The dramatic growth in the number of application domains that naturally generate probabilistic, uncertain data has resulted in a need for efficiently supporting complex querying and decision-making over such data. In this paper, we present a unified approach to ranking and top-k query processing in probabilistic databases by viewing it as a multi-criteria optimization problem, and by deriving a set of features that capture the key properties of a probabilistic dataset that dictate the ranked result. We contend that a single, specific ranking function may not suffice for probabilistic databases, and we instead propose two parameterized ranking functions, called PRF-w and PRF-e, that generalize or can approximate many of the previously proposed ranking functions. We present novel generating functions-based algorithms for efficiently ranking large datasets according to these ranking functions, even if the datasets exhibit complex correlations modeled using probabilistic and xor trees or Markov networks. We further propose that the parameters of the ranking function be learned from user preferences, and we develop an approach to learn those parameters. Finally, we present a comprehensive experimental study that illustrates the effectiveness of our parameterized ranking functions, especially PRF-e, at approximating other ranking functions and the scalability of our proposed algorithms for exact or approximate ranking.
There is a tremendous body of work on ranking documents in information retrieval, and learning how to rank documents given user preferences (see Liu @cite_36 for a comprehensive survey). That work has considered aspects such as different ranking models, loss functions, different scoring techniques etc. The techniques developed there tend to be specific to document retrieval (focusing on keywords, terms, and relevance), and usually do not deal with existence uncertainty (although they often do model document relevance as a random variable). Furthermore, our work here primarily focuses on highly efficient algorithms for ranking using a spectrum of different ranking functions. Exploring and understanding the connections between the two research areas is a fruitful direction for further research.
@cite_36
2037098674
Traditionally, information retrieval systems aim to maximize thenumber of relevant documents returned to a user within some windowof the top. For that goal, the probability ranking principle, whichranks documents in decreasing order of probability of relevance, isprovably optimal. However, there are many scenarios in which thatranking does not optimize for the users information need. Oneexample is when the user would be satisfied with some limitednumber of relevant documents, rather than needing all relevantdocuments. We show that in such a scenario, an attempt to returnmany relevant documents can actually reduce the chances of findingany relevant documents. We consider a number of information retrieval metrics from theliterature, including the rank of the first relevant result, the no metric that penalizes a system only for retrieving no relevantresults near the top, and the diversity of retrieved results whenqueries have multiple interpretations. We observe that given aprobabilistic model of relevance, it is appropriate to rank so asto directly optimize these metrics in expectation. While doing somay be computationally intractable, we show that a simple greedyoptimization algorithm that approximately optimizes the givenobjectives produces rankings for TREC queries that outperform thestandard approach based on the probability ranking principle.