Re-finding is the process of searching for information that a user has previously encountered and is a common activity carried out with information retrieval systems. In this work, we investigate re-finding in the context of vertical search, differentiating and modeling user re-finding behavior within different media and topic domains, including images, news, reference material, and movies. We distinguish the re-finding behavior in vertical domains from re-finding in a general search context and engineer features that are effective in differentiating re-finding across the domains. The features are then used to build machine-learned models, achieving an accuracy of re-finding detection in verticals of 85.7% on average. Our results demonstrate that detecting re-finding in specific verticals is more difficult than examining re-finding for general search tasks. We then investigate the effectiveness of differentiating re-finding behavior in two restricted contexts: We consider the case where the history of a searcher’s interactions with the search system is not available. In this scenario, our features and models achieve an average accuracy of 77.5% across the domains. We then examine the detection of re-finding during the early part of a search session. Both of these restrictions represent potential real-world search scenarios, where a system is attempting to learn about a user but may have limited information available. Finally, we investigate in which types of domains re-finding is most difficult. Here, it would appear that re-finding images is particularly challenging for users. This research has implications for search engine design, in terms of adapting search results by predicting the type of user tasks and potentially enabling the presentation of vertical-specific results when re-finding is identified. To the best of our knowledge, this is the first work to investigate the issue of vertical re-finding.

With the ever increasing usage of mobile search, where text input is typically slow and error-prone, assisting users to formulate their queries contributes to a more satisfactory search experience. Query auto-completion (QAC) techniques, which predict possible completions for user queries, are the archetypal example of query assistance and are present in most search engines. We argue, however, that classic QAC, which operates by suggesting whole-query completions, may be sub-optimal for the case of mobile search as the available screen real estate to show suggestions is limited and editing is typically slower than in desktop search. In this paper we propose the idea of term-by-term QAC, which is a new technique inspired by predictive keyboards that suggests to the user one term at a time, instead of whole-query completions. We describe an efficient mechanism to implement this technique and an adaptation of a prior user model to evaluate the effectiveness of both standard and term-by-term QAC approaches using query log data. Our experiments with a mobile query log from a commercial search engine show the validity of our approach according to this user model with respect to saved characters, saved terms and examination effort. Finally, a user study provides further insights about our term-by-term technique compared with standard QAC with respect to the variables analyzed in the query log-based evaluation and additional variables related to the successfulness, the speed of the interactions and the properties of the submitted queries.

Following news about a specific event can be a difficult task as new information is often scattered across web pages. An up-to-date summary of the event would help to inform users and allow them to navigate to articles that are likely to contain relevant and novel details. We demonstrate an approach that is feasible for online tracking of news that is relevant to a user's ad-hoc query.

The Web is rapidly transforming from a pure document collection to the largest connected public data space. Semantic annotations of web pages make it notably easier to extract and reuse data and are increasingly used by both search engines and social media sites to provide better search experiences through rich snippets, faceted search, task completion, etc. In our work, we study the novel problem of crawling structured data embedded inside HTML pages. We describe Anthelion, the first focused crawler addressing this task. We propose new methods of focused crawling specifically designed for collecting data-rich pages with greater efficiency. In particular, we propose a novel combination of online learning and bandit-based explore/exploit approaches to predict data-rich web pages based on the context of the page as well as using feedback from the extraction of metadata from previously seen pages. We show that these techniques significantly outperform state-of-the-art approaches for focused crawling, measured as the ratio of relevant pages and non-relevant pages collected within a given budget.

Helen Hastie, Marie-Aude Aufaure, Panos Alexopoulos, Hugues Bouchard, Catherine Breslin, Heriberto Cuayáhuitl, Nina Dethlefs, Milica Gašić, James Henderson, Oliver Lemon, Xingkun Liu, Peter Mika, Nesrine Ben Mustapha, Tim Potter, Verena Rieser, Blaise Thomson, Pirros Tsiakoulis, Yves Vanrompay, Boris Villazon-Terrazas, Majid Yazdani, Steve Young, Yanchao Yu. Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL). 2014.

