Search, recommendation
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Despite the fact that site search often receives the most traffic, it’s also the place where the user experience designer bears the least influence. Few tools exist to appraise the quality of the search experience, much less strategize ways to improve it. When it comes to site search, user experience designers are often sidelined like the single person at an old flame’s wedding: Everything seems to be moving along without you, and if you slipped out halfway through, chances are no one would notice. But relevancy testing and precision testing offer hope.
A List Apart: Articles: Testing Search for Relevancy and Precision
Findability and Exploration: the future of search
The majority of people visiting a news website don’t care about the front page.
Recommender systems or recommendation systems (sometimes replacing "system" with a synonym such as platform or engine) are a subclass of information filtering system that seek to predict the 'rating' or 'preference' that a user would give to an item (such as music , books , or movies ) or social element (e.g. people or groups ) they had not yet considered, using a model built from the characteristics of an item (content-based approaches) or the user's social environment (collaborative filtering approaches) [ 1 ] [ 2 ] . Recommender systems have become extremely common in recent years. A few examples of such systems: When viewing a product on Amazon.com , the store will recommend additional items based on a matrix of what other shoppers bought along with the currently-selected item [ 3 ] . Pandora Radio takes an initial input of a song or musician and plays music with similar characteristics (based on a series of keywords attributed to the inputted artist or piece of music).
Recommender system - Wikipedia, the free encyclopedia
The Recommendation Engine
Next: Experimental Evaluation Up: A Framework for Personalization Previous: Data Preparation and Pattern The recommendation engine takes a collection of frequent itemsets as input and generates a recommendation set for a user by matching the current user's activity against the discovered patterns. The recommendation engine is on-line process, therefore its efficiency and scalability are of paramount importance.
The Ultra Gleeper: a Recommendation Engine for Web Pages
Recommendation engines enjoyed a vogue in the mid-90s. They would solve the problem of information overload by matching user preferences against a large universe of data. The ultimate realization of this strategy would be a recommendation engine capable of mining that Northwest territory of data, the World Wide Web. Recommendation engines were built and run into troubles.Current version: 1.0, 11/15/2000 SUGGEST is a Top - N recommendation engine that implements a variety of recommendation algorithms.
SUGGEST: Recommendation Engine | Karypis Lab
Nearest neighbor search - Wikipedia, the free encyclopedia
Nearest neighbor search ( NNS ), also known as proximity search , similarity search or closest point search , is an optimization problem for finding closest points in metric spaces .
k-nearest neighbor algorithm - Wikipedia, the free encyclopedia
In pattern recognition , the k -nearest neighbor algorithm ( k -NN) is a method for classifying objects based on closest training examples in the feature space . k -NN is a type of instance-based learning , or lazy learning where the function is only approximated locally and all computation is deferred until classification. The k -nearest neighbor algorithm is amongst the simplest of all machine learning algorithms: an object is classified by a majority vote of its neighbors, with the object being assigned to the class most common amongst its k nearest neighbors ( k is a positive integer , typically small). If k = 1, then the object is simply assigned to the class of its nearest neighbor.
Also called guided navigation and faceted search , the faceted navigation model leverages metadata fields and values to provide users with visible options for clarifying and refining queries.
A List Apart: Articles: Design Patterns: Faceted Navigation
Understanding of your site visitors’ intent is one of the most delightful parts of web data analysis. In this article, we’ll learn five ways to analyze your internal site-search data—data that’s easy to get, to understand, and to act on. But let’s take a step back. Why should you care about this in the first place?
