Showing papers on "Recommender system published in 2002"

Hybrid Recommender Systems: Survey and Experiments

Abstract: Recommender systems represent user preferences for the purpose of suggesting items to purchase or examine They have become fundamental applications in electronic commerce and information access, providing suggestions that effectively prune large information spaces so that users are directed toward those items that best meet their needs and preferences A variety of techniques have been proposed for performing recommendation, including content-based, collaborative, knowledge-based and other techniques To improve performance, these methods have sometimes been combined in hybrid recommenders This paper surveys the landscape of actual and possible hybrid recommenders, and introduces a novel hybrid, EntreeC, a system that combines knowledge-based recommendation and collaborative filtering to recommend restaurants Further, we show that semantic ratings obtained from the knowledge-based part of the system enhance the effectiveness of collaborative filtering

Methods and metrics for cold-start recommendations

Abstract: We have developed a method for recommending items that combines content and collaborative data under a single probabilistic framework. We benchmark our algorithm against a naive Bayes classifier on the cold-start problem, where we wish to recommend items that no one in the community has yet rated. We systematically explore three testing methodologies using a publicly available data set, and explain how these methods apply to specific real-world applications. We advocate heuristic recommenders when benchmarking to give competent baseline performance. We introduce a new performance metric, the CROC curve, and demonstrate empirically that the various components of our testing strategy combine to obtain deeper understanding of the performance characteristics of recommender systems. Though the emphasis of our testing is on cold-start recommending, our methods for recommending and evaluation are general.

Content-boosted collaborative filtering for improved recommendations

Abstract: Most recommender systems use Collaborative Filtering or Content-based methods to predict new items of interest for a user. While both methods have their own advantages, individually they fail to provide good recommendations in many situations. Incorporating components from both methods, a hybrid recommender system can overcome these shortcomings. In this paper, we present an elegant and effective framework for combining content and collaboration. Our approach uses a content-based predictor tc enhance existing user data, and then provides personalized suggestions through collaborative filtering. We present experimental results that show how this approach, Content-Boosted Collaborative Filtering, performs better than a pure content-based predictor, pure collaborative filter, and a naive hybrid approach.

An Empirical Analysis of Design Choices in Neighborhood-Based Collaborative Filtering Algorithms

Abstract: Collaborative filtering systems predict a user's interest in new items based on the recommendations of other people with similar interests. Instead of performing content indexing or content analysis, collaborative filtering systems rely entirely on interest ratings from members of a participating community. Since predictions are based on human ratings, collaborative filtering systems have the potential to provide filtering based on complex attributes, such as quality, taste, or aesthetics. Many implementations of collaborative filtering apply some variation of the neighborhood-based prediction algorithm. Many variations of similarity metrics, weighting approaches, combination measures, and rating normalization have appeared in each implementation. For these parameters and others, there is no consensus as to which choice of technique is most appropriate for what situations, nor how significant an effect on accuracy each parameter has. Consequently, every person implementing a collaborative filtering system must make hard design choices with little guidance. This article provides a set of recommendations to guide design of neighborhood-based prediction systems, based on the results of an empirical study. We apply an analysis framework that divides the neighborhood-based prediction approach into three components and then examines variants of the key parameters in each component. The three components identified are similarity computation, neighbor selection, and rating combination.

Getting to know you: learning new user preferences in recommender systems

Abstract: Recommender systems have become valuable resources for users seeking intelligent ways to search through the enormous volume of information available to them. One crucial unsolved problem for recommender systems is how best to learn about a new user. In this paper we study six techniques that collaborative filtering recommender systems can use to learn about new users. These techniques select a sequence of items for the collaborative filtering system to present to each new user for rating. The techniques include the use of information theory to select the items that will give the most value to the recommender system, aggregate statistics to select the items the user is most likely to have an opinion about, balanced techniques that seek to maximize the expected number of bits learned per presented item, and personalized techniques that predict which items a user will have an opinion about. We study the techniques thru offline experiments with a large pre-existing user data set, and thru a live experiment with over 300 users. We show that the choice of learning technique significantly affects the user experience, in both the user effort and the accuracy of the resulting predictions.

