This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multicriteria ratings, and a provision of more flexible and less intrusive types of recommendations.

/pdf/toward-the-next-generation-of-recommender-systems-a-survey-4wwsv77rns.pdf

Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions

Recommender systems apply knowledge discovery techniques to the problem of making personalized recommendations for information, products or services during a live interaction. These systems, especially the k-nearest neighbor collaborative ltering based ones, are achieving widespread success on the Web. The tremendous growth in the amount of available information and the number of visitors to Web sites in recent years poses some key challenges for recommender systems. These are: producing high quality recommendations, performing many recommendations per second for millions of users and items and achieving high coverage in the face of data sparsity. In traditional collaborative ltering systems the amount of work increases with the number of participants in the system. New recommender system technologies are needed that can quickly produce high quality recommendations, even for very large-scale problems. To address these issues we have explored item-based collaborative ltering techniques. Item-based techniques rst analyze the user-item matrix to identify relationships between di erent items, and then use these relationships to indirectly compute recommendations for users. In this paper we analyze di erent item-based recommendation generation algorithms. We look into di erent techniques for computing item-item similarities (e.g., item-item correlation vs. cosine similarities between item vectors) and di erent techniques for obtaining recommendations from them (e.g., weighted sum vs. regression model). Finally, we experimentally evaluate our results and compare them to the basic k-nearest neighbor approach. Our experiments suggest that item-based algorithms provide dramatically better performance than user-based algorithms, while at the same time providing better quality than the best available userbased algorithms.

/pdf/item-based-collaborative-filtering-recommendation-algorithms-55n6bcyed4.pdf

Item-based collaborative filtering recommendation algorithms

PROBLEM TO BE SOLVED: To solve the problem, wherein it is impossible for an electronic content information provider to provide commercially secure and effective method, for a configurable general-purpose electronic commercial transaction/distribution control system. SOLUTION: In this system, having at least one protected processing environment for safely controlling at least one portion of decoding of digital information, a secure content distribution method comprises a process for encapsulating digital information in one or more digital containers; a process for encrypting at least a portion of digital information; a process for associating at least partially secure control information for managing interactions with encrypted digital information and/or digital container; a process for delivering one or more digital containers to a digital information user; and a process for using a protected processing environment, for safely controlling at least a portion of the decoding of the digital information. COPYRIGHT: (C)2006,JPO&NCIPI

https://apps.dtic.mil/dtic/tr/fulltext/u2/a423264.pdf

Systems and Methods for Secure Transaction Management and Electronic Rights Protection

I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

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.

/pdf/evaluating-collaborative-filtering-recommender-systems-au0a9p6z94.pdf

Evaluating collaborative filtering recommender systems

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:

GroupLens: applying collaborative filtering to Usenet news

Recommender systems have changed the way people shop online. Recommender systems on wireless mobile devices may have the same impact on the way people shop in stores. We present our experience with implementing a recommender system on a PDA that is occasionally connected to the network. This interface helps users of the MovieLens movie recommendation service select movies to rent, buy, or see while away from their computer. The results of a nine month field study show that although there are several challenges to overcome, mobile recommender systems have the potential to provide value to their users today

/pdf/movielens-unplugged-experiences-with-an-occasionally-leo9p4rzh0.pdf

MovieLens unplugged: experiences with an occasionally connected recommender system

Recommender systems using collaborative filtering are a popular technique for reducing information overload and finding products to purchase. One limitation of current recommenders is that they are not portable. They can only run on large computers connected to the Internet. A second limitation is that they require the user to trust the owner of the recommender with personal preference data. Personal recommenders hold the promise of delivering high quality recommendations on palmtop computers, even when disconnected from the Internet. Further, they can protect the user's privacy by storing personal information locally, or by sharing it in encrypted form. In this article we present the new PocketLens collaborative filtering algorithm along with five peer-to-peer architectures for finding neighbors. We evaluate the architectures and algorithms in a series of offline experiments. These experiments show that Pocketlens can run on connected servers, on usually connected workstations, or on occasionally connected portable devices, and produce recommendations that are as good as the best published algorithms to date.

PocketLens: Toward a personal recommender system

An electronic information system is provided for determining predictive utility of prediction techniques in ascertaining which items are valued. The information system includes an input mechanism for obtaining a set of measurements of actual value of items presented to a user. The items presented to the user are derived from a plurality of items by utilizing predictions from a prediction technique. A processor is operatively coupled to the input mechanism to generate a quality metric receiver operating curve (ROC) from the set of measurements which corresponds to a measure of effectiveness of the prediction technique in predicting which items are valued by the user. In addition, a method is provided for operating this electronic information system. Also, a storage device readable by a computer system for implementing this method is provided.

System, method and article of manufacture for using receiver operating curves to evaluate predictive utility

A prediction information system for use between a user and an electronic rating broker is provided. The prediction information system includes a user interface for presenting items as information to the user. An input mechanism receives data input by the user. A processor in cooperation with the user interface and the input mechanism performs an algorithm for: obtaining a particular implicit measure, generating an implicit rating as a function of the particular implicit measure, submitting the implicit rating to the rating broker, receiving prediction results for other items from the rating broker, and presenting the prediction results to the user through the user interface. In addition, a method for operating an electronic rating broker on a computer system in order to provide prediction information to a user based on implicit ratings is provided. Also, a storage device readable by a computer system for implementing this method is provided.

Bradley N. Miller

Papers

GroupLens: applying collaborative filtering to Usenet news

MovieLens unplugged: experiences with an occasionally connected recommender system

PocketLens: Toward a personal recommender system

System, method and article of manufacture for using receiver operating curves to evaluate predictive utility

System, method, and article of manufacture for utilizing implicit ratings in collaborative filters