Dominance by Stability: A Framework for Top k Dominating Query on Incomplete Data

The top-k dominating (TKD) query is a method for discovering the most prominent k items from a large dataset. According to the top-k dominance rule, an item is dominant over another if and only if it outweighs the latter in each and every dimension. For example, when a user conducts a search for the best hotels in a city, the most dominant query will provide the top k hotels according to the user’s preferences. The customer can reserve a room at any of the recommended hotels and enjoy the best services available. However, even among the best hotels, opinions might differ widely. User has no way of knowing if this is a better option or not. Because the reviews may be fake or self-made. It would be easy to give many positive fake reviews. The top k dominant query can’t handle this kind of situation. Moreover, criteria of traditional top k dominating queries generate more than k records for a top k dominating query. The problem is magnified by the available dataset when it is incomplete. To solve this problem, we present a new technique for determining the top k dominating queries which is called top k dominance by stability. The stability of each data point is determined, and then the k most stable data points are taken for ranking. The stability of a rating can be found by using the standard deviation. Ratings for a product are more stable and reliable if they clusters around the mean. It is demonstrated through an evaluation that the performance of the proposed technique provides superior results for top-k dominant queries comparing to other methods.