Chapter review from book: Search User Interfaces Marti Hearst 2009
Search Interface technology has newer trends in the areas of mobile search. multimedia search, social search, and a hybrid of natural language and command based queries in search.
Mobile search interfaces stand out because it is predicted to be the primary means of connecting to the internet by 2020 (Rainie,2008) . Here time and location information could be used to better anticipate a mobile users information needs. For example, a user may want to reserve a table at a restaurant in the next half -hour within a one mile radius.. Another factor observed by Kamvar and Baluja 2006 from a study of the requests to mobile search engine sites was that the query length was shorter for handheld devices than desktop searches and they had fewer variations. They also observed that the users did more retries and reformulations than change the topic. Context based search where context can be defined as current activity, location, time and conversation becomes relevant to mobile queries. Query entry on mobile devices are also subject to dynamic term suggestions and anticipation of common queries. Search results are also presented differently on handheld devices such as with formatting to list essential content only. Formatting could be such that the layout with thumbnails may be preserved but the relevant text could be highlighted. Information visualization and categorization also helps improve the usability on these devices. This is advocated for navigation of pages in the Fathumb (Karlson et al.) interfaces. Results could also be specialized for certain queries such as showing maps when it involves locations. Besides presenting results, some browsers are also optimized for handheld devices.
Images, video and audio are also increasingly being searched on mobile devices. While automatic image recognition is still difficult, automatic speech recognition is greatly improved. There are techniques to input queries that are spoken. Yet Image searches are an important part of the overall searches performed on mobile devices. And the issue is harder to solve when the associated text or metadata with the images do not describe the image content adequately. Videos are searched by segmenting the data into scenes and then shots. Text associate with the shots is attributed to the corresponding index in the videos. Audio searches are improved when the associated audio could be converted to some extent to text.
When the different multimedia results are to be included for a user's query input, it is called blended results or universal search. In general search results are improved with keywords, text labels from anchor texts, surrounding text in web pages or human assisted tags.
Another interesting trend for search is social search. Web 2.0 is considered to be interactive web where people interact with one another for their online needs. Social ranking of web pages, collaborative search and human powered question answering are all examples of user interaction for search. Social ranking is considered the "wisdom of crowds" and is generally indicated by the number of people's recommendations which can then be ranked by a search engine to display the results. Social tagging of websites or liking a website on social networking tools such as Facebook and search engine features to let the user rank are all used for ranking the results based on social interaction.
Multi-person and collaborative search on the other hand is for users to do common tasks involving collaboration such as for travel planning, grocery shopping, literature search, technical information search and fact finding. Typically a centralized location for assimilating a list and a view into participants actions is provided for such search. The latter helps with precision and recall. Precision and Recall are two terms used to measure search: Precision is the fraction of the retrieved instances that are relevant, while Recall is the fraction of relevant instances that are not retrieved. Therefore Precision considers the retrieved results and Recall considers the universe of relevant results. Along the same lines, another set of metrics are freshness and relevance. Freshness is about documents not yet looked at and relevance is about documents that are a match for the user's query. These two metrics counter balance each other and are continuously updated.
Massive scale human question answering is another technique for social search where people enter questions and thousands of others suggest answers in near real-time. This is particularly helpful in certain languages other than English.
Lastly, semantic search is another trend where keywords are discerned by looking up related synonyms to enhance search results.
Search Interface technology has newer trends in the areas of mobile search. multimedia search, social search, and a hybrid of natural language and command based queries in search.
Mobile search interfaces stand out because it is predicted to be the primary means of connecting to the internet by 2020 (Rainie,2008) . Here time and location information could be used to better anticipate a mobile users information needs. For example, a user may want to reserve a table at a restaurant in the next half -hour within a one mile radius.. Another factor observed by Kamvar and Baluja 2006 from a study of the requests to mobile search engine sites was that the query length was shorter for handheld devices than desktop searches and they had fewer variations. They also observed that the users did more retries and reformulations than change the topic. Context based search where context can be defined as current activity, location, time and conversation becomes relevant to mobile queries. Query entry on mobile devices are also subject to dynamic term suggestions and anticipation of common queries. Search results are also presented differently on handheld devices such as with formatting to list essential content only. Formatting could be such that the layout with thumbnails may be preserved but the relevant text could be highlighted. Information visualization and categorization also helps improve the usability on these devices. This is advocated for navigation of pages in the Fathumb (Karlson et al.) interfaces. Results could also be specialized for certain queries such as showing maps when it involves locations. Besides presenting results, some browsers are also optimized for handheld devices.
Images, video and audio are also increasingly being searched on mobile devices. While automatic image recognition is still difficult, automatic speech recognition is greatly improved. There are techniques to input queries that are spoken. Yet Image searches are an important part of the overall searches performed on mobile devices. And the issue is harder to solve when the associated text or metadata with the images do not describe the image content adequately. Videos are searched by segmenting the data into scenes and then shots. Text associate with the shots is attributed to the corresponding index in the videos. Audio searches are improved when the associated audio could be converted to some extent to text.
When the different multimedia results are to be included for a user's query input, it is called blended results or universal search. In general search results are improved with keywords, text labels from anchor texts, surrounding text in web pages or human assisted tags.
Another interesting trend for search is social search. Web 2.0 is considered to be interactive web where people interact with one another for their online needs. Social ranking of web pages, collaborative search and human powered question answering are all examples of user interaction for search. Social ranking is considered the "wisdom of crowds" and is generally indicated by the number of people's recommendations which can then be ranked by a search engine to display the results. Social tagging of websites or liking a website on social networking tools such as Facebook and search engine features to let the user rank are all used for ranking the results based on social interaction.
Multi-person and collaborative search on the other hand is for users to do common tasks involving collaboration such as for travel planning, grocery shopping, literature search, technical information search and fact finding. Typically a centralized location for assimilating a list and a view into participants actions is provided for such search. The latter helps with precision and recall. Precision and Recall are two terms used to measure search: Precision is the fraction of the retrieved instances that are relevant, while Recall is the fraction of relevant instances that are not retrieved. Therefore Precision considers the retrieved results and Recall considers the universe of relevant results. Along the same lines, another set of metrics are freshness and relevance. Freshness is about documents not yet looked at and relevance is about documents that are a match for the user's query. These two metrics counter balance each other and are continuously updated.
Massive scale human question answering is another technique for social search where people enter questions and thousands of others suggest answers in near real-time. This is particularly helpful in certain languages other than English.
Lastly, semantic search is another trend where keywords are discerned by looking up related synonyms to enhance search results.
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