Profile Diversity for Query Processing using User Recommendations ?
We propose a specific scoring function for content and profile diversification using a probabilistic model. We propose a greedy threshold-based top-k algorithm to process queries using our profile diversity score using the concept of candidate list. We propose various techniques for optimizing the computation of top-k diversified profiles. To evaluate the benefits of our scoring function and optimization techniques, we ran our algorithms using three datasets: two from Del.icio.us and one from Flickr. The results show that our approach increases the overall quality of recommendations and that our optimizing strategies reduce significantly the response time of the diversified top-k computation
diversity top-k search and recommendation