Collaborative filtering with privacy via factor analysis

Abstract: Collaborative filtering (CF) is valuable in e-commerce, and for direct recommendations for music, movies, news etc. But today's systems have several disadvantages, including privacy risks. As we move toward ubiquitous computing, there is a great potential for individuals to share all kinds of information about places and things to do, see and buy, but the privacy risks are severe. In this paper we describe a new method for collaborative filtering which protects the privacy of individual data. The method is based on a probabilistic factor analysis model. Privacy protection is provided by a peer-to-peer protocol which is described elsewhere, but outlined in this paper. The factor analysis approach handles missing data without requiring default values for them. We give several experiments that suggest that this is most accurate method for CF to date. The new algorithm has other advantages in speed and storage over previous algorithms. Finally, we suggest applications of the approach to other kinds of statistical analyses of survey or questionaire data.

The role of transparency in recommender systems

Abstract: Recommender Systems act as a personalized decision guides, aiding users in decisions on matters related to personal taste. Most previous research on Recommender Systems has focused on the statistical accuracy of the algorithms driving the systems, with little emphasis on interface issues and the user's perspective. The goal of this research was to examine the role of transprency (user understanding of why a particular recommendation was made) in Recommender Systems. To explore this issue, we conducted a user study of five music Recommender Systems. Preliminary results indicate that users like and feel more confident about recommendations that they perceive as transparent.

A personalized recommender system based on web usage mining and decision tree induction

Abstract: A personalized product recommendation is an enabling mechanism to overcome information overload occurred when shopping in an Internet marketplace. Collaborative filtering has been known to be one of the most successful recommendation methods, but its application to e-commerce has exposed well-known limitations such as sparsity and scalability, which would lead to poor recommendations. This paper suggests a personalized recommendation methodology by which we are able to get further effectiveness and quality of recommendations when applied to an Internet shopping mall. The suggested methodology is based on a variety of data mining techniques such as web usage mining, decision tree induction, association rule mining and the product taxonomy. For the evaluation of the methodology, we implement a recommender system using intelligent agent and data warehousing technologies.

Discovery and Evaluation of Aggregate Usage Profiles for Web Personalization

Abstract: Web usage mining, possibly used in conjunction with standard approaches to personalization such as collaborative filtering, can help address some of the shortcomings of these techniques, including reliance on subjective user ratings, lack of scalability, and poor performance in the face of high-dimensional and sparse data However, the discovery of patterns from usage data by itself is not sufficient for performing the personalization tasks The critical step is the effective derivation of good quality and useful (ie, actionable) “aggregate usage profiles” from these patterns In this paper we present and experimentally evaluate two techniques, based on clustering of user transactions and clustering of pageviews, in order to discover overlapping aggregate profiles that can be effectively used by recommender systems for real-time Web personalization We evaluate these techniques both in terms of the quality of the individual profiles generated, as well as in the context of providing recommendations as an integrated part of a personalization engine In particular, our results indicate that using the generated aggregate profiles, we can achieve effective personalization at early stages of users' visits to a site, based only on anonymous clickstream data and without the benefit of explicit input by these users or deeper knowledge about them

Efficient Adaptive-Support Association Rule Mining for Recommender Systems

Abstract: Collaborative recommender systems allow personalization for e-commerce by exploiting similarities and dissimilarities among customers' preferences We investigate the use of association rule mining as an underlying technology for collaborative recommender systems Association rules have been used with success in other domains However, most currently existing association rule mining algorithms were designed with market basket analysis in mind Such algorithms are inefficient for collaborative recommendation because they mine many rules that are not relevant to a given user Also, it is necessary to specify the minimum support of the mined rules in advance, often leading to either too many or too few ruless this negatively impacts the performance of the overall system We describe a collaborative recommendation technique based on a new algorithm specifically designed to mine association rules for this purpose Our algorithm does not require the minimum support to be specified in advance Rather, a target range is given for the number of rules, and the algorithm adjusts the minimum support for each user in order to obtain a ruleset whose size is in the desired range Rules are mined for a specific target user, reducing the time required for the mining process We employ associations between users as well as associations between items in making recommendations Experimental evaluation of a system based on our algorithm reveals performance that is significantly better than that of traditional correlation-based approaches

