User profiling could be a basic element of any personalization applications. Most existing user profiling strategies are primarily based on objects that users are curious about (i.e., positive preferences), but not the objects that users dislike (i.e., negative preferences). In this paper, we have a tendency to specialise in search engine personalization and develop several concept-primarily based user profiling methods that are primarily based on each positive and negative preferences. We evaluate the proposed strategies against our previously proposed personalised query clustering technique. Experimental results show that profiles which capture and utilize each of the user’s positive and negative preferences perform the simplest. An important result from the experiments is that profiles with negative preferences will increase the separation between similar and dissimilar queries. The separation provides a clear threshold for an agglomerative clustering algorithm to terminate and improve the quality of the ensuing query clusters.
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