adaptive navigation support to be applied in this domain. This actually reminds me that a video, recommended by Prof. Brusilovsky on facebook, talking about their educational virtual reality, Virtual Trillium Trial v2.0. The purpose of this Virtual Trillium Trial (www.virtualtrilliumtrail.com) is to allow you to freely go and explore, and discover, an infinitely beautiful, expansive, natural world. The Virtual Trillium Trail is a game that is both fun and beautiful, and ideal for young children. Where you can go and explore as long as you like. Where you are free to go off-trail and into the woods to learn about any of the flowers, plants, and trees that spark your curiosity. However I noticed that since the space in the virtual reality is actually very large, and there are about 20 concepts, 20 plants and about 1,000 facts, for young children, how to immediately find what they are interested in is a crucial question. As we are dealing with a very special group with their unique habits and preferences, we need to find a way to collect their interests and based on which provide their personal navigation support. Like any other adaptive educational system, for example GuizeGuide, once the user grabs the concepts of certain level correctly, there is no need to go back to some of the concepts that are the prerequisites. Once the user in the virtual reality has seen some plants. He can quickly skip that part and move on to next interesting concept. This is one application of adaptive system in this domain, we have many more to be explored with virtual reality. It is an absolutely promising area. I am looking forward to participating and seeing how research goes.

• Social navigation

Might it be a problem if social navigation suggests all users with similar profiles or interests come to the same node or link? In case where all nodes in the hypermedia space associated corresponding human knowledge, will it reduce the diversity of human knowledge in a general view because knowledge has been acquired would be accessed again and again while knowledge has not been familiar with cloud would be isolated more. At the mean time, each single user was getting the same type of information all the time by adaptive system, will this be a problem if the user has been kind of addicted this type of interest, however which might not be a good idea for his own personal development? Some logic and philosophy questions are remained to answered for the further development of adaptive information system.

• Collaborative filtering

Collaborative Filtering (CF) is some how luxury recommendation mechanisms for most online communities that endure cold-start problems, because there aren’t enough items or user ratings for the algorithm use. But as the community grows, the system could soon benefit from this mechanism. This reminds me the conference navigator 3, where the function that enables the user to rate each recommended presentation is added to the system. However I didn’t see any moves go beyond collecting the ratings. This might be an interesting way to improve the current recommendation approaches.

• Case based recommendation

This chapter reminds me the paper I have read titled as “Visualization for the Masses: Learning from the Experts”, in which the author presents an innovative application of case-based recommender system that is designed to suggest visualization of complex datasets uploaded from users. The system described in the paper is an online browser based visualization tool named ManyEyes developed by the IBM Research and the IBM Cognos software group. This related to the core idea of case based recommendation-“The users would like the similar one that they liked before.” By this approach, the system assumes structured item information with a well defined set of features and feature values. Information in the system are represented as a case and the system recommends the cases that are most similar to a user’s preference. Nice paper, and worth to read about.

Adaptive museum from Yi-ling Lin is a very cool project. I like this idea and how this idea was implemented into an adaptive recommendation system. When I looked at the type of general recommendation target, I thought there is one more potential point that may be we can improve. When we compare the real physical museum, there is one situation that you come with friends or a group to enjoy the museum tour, you talk and discuss the master piece on the wall with your friends, and this is a great pleasure where you can share your idea and communicate with your friends immediately. So museum, in this way, has become a good hang-out place for certain type of people. While, this kind of people also would like to go on internet some time and it would be great to enjoy the museum even online with their friend, which will eventually be a potential improvement for the system in order to enhance the user experience. We could create the interface, to enable inviting friends to tour the museum together and online chatting within our system as well. Also we could apply group recommendation for the users.

• Hybrid Web Recommendation System

Hybrid web recommendation system is one approach that combines multiple recommendation techniques in order to fully utilizes the available data, as well as to achieve some synergy between them, and thus to improve or produce better recommendation sets to users.

Currently I am exploring the use of mixed hybridization and weighted hybridization method in conference navigator system. Hopefully we could work out and build up this approach for the new users in the next conference to have better presentations recommended