Sparse spatial selection for novelty-based search result diversification

Abstract. Novelty-based diversification approaches aim to produce a diverse ranking by directly comparing the retrieved documents. However, since such approaches are typically greedy, they require O(n 2) documentdocument comparisons in order to diversify a ranking of n documents. In this work, we propose to model novelty-based diversification as a similarity search in a sparse metric space. In particular, we exploit the triangle inequality property of metric spaces in order to drastically reduce the number of required document-document comparisons. Thorough experiments using three TREC test collections show that our approach is at least as effective as existing novelty-based diversification approaches, while improving their efficiency by an order of magnitude.

Intent-aware search result diversification

Search result diversification has gained momentum as a way to tackle ambiguous queries. An effective approach to this problem is to explicitly model the possible aspects underlying a query, in order to maximise the estimated relevance of the retrieved documents with respect to the different aspects. However, such aspects themselves may represent information needs with rather distinct intents (e.g., informational or navigational). Hence, a diverse ranking could benefit from applying intent-aware retrieval models when estimating the relevance of documents to different aspects. In this paper, we propose to diversify the results retrieved for a given query, by learning the appropriateness of different retrieval models for each of the aspects underlying this query. Thorough experiments within the evaluation framework provided by the diversity task of the TREC 2009 and 2010 Web tracks show that the proposed approach can significantly improve state-of-the-art diversification approaches

Diversity and novelty in information retrieval

This tutorial aims to provide a unifying account of current research on diversity and novelty in different IR domains, namely, in the context of search engines, recommender sys- tems, and data streams

Diversity and novelty in web search, recommender systems and data streams

This tutorial aims to provide a unifying account of current research on diversity and novelty in the domains of web search, recommender systems, and data stream processing. © 2014 Authors

On the usefulness of query features for learning to rank

Learning to rank studies have mostly focused on query-dependent and query-independent document features, which enable the learning of ranking models of increased effectiveness. Modern learning to rank techniques based on regression trees can support query features, which are document-independent, and hence have the same values for all documents being ranked for a query. In doing so, such techniques are able to learn sub-trees that are specific to certain types of query. However, it is unclear which classes of features are useful for learning to rank, as previous studies leveraged anonymised features. In this work, we examine the usefulness of four classes of query features, based on topic classification, the history of the query in a query log, the predicted performance of the query, and the presence of concepts such as persons and organisations in the query. Through experiments on the ClueWeb09 collection, our results using a state-of-the-art learning to rank technique based on regression trees show that all four classes of query features can significantly improve upon an effective learned model that does not use any query feature

Exploiting query reformulations for web search result diversification

When a Web user's underlying information need is not clearly specified from the initial query, an effective approach is to diversify the results retrieved for this query. In this paper, we introduce a novel probabilistic framework for Web search result diversification, which explicitly accounts for the various aspects associated to an underspecified query. In particular, we diversify a document ranking by estimating how well a given document satisfies each uncovered aspect and the extent to which different aspects are satisfied by the ranking as a whole. We thoroughly evaluate our framework in the context of the diversity task of the TREC 2009 Web track. Moreover, we exploit query reformulations provided by three major Web search engines (WSEs) as a means to uncover different query aspects. The results attest the effectiveness of our framework when compared to state-of-the-art diversification approaches in the literature. Additionally, by simulating an upper-bound query reformulation mechanism from official TREC data, we draw useful insights regarding the effectiveness of the query reformulations generated by the different WSEs in promoting diversity

Selectively diversifying Web search results

Search result diversification is a natural approach for tackling ambiguous queries. Nevertheless, not all queries are equally ambiguous, and hence different queries could benefit from different diversification strategies. A more lenient or more aggressive diversification strategy is typically encoded by existing approaches as a trade-off between promoting relevance or diversity in the search results. In this paper, we propose to learn such a trade-off on a per-query basis. In particular, we examine how the need for diversification can be learnt for each query - given a diversification approach and an unseen query, we predict an effective trade-off between relevance and diversity based on similar previously seen queries. Thorough experiments using the TREC ClueWeb09 collection show that our selective approach can significantly outperform a uniform diversification for both classical and state-of-the-art diversification approaches

On the role of novelty for search result diversification

Re-ranking the search results in order to promote novel ones has traditionally been regarded as an intuitive diversification strategy. In this paper, we challenge this common intuition and thoroughly investigate the actual role of novelty for search result diversification, based upon the framework provided by the diversity task of the TREC 2009 and 2010 Web tracks. Our results show that existing diversification approaches based solely on novelty cannot consistently improve over a standard, non-diversified baseline ranking. Moreover, when deployed as an additional component by the current state-of-the-art diversification approaches, our results show that novelty does not bring significant improvements, while adding considerable efficiency overheads. Finally, through a comprehensive analysis with simulated rankings of various quality, we demonstrate that, although inherently limited by the performance of the initial ranking, novelty plays a role at breaking the tie between similarly diverse results

Effectiveness beyond the first crawl tier

Modern Web crawlers seek to visit quality documents first, and re-visit them more frequently than other documents. As a result, the first-tier crawl of a Web corpus is typically of higher quality compared to subsequent crawls. In this paper, we investigate the impact of first-tier documents on adhoc retrieval performance. In particular, we analyse the retrieval performance of runs submitted to the adhoc task of the TREC 2009 Web track in terms of how they rank first-tier documents and how these documents contribute to the performance of each run. Our results show that the performance of these runs is heavily dependent on their ability to rank first-tier documents. Moreover, we show that, different from leading Web search engines, their attempt to go beyond the first tier almost always results in decreased performance. Finally, we show that selectively removing spam from different tiers can be a direction for fully exploiting documents beyond the first tier

Explicit search result diversification through sub-queries

Queries submitted to a retrieval system are often ambiguous. In such a situation, a sensible strategy is to diversify the ranking of results to be retrieved, in the hope that users will find at least one of these results to be relevant to their information need. In this paper, we introduce xQuAD, a novel framework for search result diversification that builds such a diversified ranking by explicitly accounting for the relationship between documents retrieved for the original query and the possible aspects underlying this query, in the form of sub-queries. We evaluate the effectiveness of xQuAD using a standard TREC collection. The results show that our framework markedly outperforms state-of-the-art diversification approaches under a simulated best-case scenario. Moreover, we show that its effectiveness can be further improved by estimating the relative importance of each identified sub-query. Finally, we show that our framework can still outperform the simulated best-case scenario of the state-of-the-art diversification approaches using sub-queries automatically derived from the baseline document ranking itself