On the recommending of citations for research papers

Abstract: Collaborative filtering has proven to be valuable for recommending items in many different domains. In this paper, we explore the use of collaborative filtering to recommend research papers, using the citation web between papers to create the ratings matrix. Specifically, we tested the ability of collaborative filtering to recommend citations that would be suitable additional references for a target research paper. We investigated six algorithms for selecting citations, evaluating them through offline experiments against a database of over 186,000 research papers contained in ResearchIndex. We also performed an online experiment with over 120 users to gauge user opinion of the effectiveness of the algorithms and of the utility of such recommendations for common research tasks. We found large differences in the accuracy of the algorithms in the offline experiment, especially when balanced for coverage. In the online experiment, users felt they received quality recommendations, and were enthusiastic about the idea of receiving recommendations in this domain.

A graph-based recommender system for digital library

Abstract: Research shows that recommendations comprise a valuable service for users of a digital library [11] While most existing recommender systems rely either on a content-based approach or a collaborative approach to make recommendations, there is potential to improve recommendation quality by using a combination of both approaches (a hybrid approach) In this paper, we report how we tested the idea of using a graph-based recommender system that naturally combines the content-based and collaborative approaches Due to the similarity between our problem and a concept retrieval task, a Hopfield net algorithm was used to exploit high-degree book-book, user-user and book-user associations Sample hold-out testing and preliminary subject testing were conducted to evaluate the system, by which it was found that the system gained improvement with respect to both precision and recall by combining content-based and collaborative approaches However, no significant improvement was observed by exploiting high-degree associations

An MDP-based recommender system

Abstract: Typical Recommender systems adopt a static view of the recommendation process and treat it as a prediction problem. We argue that it is more appropriate to view the problem of generating recommendations as a sequential decision problem and, consequently, that Markov decision processes (MDP) provide a more appropriate model for Recommender systems. MDPs introduce two benefits: they take into account the long-term effects of each recommendation, and they take into account the expected value of each recommendation. To succeed in practice, an MDP-based Recommender system must employ a strong initial model; and the bulk of this paper is concerned with the generation of such a model. In particular, we suggest the use of an n-gram predictive model for generating the initial MDP. Our n-gram model induces a Markovchain model of user behavior whose predictive accuracy is greater than that of existing predictive models. We describe our predictive model in detail and evaluate its performance on real data. In addition, we show how the model can be used in an MDP-based Recommender system.

Using sequential and non-sequential patterns in predictive Web usage mining tasks

Abstract: We describe an efficient framework for Web personalization based on sequential and non-sequential pattern discovery from usage data. Our experimental results performed on real usage data indicate that more restrictive patterns, such as contiguous sequential patterns (e.g., frequent navigational paths) are more suitable for predictive tasks, such as Web prefetching, (which involve predicting which item is accessed next by a user), while less constrained patterns, such as frequent item sets or general sequential patterns are more effective alternatives in the context of Web personalization and recommender systems.

Intelligent systems for tourism

Abstract: The authors discuss travel recommender systems, adaptive context aware mobility support for tourists, tourism information systems, information delivery and travel planning information gathering agents.

Competitive recommendation systems

Abstract: A recommendation system tracks past purchases of a group of users to make product recommendations to individual members of the group. In this paper we present a notion of competitive recommendation systems, building on recent theoretical work on this subject. We reduce the problem of achieving competitiveness to a problem in matrix reconstruction. We then present a matrix reconstruction scheme that is competitive: it requires a small overhead in the number of users and products to be sampled, delivering in the process a net utility that closely approximates the best possible with full knowledge of all user-product preferences.

Exploiting Synergy Between Ontologies and Recommender Systems

Abstract: Recommender systems learn about user preferences over time, automatically finding things of similar interest. This reduces the burden of creating explicit queries. Recommender systems do, however, suffer from cold-start problems where no initial information is available early on upon which to base recommendations. Semantic knowledge structures, such as ontologies, can provide valuable domain knowledge and user information. However, acquiring such knowledge and keeping it up to date is not a trivial task and user interests are particularly difficult to acquire and maintain. This paper investigates the synergy between a web-based research paper recommender system and an ontology containing information automatically extracted from departmental databases available on the web. The ontology is used to address the recommender systems cold-start problem. The recommender system addresses the ontology's interest-acquisition problem. An empirical evaluation of this approach is conducted and the performance of the integrated systems measured.

