Efficient Computation of Diverse Query Results

Abstract

We study the problem of efficiently computing diverse query results in online shopping applications, where users specify queries through a form interface that allows a mix of structured and content-based selection conditions. Intuitively, the goal of diverse query answering is to return a representative set of top-k answers from all the tuples that satisfy the user selection condition. For example, if a user is searching for Honda cars and we can only display five results, we wish to return cars from five different Honda models, as opposed to returning cars from only one or two Honda models. A key contribution of this paper is to formally define the notion of diversity, and to show that existing score based techniques commonly used in web applications are not sufficient to guarantee diversity. Another contribution of this paper is to develop novel and efficient query processing techniques that guarantee diversity. Our experimental results using Yahoo! Autos data show that our proposed techniques are scalable and efficient.

Diversity of editors and teams versus quality of cooperative work: experiments on wikipedia

“…Recently, diversity-awareness has gained increasing interest also in other information-related areas where the actual information need of a user is unknown and/or the user query is ambiguous so that a controlled level of diversity introduced to the results increases their quality. Examples range from databases (Vee et al 2008) to Web search (Agrawal et al 2009) or to the quite novel problem of graphical entity summarisation in semantic knowledge graphs (Sydow et al 2013). A recent work (Strzezek et al 2015) demonstrates that a controlled level of population diversity increases the performance of genetic algorithm for some hard optimisation problems.…”

“…Diversity has proved to play an important role in multiple fields of information sciences and applications such as: text summarisation, web search (Agrawal et al 2009), databases (Vee et al 2008), recommender systems and semantic entity summarisation (Sydow et al 2013). Recent research also indicates that diversity of population plays a positive role in evolutionary algorithms (Strzezek et al 2015) Our hypothesis studied in this article is that diversity of editors and teams is a factor that positively affects the quality of work in a virtual cooperative environments.…”

Does a “Renaissance Man” Create Good Wikipedia Articles?

“…Examples range from databases (e.g. (Vee et al, 2008)) to Web search (e.g. (Agrawal et al, 2009)) or very recently to the quite novel problem of graphical entity summarisation in semantic knowledge graphs (Sydow et al, 2013).…”

Diversifying Product Review Rankings: Getting the Full Picture

“…al. [31] propose an algorithm for finding a representative, diverse set of top-k results for a given query. All attributes of an object are ordered according to their priority for diversification by a domain expert.…”