Information Foraging is a theory that describes information retrieval behaviour proposed by Pirolli and Card (1999). Information foraging theory is derived from a food foraging theory called optimal foraging theory that helps biologists understand the factors determining an animal's food preference and feeding strategies.
The theory focuses on “Information” but this can apply to any item, be it text, video, audio or image. The basis of foraging theory is a cost and benefit assessment of achieving a goal where cost is the amount of resources consumed when performing a chosen activity and the benefit is what is gained from engaging in that activity. In the case of a predator, how much effort is expelled in stalking and chasing a specific species of prey and how much energy can be gained from eating it. Optimal foraging is getting the highest amount of benefit whilst expending the lowest amount of energy through the structuring of environments and the selection of appropriate strategies . This cost-benefit assessment is essential for effectively achieving any goal-driven activity.
Whether information retrieval is conducted through searching or browsing techniques it can be considered a rational and goal-driven activity at all levels of granularity. When performing a traditional query search there is a clearly defined goal as to what is being sought and what the searcher hopes to be returned within the result set. When browsing there is an iterative pattern of understanding the current state, setting a goal that subsequent browsing interactions will aim to achieve, deriving a plan to achieve it and then evaluating the result of that interaction. In serendipitous browsing the goal is less apparent but interactions are still driven by an intention based on an evaluation of the environment.
Information foraging theory is based on the analogy of an animal deciding what to eat, where it can be found, the best way to obtain it and how much “energy” (how filling) the meal will (be) provide. The analogy is described in more detail in the following extract from Pirolli and Card (1999).
Imagine a predator, such as a bird of prey, that faces the recurrent problem of deciding what to eat, and we assume that its fitness, in terms of reproductive success, is dependent on energy intake. Energy flows into the environment and comes to be stored in different forms. For the bird of prey, different types of habitat and prey will yield different amounts of net energy (energetic profitability) if included in the diet. Furthermore, the different food-source types will have different distributions over the environment. For the bird of prey, this means that the different habitats or prey will have different access or navigation costs. Different species of birds of prey might be compared on their ability to extract energy from the environment. Birds are better adapted if they have evolved strategies that better solve the problem of maximizing the amount of energy returned per amount of effort. Conceptually, the optimal forager finds the best solution to the problem of maximizing the rate of net energy returned per effort expended, given the constraints of the environment in which it lives. Pirolli and Card 1999
The key concepts to emerge from this analogy are the food source, the location in which it can be found, the strategies with which to find it, the tools available to find it and the benefit that can be gained from its consumption. These concepts can be translated into an information-seeking context using the following terms:
The challenge of the information retrieval community is to design interfaces that effectively support these concepts. Interfaces that provide rich sources of metadata, useful navigation cues, effective content grouping and that support appropriate strategies for different information requirements. This is especially challenging in the case of the growing number of large video and image collections where much of the content has little, to no, manual mark-up. Poor mark-up reduces our ability to search for content and increases the amount of browsing behaviour that is determined based on the results of fallible image and video analysis technologies.
The benefits that are gained from information seeking are not always easy to observe as an information-seeking task is embedded in the context and goals of some other task. The benefit of finding information is seen in the improved ability of a user to perform a task that utilises the information found. In information foraging this cost/ benefit model is dynamic over time in relation to this embedded task. Should the task become lower priority then the benefit of consuming the information is likewise reduced.
In order to realise this model into something that we can use to evaluate information retrieval technology and understand user behaviour with these systems Pirolli and Card have developed a detailed process model called ACT-IF. This process model presents a cognitive model for information foraging in which the efficiency of information retrieval is calculated by assessments of information scent and heuristic values for the selection of production rules. The ACT-IF model is based on conventional foraging theory metrics and equations that are detailed in Pirolli and Card (1999). The ability of ACT-IF to apply to information foraging is not fully understood and there is a requirement for further studies of information retrieval behaviour. Creating an ACT-IF is a complex task and the author of this article has not experienced it in detail. Further additions to this article in relation to ACT-IF are welcomed.
Without going into the detailed analysis provided by the ACT-IF process models the theory of information foraging offers a novel and useful perspective on designing systems for information retrieval. It encourages the designer to think about the structure of the interface, how to support different searching and browsing strategies appropriate for the context of work and how to effectively use metadata cues to enhance item selection and patch navigation. The author of this article has effectively used this theory to provide a framework for information retrieval into which key principles and interactions can be structured to offer a detailed heuristic evaluation of how well different systems support different information retrieval tasks.
Information foraging is becoming a popular theory for describing and understanding web browsing behaviour (Chalmers 2000, Dix et al. 2003) and a major influence for information visualisation researchers attempting to discover effective ways to represent massive amounts of data and provide effective mechanisms for navigating through it.