Most traditional recommender systems focus on the objective of improving the accuracy of recommendations in a single domain. However, preferences of users may extend over multiple domains, especially in the Web where users often have browsing preferences that span across different sites, while being unaware of relevant resources on other sites. This work tackles the problem of recommending resources from various domains by exploiting the semantic content of these resources in combination with patterns of user browsing behavior. We overcome the lack of overlaps between domains by deriving connections based on the explored semantic content of Web resources. We present an approach that applies Support Vector Machines for learning the relevance of resources and predicting which ones are the most relevant to recommend to a user, given that the user is currently viewing a certain page. In real-world datasets of semantically-enriched logs of user browsing behavior at multiple Web sites, we study the impact of structure in generating accurate recommendations and conduct experiments that demonstrate the effectiveness of our approach.

Web usage mining has traditionally focused on the individual queries or query words leading to a web site or web page visit, mining patterns in such data. In our work, we aim to characterize websites in terms of the semantics of the queries that lead to them by linking queries to large knowledge bases on the Web. We demonstrate how to exploit such links for more effective pattern mining on query log data. We also show how such patterns can be used to qualitatively describe the differences between competing websites in the same domain and to quantitatively predict website abandonment.

Search, exploration and social experience on the Web has recently undergone tremendous changes with search engines, web portals and social networks offering a different perspective on information discovery and consumption. This new perspective is aimed at capturing user intents, and providing richer and highly connected experiences. The new battleground revolves around technologies for the ingestion, disambiguation and enrichment of entities from a variety of structured and unstructured data sources - we refer to this process as knowledge base synthesis. This paper presents the design, implementation and production deployment of the Web Of Objects (WOO) system, a Hadoop-based platform tackling such challenges. WOO has been designed and implemented to enable various products in Yahoo! to synthesize knowledge bases (KBs) of entities relevant to their domains. Currently, the implementation of WOO we describe is used by various Yahoo! properties such as Intonow, Yahoo! Local, Yahoo! Events and Yahoo! Search. This paper highlights: (i) challenges that arise in designing, building and operating a platform that handles multi-domain, multi-version, and multi-tenant disambiguation of web-scale knowledge bases (hundreds of millions of entities), (ii) the architecture and technical solutions we devised, and (iii) an evaluation on real-world production datasets.

There is an increasing amount of structure on the Web as a result of modern Web languages, user tagging and annotation, emerging robust NLP tools, and an ever growing volume of linked data. These meaningful, semantic, annotations hold the promise to significantly enhance information access, by enhancing the depth of analysis of today's systems. Currently, we have only started exploring the possibilities and only begin to understand how these valuable semantic cues can be put to fruitful use. To complicate matters, standard text search excels at shallow information needs expressed by short keyword queries, and here semantic annotation contributes very little, if anything. The main questions for the workshop are how to leverage the rich context currently available, especially in a mobile search scenario, giving powerful new handles to exploit semantic annotations. And how can we fruitfully combine information retrieval and semantic web approaches, and for the first time work actively toward a unified view on exploiting semantic annotations.

Query logs record the actual usage of search systems and their analysis has proven critical to improving search engine functionality. Yet, despite the deluge of information, query log analysis often suffers from the sparsity of the query space. Based on the observation that most queries pivot around a single entity that represents the main focus of the user's need, we propose a new model for query log data called the entity-aware click graph. In this representation, we decompose queries into entities and modifiers, and measure their association with clicked pages. We demonstrate the benefits of this approach on the crucial task of understanding which websites fulfill similar user needs, showing that using this representation we can achieve a higher precision than other query log-based approaches.

As user demands become increasingly sophisticated, search engines today are competing in more than just returning document results from the Web. One area of competition is providing web object results from structured data extracted from a multitude of information sources. We address the problem of performing keyword retrieval over a collection of objects containing a large degree of duplication as different Web-based information sources provide descriptions of the same object. We develop a method for coreference aware retrieval that performs topic-specific coreference resolution on retrieved objects in order to improve object search results. Our results demonstrate that coreference has a significant impact on the effectiveness of retrieval in the domain of local search. Our results show that a coreference aware system outperforms naive object retrieval by more than 20% in P5 and P10.

