Efficient Diversification of Web Search Results

In this paper we analyze the efficiency of various search results diversification methods. While efficacy of diversification approaches has been deeply investigated in the past, response time and scalability issues have been rarely addressed. A unified framework for studying performance and feasibility of result diversification solutions is thus proposed. First we define a new methodology for detecting when, and how, query results need to be diversified. To this purpose, we rely on the concept of "query refinement" to estimate the probability of a query to be ambiguous. Then, relying on this novel ambiguity detection method, we deploy and compare on a standard test set, three different diversification methods: IASelect, xQuAD, and OptSelect. While the first two are recent state-of-the-art proposals, the latter is an original algorithm introduced in this paper. We evaluate both the efficiency and the effectiveness of our approach against its competitors by using the standard TREC Web diversification track testbed. Results shown that OptSelect is able to run two orders of magnitude faster than the two other state-of-the-art approaches and to obtain comparable figures in diversification effectiveness.Comment: VLDB201

University of Twente @ TREC 2009: Indexing half a billion web pages

This report presents results for the TREC 2009 adhoc task, the diversity task, and the relevance feedback task. We present ideas for unsupervised tuning of search system, an approach for spam removal, and the use of categories and query log information for diversifying search results

Experiments in Diversifying Flickr Result Sets

The 2013 MediaEval Retrieving Diverse Social Images Task looked to tackling the problem of search result diversification of Flickr results sets formed from queries about geographic places and landmarks. In this paper we describe our approach of using a min-max similarity diversifier coupled with pre-filters and a reranker. We also demonstrate a number of novel features for measuring similarity to use in the diversification step

Diversity, Assortment, Dissimilarity, Variety: A Study of Diversity Measures Using Low Level Features for Video Retrieval

In this paper we present a number of methods for re-ranking video search results in order to introduce diversity into the set of search results. The usefulness of these approaches is evaluated in comparison with similarity based measures, for the TRECVID 2007 collection and tasks [11]. For the MAP of the search results we find that some of our approaches perform as well as similarity based methods. We also find that some of these results can improve the P@N values for some of the lower N values. The most successful of these approaches was then implemented in an interactive search system for the TRECVID 2008 interactive search tasks. The responses from the users indicate that they find the more diverse search results extremely useful

Personalized Ranking in eCommerce Search

We address the problem of personalization in the context of eCommerce search. Specifically, we develop personalization ranking features that use in-session context to augment a generic ranker optimized for conversion and relevance. We use a combination of latent features learned from item co-clicks in historic sessions and content-based features that use item title and price. Personalization in search has been discussed extensively in the existing literature. The novelty of our work is combining and comparing content-based and content-agnostic features and showing that they complement each other to result in a significant improvement of the ranker. Moreover, our technique does not require an explicit re-ranking step, does not rely on learning user profiles from long term search behavior, and does not involve complex modeling of query-item-user features. Our approach captures item co-click propensity using lightweight item embeddings. We experimentally show that our technique significantly outperforms a generic ranker in terms of Mean Reciprocal Rank (MRR). We also provide anecdotal evidence for the semantic similarity captured by the item embeddings on the eBay search engine.Comment: Under Revie

ASFA: Back from the Edge and Moving towards a New Horizon

Since its establishment in 1971, ASFA has steadily grown its partnerships and database, paying increasing attention to developing countries’ needs. However, its technologies and partnership model have failed to keep pace with modern developments. In order to address this, the FAO ASFA Secretariat has decided to implement a new Business Model by 2023, and will work with its stakeholders, including the Impact and Strategies Working Group and Strategic Advisory Group, to ensure ASFA remains a valued information product. The new business model will ensure ASFA meets FAO strategic objectives and wider goals of increasing access and dissemination of aquatic sciences and fisheries information. Several analyses have been performed by the ASFA Secretariat and others, which have informed ASFA’s direction. Work has already progressed on a number of areas that will be discussed in this presentation, specifically: ASFA technologies (new input software; online controlled vocabulary); ASFA collaborations (partnering with FAO departments and projects as well as external collaborations); and ASFA partnership model (a new Publishing Agreement with increased access for institutions in developing countries). However, a number of areas of work are to be determined, one of them being the MOU between FAO and IAMSLIC. Updating the MOU could lead to better collaboration between IAMSLIC and ASFA, in line with FAO goals, especially in the area of digital preservation