Showing papers on "Recommender system published in 2004"

Evaluating collaborative filtering recommender systems

Abstract: 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.

Item-based top-N recommendation algorithms

Abstract: The explosive growth of the world-wide-web and the emergence of e-commerce has led to the development of recommender systems---a personalized information filtering technology used to identify a set of items that will be of interest to a certain user. User-based collaborative filtering is the most successful technology for building recommender systems to date and is extensively used in many commercial recommender systems. Unfortunately, the computational complexity of these methods grows linearly with the number of customers, which in typical commercial applications can be several millions. To address these scalability concerns model-based recommendation techniques have been developed. These techniques analyze the user--item matrix to discover relations between the different items and use these relations to compute the list of recommendations.In this article, we present one such class of model-based recommendation algorithms that first determines the similarities between the various items and then uses them to identify the set of items to be recommended. The key steps in this class of algorithms are (i) the method used to compute the similarity between the items, and (ii) the method used to combine these similarities in order to compute the similarity between a basket of items and a candidate recommender item. Our experimental evaluation on eight real datasets shows that these item-based algorithms are up to two orders of magnitude faster than the traditional user-neighborhood based recommender systems and provide recommendations with comparable or better quality.

Ontological user profiling in recommender systems

Abstract: We explore a novel ontological approach to user profiling within recommender systems, working on the problem of recommending on-line academic research papers. Our two experimental systems, Quickstep and Foxtrot, create user profiles from unobtrusively monitored behaviour and relevance feedback, representing the profiles in terms of a research paper topic ontology. A novel profile visualization approach is taken to acquire profile feedback. Research papers are classified using ontological classes and collaborative recommendation algorithms used to recommend papers seen by similar people on their current topics of interest. Two small-scale experiments, with 24 subjects over 3 months, and a large-scale experiment, with 260 subjects over an academic year, are conducted to evaluate different aspects of our approach. Ontological inference is shown to improve user profiling, external ontological knowledge used to successfully bootstrap a recommender system and profile visualization employed to improve profiling accuracy. The overall performance of our ontological recommender systems are also presented and favourably compared to other systems in the literature.

Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering

Abstract: Recommender systems are being widely applied in many application settings to suggest products, services, and information items to potential consumers. Collaborative filtering, the most successful recommendation approach, makes recommendations based on past transactions and feedback from consumers sharing similar interests. A major problem limiting the usefulness of collaborative filtering is the sparsity problem, which refers to a situation in which transactional or feedback data is sparse and insufficient to identify similarities in consumer interests. In this article, we propose to deal with this sparsity problem by applying an associative retrieval framework and related spreading activation algorithms to explore transitive associations among consumers through their past transactions and feedback. Such transitive associations are a valuable source of information to help infer consumer interests and can be explored to deal with the sparsity problem. To evaluate the effectiveness of our approach, we have conducted an experimental study using a data set from an online bookstore. We experimented with three spreading activation algorithms including a constrained Leaky Capacitor algorithm, a branch-and-bound serial symbolic search algorithm, and a Hopfield net parallel relaxation search algorithm. These algorithms were compared with several collaborative filtering approaches that do not consider the transitive associations: a simple graph search approach, two variations of the user-based approach, and an item-based approach. Our experimental results indicate that spreading activation-based approaches significantly outperformed the other collaborative filtering methods as measured by recommendation precision, recall, the F-measure, and the rank score. We also observed the over-activation effect of the spreading activation approach, that is, incorporating transitive associations with past transactional data that is not sparse may "dilute" the data used to infer user preferences and lead to degradation in recommendation performance.

Shilling recommender systems for fun and profit

Abstract: Recommender systems have emerged in the past several years as an effective way to help people cope with the problem of information overload. One application in which they have become particularly common is in e-commerce, where recommendation of items can often help a customer find what she is interested in and, therefore can help drive sales. Unscrupulous producers in the never-ending quest for market penetration may find it profitable to shill recommender systems by lying to the systems in order to have their products recommended more often than those of their competitors. This paper explores four open questions that may affect the effectiveness of such shilling attacks: which recommender algorithm is being used, whether the application is producing recommendations or predictions, how detectable the attacks are by the operator of the system, and what the properties are of the items being attacked. The questions are explored experimentally on a large data set of movie ratings. Taken together, the results of the paper suggest that new ways must be used to evaluate and detect shilling attacks on recommender systems.

