This chapter presents an evaluation of the proposed image mining systems via average precision-recall curves of proposed image retrieval systems for Pascal database, average precision of top-ranked results after the ninth feedback for the Corel database, average recall of top-ranked results after the ninth feedback for the Corel database, average precision of proposed methods for different semantic classes for the Pascal database, average recall of proposed methods for different semantic classes for the Pascal database, average precision of top-ranked results after the ninth feedback for information retrieval (IR) with summarization and IR without summarization for the Pascal database, average execution time of proposed methods (in seconds), and performance analysis of top retrieval results obtained with the proposed image retrieval systems. Average The experiment on Corel and Vistex image database …

Recommender systems have been evaluated in many, often incomparable, ways. In this article, we review the key decisions in evaluating collaborative filtering recommender systems: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole. In addition to reviewing the evaluation strategies used by prior researchers, we present empirical results from the analysis of various accuracy metrics on one content domain where all the tested metrics collapsed roughly into three equivalence classes. Metrics within each equivalency class were strongly correlated, while metrics from different equivalency classes were uncorrelated.

This paper presents the SERF (System for Electronic Recommendation Filtering) which is a collaborative filtering system that recommends context-sensitive, high-quality information sources for document search. Collaborative filtering systems remove the limitation of traditional content-based search by using individual's ratings to evaluate and recommend information sources. SERF uses collaborative filtering algorithms to predict the relevance and quality of each document with respect to each particular user and their specific information need. In our system, users specify their need in the form of a natural language query, and are provided with recommended documents based on ratings by other users with similar questions. Preliminary experiments show that the collaborative filtering recommendations increase the efficiency of the document search process. We also discuss some key challenges of designing a collaborative filtering system for document search.

A technique that correlates database items to a task adds content-independent context to a recommender system based solely on user interest ratings. In this article, we present a task-focused approach to recommendation that is entirely independent of the type of content involved. The approach leverages robust, high-performance, commercial software. We have implemented it in a live movie recommendation site and validated it with empirical results from user studies.

Automated collaborative ltering (ACF) systems predict a person's a nity for unexperienced items based on the past experiences of that person and the past and current experiences of a community of people. ACF systems have been successful in research, with projects such as GroupLens[7], Ringo[10], and Video Recommender[4] gaining large followings on the Internet. Commercially, some of the highest pro le web sites like Amazon.com, CDNow.com, and MovieFinder.com have made successful use of ACF technology. While automated collaborative ltering systems have proven to be generally accurate, their failure rates still remain unacceptable for certain domains or individuals. While a user may be willing to risk purchasing a music CD based on the recommendation of an ACF system, he will probably not risk choosing a honeymoon vacation spot based on such a recommendation. However, there is no reason why the higherrisk domains should not bene t from ACF technology. There are several key problems obstructing the development of trust in an automated collaborative ltering system as a decision aid. The primary problem is that current ACF systems are stochastic processes and will make mistakes from time to time, no matter how well implemented. A secondary problem is that ACF systems are black boxes, computerized oracles that give advice, but cannot be questioned. A user has no feeling when to trust a recommendation and when to doubt a recommendation. These problems can prevent acceptance of ACF systems as decision support aids. Explanation capabilities provide a solution to building trust and may also improve the decision-making performance of automated collaborative ltering systems. An explanation behind the reasoning of a recommendation provides transparency into the workings of the ACF system. Users will be more likely to trust a recommendation when they know the reasons behind that recommendation. Explanations will help users understand the process of ACF, and know where its strengths and weaknesses are. Research is necessary to determine how e ective explanation facilities will be with ACF systems, and what is the proper way to implement them. The remainder of this statement describes our plans for user experiments related to the development of explanation facilities in ACF systems. We begin by describing the errors that are introduced into a automated collaborative ltering systems.

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Information filtering agents and collaborative filtering both attempt to alleviate information overload by identifying which items a user will find worthwhile. Information filtering (IF) focuses on the analysis of item content and the development of a personal user interest profile. Collaborative filtering (CF) focuses on identification of other users with similar tastes and the use of their opinions to recommend items. Each technique has advantages and limitations that suggest that the two could be beneficially combined.This paper shows that a CF framework can be used to combine personal IF agents and the opinions of a community of users to produce better recommendations than either agents or users can produce alone. It also shows that using CF to create a personal combination of a set of agents produces better results than either individual agents or other combination mechanisms. One key implication of these results is that users can avoid having to select among agents; they can use them all and let the CF framework select the best ones for them.

