Effective Diversification for Ambiguous Queries in Social Image Retrieval

Recent years have witnessed a great popularity of social photos sharing websites, which host a tremendous volume of digital images accompanied by their associated tags. Thus, extensive research efforts have been dedicated to tag-based social image search which enables users to formulate their queries using tags. However, tag queries are often ambiguous and typically short. Search results diversification approach is the common solution which aims to increase the number of satisfied users using only a single results set that cover the maximum of query aspects. However, not all queries are uniformly ambiguous and hence different diversification strategies might be suggested. In such context, we propose a new ranking process which dynamically predicts an effective trade-off between the relevance and diversity based results ranking according to the ambiguity level of a given query. Thorough experiments using 12 ambiguous queries over the NUS-WIDE dataset show the effectiveness of our approach over classical uniform diversification approaches.