HUMAN COMPUTER INTERFACE

Human Computer Interface

Human Computer Interface

Introduction

Recommender Systems (RS) are information search tools that have been recently proposed to cope with the “information overload” problem, i.e., the typical state of a web user, of having too much information to make a decision or remain informed about a topic. In fact, users who are approaching an E-commerce web site (e.g., Amazon) or a content web site (e.g., cnet.com or visitfinland.com) for collecting information about a product or service, or simply a topic (e.g., Lapland) could be overwhelmed by the quantity of the relevant pages and ultimately the information displayed in these web sites. In order to address this problem Recommender Systems have been proposed (Resnick and Varian, 1997, pp 56-58).

These are intelligent personalized applications that suggest products or services, or more generally speaking information “items”, that best suit the user's needs and preferences, in a given situation and context (Anand and Mobasher, 2005, pp 1-36) The core computational task of a RS is to predict the subjective evaluation a user will give to an item. This prediction is computed using a number of predictive models that have a common characteristic, i.e., they exploit the evaluations/ratings provided by user(s) for previously viewed or purchased items. Based on the particular prediction technique being employed, recommender systems have been classified into the following four main categories (Burke, 2007, pp 408): collaborative-based, content-based, knowledge-based and hybrid.

Discussion

The simplest collaborative-based systems compute correlations between users; they predict product ratings for the current user based on the ratings provided by other users, who have preferences highly correlated to the current user (Herlocker et al., 1999, Pp 230-237). Newer and more sophisticated approaches are based on matrix decomposition techniques, they try to approximate the user-item matrix, i.e., the two-dimensional matrix with entry in position (i; j) equal to the rating provided by user i to item j, as the product of two smaller matrices (Koren, 2008, Pp 426-434). Most of the entries in the original matrix are actually unknown and with this factorization a prediction is computed for all the missing values. Content-based systems use only the preferences of the current user; they predict ratings for an unseen item based on how much its description (content) is similar to items which the user has highly rated in the past (Pazzani and Billsus, 2007, Pp 325-341).

These approaches are based on information retrieval techniques (Manning, 2008, pp 110) since the item description is usually a text, and a vector (feature based) representation is derived by identifying the most relevant keywords appearing in the text. But in content-based RSs there is not any equivalent of what is a query for an IR system. In other words, the ranking produced by the system for a user is static and it represents the best (predicted) ordering of the items with respect to the relevance of the items for the user. Knowledge-based systems use a knowledge structure to make inferences about the user needs and ...