The primary problem confronting any new kind of search task is how to boot-strap a reliable and repeatable evaluation campaign, and a crowd-sourcing approach provides many advantages. However, can these crowd-sourced evaluations be repeated over long periods of time in a reliable manner? To demonstrate, we investigate creating an evaluation campaign for the semantic search task of keyword-based ad-hoc object retrieval. In contrast to traditional search over web-pages, object search aims at the retrieval of information from factual assertions about real-world objects rather than searching over web-pages with textual descriptions. Using the first large-scale evaluation campaign that specifically targets the task of ad-hoc Web object retrieval over a number of deployed systems, we demonstrate that crowd-sourced evaluation campaigns can be repeated over time and still maintain reliable results. Furthermore, we show how these results are comparable to expert judges when ranking systems and that the results hold over different evaluation and relevance metrics. This work provides empirical support for scalable, reliable, and repeatable search system evaluation using crowdsourcing.

"Ten blue links" have defined web search results for the last fifteen years -- snippets of text combined with document titles and URLs. In this paper, we establish the notion of enhanced search results that extend web search results to include multimedia objects such as images and video, intent-specific key value pairs, and elements that allow the user to interact with the contents of a web page directly from the search results page. We show that users express a preference for enhanced results both explicitly, and when observed in their search behavior. We also demonstrate the effectiveness of enhanced results in helping users to assess the relevance of search results. Lastly, we show that we can efficiently generate enhanced results to cover a significant fraction of search result pages.

Semantic Search refers to a loose set of concepts, challenges and techniques having to do with harnessing the information of the growing Web of Data (WoD) for Web search. Here we propose a formal model of one specific semantic search task: ad-hoc object retrieval. We show that this task provides a solid framework to study some of the semantic search problems currently tackled by commercial Web search engines. We connect this task to the traditional ad-hoc document retrieval and discuss appropriate evaluation metrics. Finally, we carry out a realistic evaluation of this task in the context of a Web search application.

We present a semantic approach to suggesting query completions which leverages entity and type information. When compared to a frequency-based approach, we show that such information mostly helps rare queries.

We discuss the problem of ranking very many entities of different types. In particular we deal with a heterogeneous set of types, some being very generic and some very specific. We discuss two approaches for this problem: i) exploiting the entity containment graph and ii) using a Web search engine to compute entity relevance. We evaluate these approaches on the real task of ranking Wikipedia entities typed with a state-of-the-art named-entity tagger. Results show that both approaches can greatly increase the performance of methods based only on passage retrieval.

A formal, web-based representation of social networks is both a necessity in terms of infrastructure as well as a prominent application for the Semantic Web. In this paper we present three advances in exploiting the opportunity of semantically-enriched network data: (1) an ontology for the representation of social networks and relationships (2) a hybrid system for online data acquisition that combines traditional web mining techniques with the collection of Semantic Web data (2) a case study highlighting some of the possible analysis of this data using methods from Social Network Analysis, the branch of sociology concerned with relational data.

Clarity in semantics and a rich formalization of this semantics are important requirements for ontologies designed to be deployed in large-scale, open, distributed systems such as the envisioned Semantic Web This is especially important for the description of Web Services, which should enable complex tasks involving multiple agents. As one of the first initiatives of the Semantic Webcommunity for describing Web Services, OWL-S attracts a lot of interest even though it is still under development. We identify problematic aspects of OWL-S and suggest enhancements through alignment to a foundational ontology. Another contribution of ourwork is the Core Ontology of Services that tries to fill the epistemological gap between the foundational ontology and OWL-S. It can be reused to align other Web Service description languages as well. Finally, we demonstrate the applicability of our work byaligning OWL-S' standard example called CongoBuy.