Structured search result differentiation

Abstract

Studies show that about 50% of web search is for information exploration purpose, where a user would like to investigate, compare, evaluate, and synthesize multiple relevant results. Due to the absence of general tools that can effectively analyze and differentiate multiple results, a user has to manually read and comprehend potentially large results in an exploratory search. Such a process is time consuming, labor intensive and error prone. With meta information embedded, keyword search on structured data provides the potential for automating or semi-automating the comparison of multiple results.In this paper we present an approach for differentiating search results on structured data. We define the differentiability of query results and quantify the degree of difference. Then we define the problem of identifying a limited number of valid features in a result that can maximally differentiate this result from the others, which is proved to be NP-hard. We propose two local optimality conditions, namely singleswap and multi-swap. Efficient algorithms are designed to achieve local optimality. To show the applicability of our approach, we implemented a system XRed for XML result differentiation. Our empirical evaluation verifies the effectiveness and efficiency of the proposed approach.

On the complexity of query result diversification

“…The complexity of diversification has been studied in [3,17,25,31,36], which differ from ours in the following.…”

“…Diversification has been studied for Web search [3,6,7,17,36], recommender systems [38,39,40,41], and structured databases [9,16,25,35] possibly with user preferences [8,31] (see [12,26] for surveys). As remarked earlier, the previous work has mostly focused on metrics for assessing relevance and diversity, and algorithms and optimization techniques for computing diverse answers.…”

“…The diversity of query answers is measured in terms of (a) contents, to include items that are dissimilar to each other, (b) novelty, to retrieve items that contain new information not found in previous results, and (c) coverage, to advocate items in different categories [12]. It has proven effective in Web search [17,36], recommender systems [39,40,41], databases [9,25,35], as well as in operations research and finance (see [12,26] for surveys).…”

“…While there has been a host of work on result diversification, the previous work has mostly focused on diversity and relevance metrics, and on algorithms for computing diverse results [12,26]. Few complexity results have been developed for query result diversification, and the known results are mostly lower bounds (NP-hardness) [3,17,25,31,36]. Furthermore, these results are established by assuming that query result Q(D) is already known.…”

Scalable diversification for data exploration platforms

“…e.g., web search [6,7,97], recommender systems [86,99,100,103], database search [63,96] and query suggestions [24,77,85]. Regardless of the domain, in general the diversification problem can be defined as follows:…”