Trust-Aware Collaborative Filtering for Recommender Systems

Abstract: Recommender Systems allow people to find the resources they need by making use of the experiences and opinions of their nearest neighbours. Costly annotations by experts are replaced by a distributed process where the users take the initiative. While the collaborative approach enables the collection of a vast amount of data, a new issue arises: the quality assessment. The elicitation of trust values among users, termed “web of trust”, allows a twofold enhancement of Recommender Systems. Firstly, the filtering process can be informed by the reputation of users which can be computed by propagating trust. Secondly, the trust metrics can help to solve a problem associated with the usual method of similarity assessment, its reduced computability. An empirical evaluation on Epinions.com dataset shows that trust propagation can increase the coverage of Recommender Systems while preserving the quality of predictions. The greatest improvements are achieved for users who provided few ratings.

Context-Aware Recommendations in the Mobile Tourist Application COMPASS

Abstract: This paper describes the context-aware mobile tourist application COMPASS that adapts its services to the user’s needs based on both the user’s interests and his current context. In order to provide context-aware recommendations, a recommender system has been integrated with a context-aware application platform. We describe how this integration has been accomplished and how users feel about such an adaptive tourist application.

PocketLens: Toward a personal recommender system

Abstract: 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.

A collaborative filtering algorithm and evaluation metric that accurately model the user experience

Abstract: Collaborative Filtering (CF) systems have been researched for over a decade as a tool to deal with information overload. At the heart of these systems are the algorithms which generate the predictions and recommendations.In this article we empirically demonstrate that two of the most acclaimed CF recommendation algorithms have flaws that result in a dramatically unacceptable user experience.In response, we introduce a new Belief Distribution Algorithm that overcomes these flaws and provides substantially richer user modeling. The Belief Distribution Algorithm retains the qualities of nearest-neighbor algorithms which have performed well in the past, yet produces predictions of belief distributions across rating values rather than a point rating value.In addition, we illustrate how the exclusive use of the mean absolute error metric has concealed these flaws for so long, and we propose the use of a modified Precision metric for more accurately evaluating the user experience.

Using Trust in Recommender Systems: An Experimental Analysis

Abstract: Recommender systems (RS) have been used for suggesting items (movies, books, songs, etc.) that users might like. RSs compute a user similarity between users and use it as a weight for the users’ ratings. However they have many weaknesses, such as sparseness, cold start and vulnerability to attacks. We assert that these weaknesses can be alleviated using a Trust-aware system that takes into account the “web of trust” provided by every user.

Unifying collaborative and content-based filtering

Abstract: Collaborative and content-based filtering are two paradigms that have been applied in the context of recommender systems and user preference prediction. This paper proposes a novel, unified approach that systematically integrates all available training information such as past user-item ratings as well as attributes of items or users to learn a prediction function. The key ingredient of our method is the design of a suitable kernel or similarity function between user-item pairs that allows simultaneous generalization across the user and item dimensions. We propose an on-line algorithm (JRank) that generalizes perceptron learning. Experimental results on the EachMovie data set show significant improvements over standard approaches.

System and method for utilizing social networks for collaborative filtering

Abstract: A novel system and method of predicting a user's rating of a new item in a collaborative filtering system is described The invention incorporates social network information in addition to user ratings to make recommendations The distance between users in the social network is used to enhance the estimate of user similarities for collaborative filtering The social network can be constructed explicitly by users or deduced implicitly from observed interaction between users

Probabilistic memory-based collaborative filtering

Abstract: Memory-based collaborative filtering (CF) has been studied extensively in the literature and has proven to be successful in various types of personalized recommender systems. In this paper, we develop a probabilistic framework for memory-based CF (PMCF). While this framework has clear links with classical memory-based CF, it allows us to find principled solutions to known problems of CF-based recommender systems. In particular, we show that a probabilistic active learning method can be used to actively query the user, thereby solving the "new user problem." Furthermore, the probabilistic framework allows us to reduce the computational cost of memory-based CF by working on a carefully selected subset of user profiles, while retaining high accuracy. We report experimental results based on two real-world data sets, which demonstrate that our proposed PMCF framework allows an accurate and efficient prediction of user preferences.

