What is an autocomplete or autosuggest feature in search? Autocomplete or autosuggest uses the partial search terms that a user is typing in to complete the rest of the search query so the user can simply click to search from a drop down box in the search form. What are the challenges with autocomplete/autosuggest? Autocomplete can use previous search queries or use content that has been indexed by the search engine to provide the completion suggestions, however there are several issues which provide a bad search experience: •Completion does not take into account where the search term occurs •No relevance ranking for the suggestions •May show incorrect results when search queries are used for completion •Content based suggestions may not be accurate for large documents/web pages/websites •Manual management of autocomplete is required in most cases How can SearchAI SmartSuggest predict better search queries leading to better search queries? SearchBlox SearchAI uses SmartSuggest to predict search queries that lead to more relevant search results. SearchAI understands the search queries and how they relate to the documents using deep learning based NLP processors that read through the content and understand how they are related.
In the real world, the search is so large that we cannot enumerate the entire search engine. The digital world is so dynamic and disorganized that it has made it difficult to find an effective solution to ambiguous queries. The process of retrieval is affected by the ambiguous queries which average users type into the search engines. This is why they return too many results which can be manipulated by search engine black hat hackers. Problem: How a user can find an appropriate answer that is relevant to his/her query?
A black spot on a white sheet of paper can be found with a quick glance. What if you have to search for a black dot with certain radius among the cluster of dots on a large sheet of white paper. Such is the need of the hour where you have to intelligently search for a piece of information from a cornucopia of data in your system. Cognitive search is revolutionizing the process of retrieving the files. There is a diminishing trend of manually searching for a document stored somewhere in your system.
We are seeing more references to machine learning in how Google is ranking pages and other documents in search results. That seems to be a direction that will leave what we know as traditional, or old school signals that are referred to as ranking signals behind. It's still worth considering some of those older ranking signals because they may play a role in how things are ranked. As I was going through a new patent application from Google on ranking image search results, I decided that it was worth including what I used to look at when trying to rank images. Images can rank highly in image search, and they can also help pages that they appear upon rank higher in organic web results, because they can help make a page more relevant for the query terms that page may be optimized for.
Smyth, Barry (CLARITY: Centre for Sensor Web Technologies) | Freyne, Jill (Tasmanian ICT Centre, CSIRO) | Coyle, Maurice (HeyStaks Technologies Limited) | Briggs, Peter (HeyStaks Technologies Limited)
Recommender systems now play an important role in online information discovery, complementing traditional approaches such as search and navigation, with a more proactive approach to discovery that is informed by the users interests and preferences. To date recommender systems have been deployed within a variety of e-commerce domains, covering a range of products such as books, music, movies, and have proven to be a successful way to convert browsers into buyers. Recommendation technologies have a potentially much greater role to play in information discovery however and in this article we consider recent research that takes a fresh look at web search as a fertile platform for recommender systems research as users demand a new generation of search engines that are less susceptible to manipulation and more responsive to searcher needs and preferences.