Durable reverse top-k queries on time-varying preference

Recently, a query, called reverse top-k query, is proposed. The reverse top-k query takes an object as input and retrieves the users whose top-k query results include the object while the top-k query retrieves the top-k matching objects based on the user preference. In business analysis, reverse top-k queries are crucial for evaluating product impact and potential market. However, the reverse top-k query assumes that user’s preference is static. In practice, user preference may change with moods, seasons, economic conditions or other reasons. To overcome this disadvantage, this paper proposes a new reverse top-k query, named as durable reverse top-k query, without limitation of user’s preference being static. The durable reverse top-k query retrieves users who put a given object in the top-k favorite objects most of the time during a given time period. An efficient pruning-based algorithm for the queries with fixed k is proposed in this paper. For the case of k being variable, this paper proposes a pruning-based algorithm with an index to achieve a trade-off between time and space. Experiments on both real and synthetic datasets demonstrate that the proposed algorithms are very efficient.