More than the sum of its members: challenges for group recommender systems

Abstract: Systems that recommend items to a group of two or more users raise a number of challenging issues that are so far only partly understood. This paper identifies four of these issues and points out that they have been dealt with to only a limited extent in the group recommender systems that have been developed so far. The issues are especially important in settings where group members specify their preferences explicitly and where they are not able to engage in face-to-face interaction. We illustrate some of the solutions discussed with reference to the TRAVEL DECISION FORUM prototype. The issues concern (a) the design of suitable preference elicitation and aggregation methods, in particular nonmanipulable aggregation mechanisms; and (b) ways of making members aware of each other's preferences and motivational orientations, such as the use of animated representatives of group members.

A personalized system for conversational recommendations

Abstract: Searching for and making decisions about information is becoming increasingly difficult as the amount of information and number of choices increases. Recommendation systems help users find items of interest of a particular type, such as movies or restaurants, but are still somewhat awkward to use. Our solution is to take advantage of the complementary strengths of personalized recommendation systems and dialogue systems, creating personalized aides. We present a system - the ADAPTIVE PLACE ADVISOR - that treats item selection as an interactive, conversational process, with the program inquiring about item attributes and the user responding. Individual, long-term user preferences are unobtrusively obtained in the course of normal recommendation dialogues and used to direct future conversations with the same user. We present a novel user model that influences both item search and the questions asked during a conversation. We demonstrate the effectiveness of our system in significantly reducing the time and number of interactions required to find a satisfactory item, as compared to a control group of users interacting with a non-adaptive version of the system.

Application of Web usage mining and product taxonomy to collaborative recommendations in e-commerce

Abstract: The rapid growth of e-commerce has caused product overload where customers on the Web are no longer able to effectively choose the products they are exposed to To overcome the product overload of online shoppers, a variety of recommendation methods have been developed Collaborative filtering (CF) is the most successful recommendation method, but its widespread use has exposed some well-known limitations, such as sparsity and scalability, which can lead to poor recommendations This paper proposes a recommendation methodology based on Web usage mining, and product taxonomy to enhance the recommendation quality and the system performance of current CF-based recommender systems Web usage mining populates the rating database by tracking customers' shopping behaviors on the Web, thereby leading to better quality recommendations The product taxonomy is used to improve the performance of searching for nearest neighbors through dimensionality reduction of the rating database Several experiments on real e-commerce data show that the proposed methodology provides higher quality recommendations and better performance than other CF methodologies

Enhancing digital libraries with TechLens

Abstract: The number of research papers available is growing at a staggering rate. Researchers need tools to help them find the papers they should read among all the papers published each year. In this paper, we present and experiment with hybrid recommender algorithms that combine Collaborative Filtering and Content-based. Filtering to recommend research papers to users. Our hybrid algorithms combine the strengths of each filtering approach to address their individual weaknesses. We evaluated our algorithms through offline experiments on a database of 102, 000 research papers, and through an online experiment with 110 users. For both experiments we used a dataset created from the CiteSeer repository of computer science research papers. We developed separate English and Portuguese versions of the interface and specifically recruited American and Brazilian users to test for cross-cultural effects. Our results show that users value paper recommendations, that the hybrid algorithms can be successfully combined, that different algorithms are more suitable for recommending different kinds of papers, and that users with different levels of experience perceive recommendations differently These results can be applied to develop recommender systems for other types of digital libraries.

