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Personalized ranking in web databases: establishing and utilizing an appropriate workload

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Abstract

The emergence of the deep Web has given a new connotation to the concept of ranking database query results. Earlier approaches for ranking either resorted to analyzing frequencies of database values and query logs or establishing user profiles. In contrast, an integrated approach, based on the notion of a similarity model, for holistically supporting user- and query-dependent ranking has been recently proposed (Telang et al. in IEEE Transactions on Knowledge and Data Engineering (TKDE), 2011). An important component of this framework is a workload consisting of ranking functions, wherein each function represents an individual user’s preferences towards the results of a specific query. At the time of answering a query for which no prior ranking function exists, the similarity model is employed, and is expected to ensure a good quality of ranking as long as a ranking function for a very similar user-query pair exists in this workload.

In this paper, we address the problem of determining an appropriate set of user-query pairs to form a workload of ranking functions to support user- and query-dependent ranking for Web databases. We propose a novel metric, termed workload goodness, that quantifies the notion of a “good” workload into an absolute value. The process of finding such a workload of optimal goodness is a combinatorially explosive problem; therefore, we propose a heuristic solution, and advance three approaches for determining an acceptable workload, in a static as well as a dynamic environment. We discuss the effectiveness of our proposal analytically as well as experimentally over two Web databases.

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Notes

  1. The concept of workload here is significantly different from the one in traditional databases. In the Former’s case, the workload is a collection of ranking functions along with the user-query pairs for whom the functions are derived; in contrast, it pertains to a log of queries in the latter’s context.

  2. A ranking function is obtained via a learning model, proposed in [30], that analyzes a user’s preferences towards the results of a query.

  3. The functional details of the similarity-based ranking framework are elaborated in Sect. 2.

  4. Given that we focus on establishing only W K , we use the term workload and W K interchangeably for the rest of the paper.

  5. Typically, the number of users and queries on most real Web databases like Yahoo! Autos, Google Base, etc. are extremely large, whereas the value of K is typically much smaller.

  6. The value ‘any’ will match all possible values for the domain of the particular attribute. For example, a value of ‘any’ for the Transmission attribute in a Vehicle database retrieves cars with ‘manual’ as well as ‘auto’ transmission.

  7. Without loss of generality, we assume {Q 1,Q 2,…,Q r } are the common queries for U and U′, although they can be any queries.

  8. As elaborated in Sect. 2, since the highest rank of 0 is assigned to the pair itself, the next highest possible rank, computed by (1), of a user-query pair with respect to a given pair is 1.

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Authors and Affiliations

  1. Dept. of Comp. Sci. & Engg., The University of Texas at Arlington, Arlington, TX, USA

    Aditya Telang, Sharma Chakravarthy & Chengkai Li

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  1. Aditya Telang
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  2. Sharma Chakravarthy
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Correspondence to Aditya Telang.

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Communicated by: Kaushik Chakrabarti.

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Telang, A., Chakravarthy, S. & Li, C. Personalized ranking in web databases: establishing and utilizing an appropriate workload. Distrib Parallel Databases 31, 47–70 (2013). https://doi.org/10.1007/s10619-012-7106-2

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