Trends in Information Retrieval
In this section we review a few concepts that are being considered in more recent research work in information retrieval.
1. Faceted Search
Faceted Search is a technique that allows for integrated search and navigation experience by allowing users to explore by filtering available information. This search technique is used often in ecommerce Websites and applications enabling users to navigate a multi-dimensional information space. Facets are generally used for handling three or more dimensions of classification. This allows the faceted classification scheme to classify an object in various ways based on different taxonomical criteria. For example, a Web page may be classified in various ways: by content (air-lines, music, news, ...); by use (sales, information, registration, ...); by location; by language used (HTML, XML, ...) and in other ways or facets. Hence, the object can be classified in multiple ways based on multiple taxonomies.
A facet defines properties or characteristics of a class of objects. The properties should be mutually exclusive and exhaustive. For example, a collection of art objects might be classified using an artist facet (name of artist), an era facet (when the art was created), a type facet (painting, sculpture, mural, ...), a country of origin facet, a media facet (oil, watercolor, stone, metal, mixed media, ...), a collection facet (where the art resides), and so on.
Faceted search uses faceted classification that enables a user to navigate information along multiple paths corresponding to different orderings of the facets. This contrasts with traditional taxonomies in which the hierarchy of categories is fixed and unchanging. University of California, Berkeley’s Flamenco project is one of the earlier examples of a faceted search system.
2. Social Search
The traditional view of Web navigation and browsing assumes that a single user is searching for information. This view contrasts with previous research by library scientists who studied users’ information seeking habits. This research demonstrated that additional individuals may be valuable information resources during information search by a single user. More recently, research indicates that there is often direct user cooperation during Web-based information search. Some studies report that significant segments of the user population are engaged in explicit collaboration on joint search tasks on the Web. Active collaboration by multiple parties also occur in certain cases (for example, enterprise settings); at other times, and perhaps for a majority of searches, users often interact with others remotely, asynchronously, and even involuntarily and implicitly.
Socially enabled online information search (social search) is a new phenomenon facilitated by recent Web technologies. Collaborative social search involves different ways for active involvement in search-related activities such as co-located search, remote collaboration on search tasks, use of social network for search, use of expertise networks, involving social data mining or collective intelligence to improve the search process and even social interactions to facilitate information seeking and sense making. This social search activity may be done synchronously, asynchronously, co-located or in remote shared workspaces. Social psychologists have experimentally validated that the act of social discussions has facilitated cognitive performance. People in social groups can provide solutions (answers to questions), pointers to databases or to other people (meta-knowledge), validation and legitimization of ideas, and can serve as memory aids and help with problem reformulation. Guided participation is a process in which people co-construct knowledge in concert with peers in their community. Information seeking is mostly a solitary activity on the Web today. Some recent work on collaborative search reports several interesting findings and the potential of this technology for better information access.
3. Conversational Search
Conversational Search (CS) is an interactive and collaborative information finding interaction. The participants engage in a conversation and perform a social search activity that is aided by intelligent agents. The collaborative search activity helps the agent learn about conversations with interactions and feedback from participants. It uses the semantic retrieval model with natural language understanding to provide the users with faster and relevant search results. It moves search from being a solitary activity to being a more participatory activity for the user. The search agent performs multiple tasks of finding relevant information and connecting the users together; participants provide feedback to the agent during the conversations that allows the agent to perform better.
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