Abstract:

Traditional collaborative filtering methods calculate users’ similarity to find the nearest neighbors without distinguishing their attributions, and the recommendation seems to be inefficient. A concept lattice based users’ interests prediction algorithm was presented as follows: first, formal context about the user-navigation was extracted from the user access logs, and the concept lattice was built from it; second, an appropriate sliding window was used to limit the user's current access content, thus could identify the user's current independent preferences; at last,the recommendation utilities of documents were calculated according to the independently preferences, and the weighted sum was used to get the personal preferences reflect by all the current interests to predict users’ interests. This method analyzed the issue of classification between documents in the traditional methods,thus users’ preferences could be effectively identified and divided, which coincide in characteristics that users were similar only in certain aspects, but not all the features of interests. Experiment on real log data proved the effectiveness in resource recommendation, and the cold start problem in the traditional collaborative filtering methods could be smoothed.
 

Key words: collaborative filtering, concept lattice, personalized recommendation, decision making theory