A graph model for E-commerce recommender systems

Abstract: Information overload on the Web has created enormous challenges to customers selecting products for online purchases and to online businesses attempting to identify customers' preferences efficiently. Various recommender systems employing different data representations and recommendation methods are currently used to address these challenges. In this research, we developed a graph model that provides a generic data representation and can support different recommendation methods. To demonstrate its usefulness and flexibility, we developed three recommendation methods: direct retrieval, association mining, and high-degree association retrieval. We used a data set from an online bookstore as our research test-bed. Evaluation results showed that combining product content information and historical customer transaction information achieved more accurate predictions and relevant recommendations than using only collaborative information. However, comparisons among different methods showed that high-degree association retrieval did not perform significantly better than the association mining method or the direct retrieval method in our test-bed.

TiVo: making show recommendations using a distributed collaborative filtering architecture

Abstract: We describe the TiVo television show collaborative recommendation system which has been fielded in over one million TiVo clients for four years. Over this install base, TiVo currently has approximately 100 million ratings by users over approximately 30,000 distinct TV shows and movies. TiVo uses an item-item (show to show) form of collaborative filtering which obviates the need to keep any persistent memory of each user's viewing preferences at the TiVo server. Taking advantage of TiVo's client-server architecture has produced a novel collaborative filtering system in which the server does a minimum of work and most work is delegated to the numerous clients. Nevertheless, the server-side processing is also highly scalable and parallelizable. Although we have not performed formal empirical evaluations of its accuracy, internal studies have shown its recommendations to be useful even for multiple user households. TiVo's architecture also allows for throttling of the server so if more server-side resources become available, more correlations can be computed on the server allowing TiVo to make recommendations for niche audiences.

Method and system for searching location based information on a mobile device

Abstract: A method and system for searching location based information on a mobile device is disclosed. The method and system provides for location based resource information retrieval, processing retrieved resource information based on probability of finding them in the given location, a Peer to Peer recommendation system that combines other user's real time recommendations with archived recommendations, a virtual social network that creates a dynamic network consisting of user and user's acquaintances for refining the resource information, providing a refined set of search results, by considering user's privacy choices and personal preferences.

Taxonomy-driven computation of product recommendations

Abstract: Recommender systems have been subject to an enormous rise in popularity and research interest over the last ten years At the same time, very large taxonomies for product classification are becoming increasingly prominent among e-commerce systems for diverse domains, rendering detailed machine-readable content descriptions feasible Amazoncom makes use of an entire plethora of hand-crafted taxonomies classifying books, movies, apparel, and various other goods We exploit such taxonomic background knowledge for the computation of personalized recommendations Hereby, relationships between super-concepts and sub-concepts constitute an important cornerstone of our novel approach, providing powerful inference opportunities for profile generation based upon the classification of products that customers have chosen Ample empirical analysis, both offline and online, demonstrates our proposal's superiority over common existing approaches when user information is sparse and implicit ratings prevail

Survey of Preference Elicitation Methods

Abstract: increasingly rely on interactive decision support systems to choose products and make decisions, building effective interfaces for these systems becomes more and more challenging due to the explosion of on-line information, the initial incomplete user preference and user' s cognitive and emotional limitations of information processing. How to accurately elicit user's preference thereby becomes the main concern of current decision support systems. This paper is a survey of the typical preference elicitation methods proposed by related research works, starting from the traditional utility function elicitation and analytic hierarchy process methods, to computer aided elicitation approaches which include example critiquing, needs -oriented interaction, comparison matrix, CP -network, preferences clustering & matching and collaborative filtering.

Recommender Systems Research: A Connection-Centric Survey

Abstract: Recommender systems attempt to reduce information overload and retain customers by selecting a subset of items from a universal set based on user preferences. While research in recommender systems grew out of information retrieval and filtering, the topic has steadily advanced into a legitimate and challenging research area of its own. Recommender systems have traditionally been studied from a content-based filtering vs. collaborative design perspective. Recommendations, however, are not delivered within a vacuum, but rather cast within an informal community of users and social context. Therefore, ultimately all recommender systems make connections among people and thus should be surveyed from such a perspective. This viewpoint is under-emphasized in the recommender systems literature. We therefore take a connection-oriented perspective toward recommender systems research. We posit that recommendation has an inherently social element and is ultimately intended to connect people either directly as a result of explicit user modeling or indirectly through the discovery of relationships implicit in extant data. Thus, recommender systems are characterized by how they model users to bring people together: explicitly or implicitly. Finally, user modeling and the connection-centric viewpoint raise broadening and social issues—such as evaluation, targeting, and privacy and trust—which we also briefly address.

