SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
Novelty and diversity in information retrieval evaluation
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Visual diversification of image search results
Proceedings of the 18th international conference on World wide web
Generic and Spatial Approaches to Image Search Results Diversification
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
IEEE Transactions on Knowledge and Data Engineering
Efficient diversification of web search results
Proceedings of the VLDB Endowment
On query result diversification
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
Evaluation and user preference study on spatial diversity
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques
IEEE Transactions on Knowledge and Data Engineering
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
λ-Diverse Nearest Neighbors Browsing for Multidimensional Data
IEEE Transactions on Knowledge and Data Engineering
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To many location-based service applications that prefer diverse results, finding locations that are spatially diverse and close in proximity to a query point (e.g., the current location of a user) can be more useful than finding the k nearest neighbors/locations. In this paper, we investigate the problem of searching for the k Diverse-Near Neighbors (kDNNs)} in spatial space that is based upon the spatial diversity and proximity of candidate locations to the query point. While employing a conventional distance measure for proximity, we develop a new and intuitive diversity metric based upon the variance of the angles among the candidate locations with respect to the query point. Accordingly, we create a dynamic programming algorithm that finds the optimal kDNNs. Unfortunately, the dynamic programming algorithm, with a time complexity of O(kn3), incurs excessive computational cost. Therefore, we further propose two heuristic algorithms, namely, Distance-based Browsing (DistBrow) and Diversity-based Browsing (DivBrow) that provide high effectiveness while being efficient by exploring the search space prioritized upon the proximity to the query point and spatial diversity, respectively. Using real and synthetic datasets, we conduct a comprehensive performance evaluation. The results show that DistBrow and DivBrow have superior effectiveness compared to state-of-the-art algorithms while maintaining high efficiency.