When information is abundant, the knowledge of which information is useful and valuable matters most. We all use our network of family, friends, and colleagues to recommend movies, books, cars, and news articles. Collaborative filtering technology automates the process of sharing opinions on the relevance and duality of information. Collaborative filtering is one technique among many information filtering techniques that range from unfiltered to personalized and from effortless to laborious. Libraries or the Web are good examples of unfiltered information sources. E-mail directed to one recipient is a good example of a filtered information source. A best-seller list requires little effort fur the user, but provides the same recommendations to all users. Filters based on demographics, such as age, sex, or marital status, require some effort from the user in providing the demographics, and provide some level of personal filtering, so they are near the middle of the chart. Collaborative filtering requires relatively little effort from the user, and provides individually targeted recommendations, so it is in the upper right of the chart. Effort, of course, can be reduced via automation. While collaborative filtering is not necessarily effortless, it requires a relatively small amount of effort on the part of the user and provides very individualized recommendations. The collaborative filtering systems that we discuss here each offer a high degree of personalization, but each system takes a different approach to automation, attempting to find the best trade-off between the amount of work the users must put into the system and the perceived value and benefits they receive in return.

newsgroups carry a wide enough spread of messages to make most individuals consider Usenet news to be a high noise information resource. Furthermore, each user values a different set of messages. Both taste and prior knowledge are major factors in evaluating news articles. For example, readers of the rec.humor newsgroup, a group designed for jokes and other humorous postings, value articles based on whether they perceive them to be funny. Readers of technical groups, such as comp.lang.c11 value articles based on interest and usefulness to them—introductory questions and answers may be uninteresting to an expert C11 programmer just as debates over subtle and advanced language features may be useless to the novice. The combination of high volume and personal taste made Usenet news a promising candidate for collaborative filtering. More formally, we determined the potential predictive utility for Usenet news was very high. The GroupLens project started in 1992 and completed a pilot study at two sites to establish the feasibility of using collaborative filtering for Usenet news [8]. Several critical design decisions were made as part of that pilot study, including:

newsgroups carry a wide enough spread of messages to make most individuals consider Usenet news to be a high noise information resource. Furthermore, each user values a different set of messages. Both taste and prior knowledge are major factors in evaluating news articles. For example, readers of the rec.humor newsgroup, a group designed for jokes and other humorous postings, value articles based on whether they perceive them to be funny. Readers of technical groups, such as comp.lang.c11 value articles based on interest and usefulness to them—introductory questions and answers may be uninteresting to an expert C11 programmer just as debates over subtle and advanced language features may be useless to the novice. The combination of high volume and personal taste made Usenet news a promising candidate for collaborative filtering. More formally, we determined the potential predictive utility for Usenet news was very high. The GroupLens project started in 1992 and completed a pilot study at two sites to establish the feasibility of using collaborative filtering for Usenet news [8]. Several critical design decisions were made as part of that pilot study, including:

NR is a point and click GUI interface for browsing Usenet news. The NR interface is built using the Tk interface toolkit, and coded entirely in the Tcl scripting language. NR was designed as a framework for pursuing research into electronic information browsing. As a result, NR had strong requirements for configurability, extensibility, portability, and performance. This paper describes how those requirements were met through the use of features and extensions of the Tcl/Tk scripting language. The paper also describes some of the information filtering technologies implemented in NR.

This paper discusses the design and implementation of a command stream based on Tcl. A command stream is a series of arbitrary commands that can be tightly synchronized with other media in a distributed multimedia presentation. In TclStream, we represent an arbitrary command as a collection of fragments of Tcl code. The command stream medium supports the standard manipulation functions of multimedia environments: reverse, fast-forward, random access, and variable speed. The ability to specify arbitrary actions, combined with fine playback control, make TclStream an extremely flexible and powerful presentation medium.