Project : axis
Section: Scientific Foundations
Keywords : recommender system, personalization, collaborative filtering, user support Web, hypermedia, user profile, KDD, CBR.
Adaptive Recommender Systems
The objective of a recommender system is to help system users to make their choices in a field where they have little information for sorting and evaluating the possible alternatives [78][76][74].
A recommender system can be divided into three basic entities (e.g. figure 3): the group of recommendations producer agents, the module of recommendation computation and the group of recommendations consumers.
A major challenge in the field of recommender systems design is the following:
How to produce adaptive recommendations of high quality
minimizing the effort of producers and the consumers?
Two main complementary approaches are proposed in the literature: 1) approaches based on the content and the machine learning of user profiles and 2) approaches known as a collaborative filtering based on data mining techniques. The user profile is a structure of data that describes user's centers of interests in the space of the objects which can be recommended. The user profile is a structure built in the first approach or specified by the user in the second approach.
The user profile is used either to filter available objects (content based filtering), or to recommend to a user something that satisfied previous users with a similar profile (collaborative filtering) [76].
In the Axis project-team, we continue the development of a hybrid approach of calculation of recommendations based on the analysis of visited content and centered on data mining, where the past behaviours of a user group are used to calculate the recommendations (collaborative filtering).
The vast majority of approaches based on data mining are mainly statistical approaches where the order of occurrence of events in the history is not taken into account for the calculation of recommendations. Here are some first examples in the field of navigation assistance on the Web: the FootPrints system [80] and the system of Yan et al [81].
The implementation difficulties of our approach relate to the following aspects:
providing techniques of identification and extraction of relevant behaviours (i.e. of the learning behaviours or case behaviours) starting from raw data of past behaviours,
defining methods and measurement techniques of similarities between behaviours,
defining inference techniques of adaptive recommendations starting from the identified relevant past behaviours (or starting from the reminded cases).
We explore all three problems above by using case-based reasoning (CBR) techniques and more generally KDD techniques.
We study the class of recommender systems, based on the re-use of a user group's past experiences, using case based reasoning techniques (CBR). We focus on two types of recommender systems:
systems where the calculation of recommendations is based on the re-use of experiences of a users group that search for information on an hypertext information system like the Web or on an Internet/intranet site. These systems aim at an adaptive assistance to the search for information activity ;
systems where the calculation of recommendations is based on the re-use of past experiences of experts, in order to provide an assistance to design process.