Introduction to recommender systems: Algorithms and Evaluation

Abstract: Recommender systems use the opinions of members of a community to help individuals in that community identify the information or products most likely to be interesting to them or relevant to their needs. These systems, originally referred to as collaborative filtering systems, were developed to address two challenges that could not be addressed by existing keyword-based information filtering systems. First, they addressed the problem of overwhelming numbers of on-topic documents—ones which would all be selected by a keyword filter— by filtering based on human judgement about the quality of those documents. Second, they addressed the problem of filtering non-text documents based on human taste. For example, the Ringo system [Shardanand and Maes, 1995] applied collaborative filtering to recommend music to individuals and later research and commercial systems applied the same techniques to other art forms. Early research in this area focused largely on the ability of these systems to generate recommendations that were valued by the users of the system. And, indeed, these systems generated substantial enthusiasm and support from their users. In 1996, at the first of a series of workshops on collaborative filtering, it first became clear that some fairly simple algorithms (namely weighted knearest-neighbor algorithms applied to a sparse matrix of the ratings that users assigned to particular items or documents) worked well for several different research groups and application areas. This workshop also started using the term “Recommender Systems” and led to the publication of a special issue of Communications of the ACM on the topic (March 1997). At this point, the Recommender Systems research field diverged. Substantial commercial interest focused attention on a variety of practical questions, including the speed with which recommendations could be generated, the scale of problems that could be addressed, and the assessment of the value of recommendations to the business itself or to the customers. At the same time, a broad range of machine learning researchers (broadly defined) started applying a wide variety of techniques to recommendation problems, exploring issues of improving accuracy of algorithms, better exploiting knowledge about the

Analyzing Correlation between Trust and User Similarity in Online Communities

Abstract: Past evidence has shown that generic approaches to recommender systems based upon collaborative filtering tend to poorly scale. Moreover, their fitness for scenarios supposing distributed data storage and decentralized control, like the Semantic Web, becomes largely limited for various reasons. We believe that computational trust models bear several favorable properties for social filtering, opening new opportunities by either replacing or supplementing current techniques. However, in order to provide meaningful results for recommender system applications, we expect notions of trust to clearly reflect user similarity. In this work, we therefore provide empirical results obtained from one real, operational community and verify latter hypothesis for the domain of book recommendations.

System and method for influencing recommender system

Abstract: A system and method for implementing/influencing a recommender system which provides recommendations to users based on characteristics of certain trendsetters within a member population. The trendsetters are determined by studying historical adoption behavior of a group within the member population, or by reference to known indicia.

A scalable P2P recommender system based on distributed collaborative filtering

Abstract: Collaborative Filtering (CF) technique has been proved to be one of the most successful techniques in recommender systems in recent years. However, most existing CF based recommender systems worked in a centralized way and suffered from its shortage in scalability as their calculation complexity increased quickly both in time and space when the record in user database increases. In this article, we first propose a distributed CF algorithm called PipeCF together with two novel approaches: significance refinement and unanimous amplification, to further improve the scalability and prediction accuracy. We then show how to implement this algorithm on a Peer-to-Peer (P2P) structure through distributed hash table method, which is the most popular and efficient P2P routing algorithm, to construct a scalable distributed recommender system. The experimental data show that the distributed CF-based recommender system has much better scalability than traditional centralized ones with comparable prediction efficiency and accuracy. q 2004 Elsevier Ltd. All rights reserved.

A personalized recommender system for the cosmetic business

Abstract: In order to have an effective command of the relationship between customers and products, we have constructed a personalized recommender system which incorporates content-based, collaborative filtering, and data mining techniques. We have also introduced a new scoring approach to determine customers' interest scores on products. To demonstrate how our system works, we used it to analyze real cosmetic data and generate a recommender score table for sellers to refer to. After tracking its performance for 1 year, we have obtained quite impressive results.