SAC : G : From Context to Query

Web surfers often face the need for additional material beyond the page, they are currently reading. To support web surfers in finding related material, we present a proactive retrieval approach, which is applicable to any search system that supports keyword queries. Our approach identifies the relevant context, detects an information need and automatically constructs a query to express this information need. We show, that with the presented approach, automatic queries can be constructed, which perform better in terms of precision (0.40) than the best queries, users are able to formulate (0.37). 1. PROBLEM AND MOTIVATION Reading a web page often triggers the need for additional information. Then a user has to visit a digital library or general search engine and express this information need as a query in order to retrieve related resources. Proactive retrieval simplifies this process by presenting related material according to the current context without explicit user interaction. In accordance with the user-based information seeking model of Marchionini & White [10], the required steps to turn the above process into automatic, proactive retrieval consist of: 1. identifying the relevant context 2. recognizing an information need 3. expressing this information need (as query to the retrieval system) We address all of these three steps, with a special emphasis on the third step, i.e. automatically constructing a query. 2. BACKGROUND AND RELATED WORK Proactive retrieval was first made popular by Rhodes as Just-in-Time Retrieval [14]. Recently, Rhodes’ research has been continued under the topic of zero effort queries [1] with special emphasis on mobile applications [8]. Zero effort queries require minimal, ideally no effort from the user in expressing her information need and obtaining relevant results. While earlier work [15, 9, 2] focused on document retrieval, a wide variety of content and media types is taken into account in more recent work [20]. However, most of the systems either treat the retrieval system as an integral part of the application or focus on domain-specific sets of information needs. We present an approach, that is agnostic of the underlying retrieval system. The de-coupling from the retrieval engine is achieved by focusing on the query-side of retrieval: our aim is to construct queries that yield results, relevant to the current context. More formally, we want to optimize the function f = g ◦ h : C → R, where C is the context and R the result set, towards relevant results. The mapping from the context C to a query Q is defined by h : C → Q and the retrieval of results by g : Q → R. Less formal, the whole process is defined as C h → Q g → R. In just-in-time retrieval systems, that treat the search engine as integral part, f is not a composition of g and h, but results are retrieved directly according to the context (f : C → R). In this paper, we treat the search engine as black box and focus on the query side of retrieval. This means, we have no influence on g, but rather seek to optimize h in terms of optimizing f = g ◦ h. 3. APPROACH AND UNIQUENESS The unique feature of our approach is treating the retrieval engine as a black box the key factor is constructing a query, which yields results relevant to a piece of text. While we describe the approach for a web setting, the core of finding a good query is equally applicable in any scenario, where related material to a piece of text is desirable. This material can be of any kind, such as scientific documents, cultural images or selling products, as long at is exposed via a search engine. In a web setting, a browser extension allows to proactively present resources to users, while observing the user’s context and adapting resources to the respective context [17]. In this setting, the observable context dimensions encompass first of all the web pages visited and in addition information like the user’s location. As a proof of concept, we developed a mobile application, which integrates multiple context dimensions [18], but focus on the textual content of web pages in this paper. This textual context can be subdivided into five levels of granularity (from fine-grained to coarse): terms, phrases, paragraphs, pages and sessions. Table 1 provides an overview of our approach for each of the steps, from identifying the relevant context to recognizing and expressing an information need, on the different levels of context granularity. In the following, we briefly sketch each step on every level and describe the expression of the information need on paragraph level in detail. We omit the term level, as it is captured by our approach for the phrase level by treating terms as single term phrases. While we conduct proactive retrieval on the phrase and paragraph level, Table 1: Overview on steps 1-3 for each contextual granularity level. Proactive retrieval is conducted on the phrase and paragraph level, while the page and session level serve as supporting input for the lower levels. context granularity detection method information need detection information need formulation information need representation phrase text selection TRUE (for selection) CRF Model terms paragraph web browser focus area topic overlap with user profile NER + filtering keyword detection terms + entities page NONE topic overlap with user profile main topic extraction entity session (sequence of pages) topic similarity navigation patterns session clusters main topic extraction entities (1) identify the relevant context (2) recognize an information need (3) express this information need we utilize the outcome of the more coarse-grained page and session level as supporting features for the aforementioned levels.