Users often issue vague queries; when we cannot predict their intents precisely, a natural solution is to diversify the search results, hoping that some of the results correspond to the intent: This is usually called “result diversification”. Only a few studies have been completed to systematically evaluate approaches on result diversity. Some questions still remain unanswered: 1) As we cannot exhaustively list all intents in an evaluation, how does an incomplete intent set influence evaluation results? 2) Intents are not equally popular; so how can we estimate the probability of each intent? In this paper, we address these questions in building up a test collection for multi-intent queries. The labeling tool that we have developed allows assessors to add new intents while performing relevance assessments. Thus, we can investigate the influence of an incomplete intent set through experiments. Moreover, we propose two simple methods to estimate the probabilities of the underlying intents. Experimental results indicate that the evaluation results are different if we take the probabilities into consideration.

People interact with interfaces to accomplish goals, and knowledge about human goals can be useful for building intelligent user interfaces. We suggest that modeling high, human-level goals like “repair my credit score”, is especially useful for coordinating workflows between interfaces, automated planning, and building introspective applications. We analyzed data from 43Things.com, a website where users share and discuss goals and plans in natural language, and constructed a goal network that relates what goals people have with how people solve them. We then label goals with specific details, such as where the goal typically is met and how long it takes to achieve, facilitating plan and goal recognition. Lastly, we demonstrate a simple application of goal networks, deploying it in a mobile, location-aware to-do list application, ToDoGo, which uses goal networks to help users plan where and when to accomplish their desired goals.

Search engines represent a primary instrument for users of the World Wide Web to express their informational needs. However, during the process of search query formulation, the original intention behind a search query is often lost. This master’s thesis aims to address this problem by studying the construction of different graphs of search goals expressed in search query logs. While previous work focused on classification of web queries into intent taxonomies, this work focuses on queries that contain explicit statements of goals. To infer relations between search goals, a new type of graphs which can be derived from search query logs is introduced: bipartite goal-tag graphs. This work demonstrates how such graphs can be utilized to predict the intent a user has given a query he or she issued or how to get related goals of a specified goal. One of the major contributions is a parametric method for graph construction and corresponding qualitative and quantitative evaluations. Furthermore SearchGoalNet- a network of human search goals containing 57562 goals- is presented and applications, which are based on it, like a Goal Prediction Interface and a web service which enables access to the graphs, are illustrated. Selected results of this work have been published in [Strohmaier et al., 2009].