Recommender systems using linear classifiers

Abstract: Recommender systems use historical data on user preferences and other available data on users (for example, demographics) and items (for example, taxonomy) to predict items a new user might like. Applications of these methods include recommending items for purchase and personalizing the browsing experience on a web-site. Collaborative filtering methods have focused on using just the history of user preferences to make the recommendations. These methods have been categorized as memory-based if they operate over the entire data to make predictions and as model-based if they use the data to build a model which is then used for predictions. In this paper, we propose the use of linear classifiers in a model-based recommender system. We compare our method with another model-based method using decision trees and with memory-based methods using data from various domains. Our experimental results indicate that these linear models are well suited for this application. They outperform a commonly proposed memory-based method in accuracy and also have a better tradeoff between off-line and on-line computational requirements.

Exploiting synergy between ontologies and recommender systems

Abstract: Recommender systems learn about user preferences over time, automatically finding things of similar interest. This reduces the burden of creating explicit queries. Recommender systems do, however, suffer from cold-start problems where no initial information is available early on upon which to base recommendations. Semantic knowledge structures, such as ontologies, can provide valuable domain knowledge and user information. However, acquiring such knowledge and keeping it up to date is not a trivial task and user interests are particularly difficult to acquire and maintain. This paper investigates the synergy between a web-based research paper recommender system and an ontology containing information automatically extracted from departmental databases available on the web. The ontology is used to address the recommender systems cold-start problem. The recommender system addresses the ontology's interest-acquisition problem. An empirical evaluation of this approach is conducted and the performance of the integrated systems measured.

A Graph Based Approach to Extract a Neighborhood Customer Community for Collaborative Filtering

Abstract: In E-commerce sites, recommendation systems are used to recommend products to their customers. Collaborative filtering (CF) is widely employed approach to recommend products. In the literature, researchers are making efforts to improve the scalability and online performance of CF. In this paper we propose a graph based approach to improve the performance of CF. We abstract a neighborhood community of a given customer through dense bipartite graph (DBG). Given a data set of customer preferences, a group of neighborhood customers for a given customer is extracted by extracting corresponding DBG. The experimental results on the MovieLens data set show that the recommendation made with the proposed approach matches closely with the recommendation of CF. The proposed approach possesses a potential to adopt to frequent changes in the product preference data set.

Intelligent agent-based systems for personalized recommendations in Internet commerce

Abstract: The prosperity of electronic commerce has changed the traditional trading behaviors and more and more people are willing to conduct Internet shopping. However, the exponentially increasing information provided by the Internet enterprises causes the problem of overloaded information, and this inevitably reduces the customer's satisfaction and loyalty. One way to overcome such a problem is to build personalized recommender systems to retrieve product information that really interests the customers. For products that people may purchase relatively often, such as books and CDs, recommender systems can be built to reason about a customer's personal preferences from his purchasing history and then provide the most appropriate information services to meet his needs. On the other hand, for those commodities a general customer does not buy frequently, for example computers and home theater systems, more appropriate are the kinds of recommender systems able to retrieve optimal products based on the customer's current preferences obtained from the iterative system–customer interactions. This paper presents the above two kinds of recommender systems we have developed for supporting Internet commerce. Experimental results show the promise of our systems.

ITR: A Case-Based Travel Advisory System

Abstract: This paper presents a web based recommender system aimed at supporting a user in information filtering and product bundling. The system enables the selection of travel locations, activities and attractions, and supports the bundling of a personalized travel plan. A travel plan is composed in a mixed initiative way: the user poses queries and the recommender exploits an innovative technology that helps the user, when needed, to reformulate the query. Travel plans are stored in a memory of cases, which is exploited for ranking travel items extracted from catalogues. A new 'collaborative' approach is introduced, where user past behavior similarity is replaced with session (travel plan) similarity.

Meta-recommendation systems: user-controlled integration of diverse recommendations

Abstract: In a world where the number of choices can be overwhelming, recommender systems help users find and evaluate items of interest. They do so by connecting users with information regarding the content of recommended items or the opinions of other individuals. Such systems have become powerful tools in domains such as electronic commerce, digital libraries, and knowledge management. In this paper, we address such systems and introduce a new class of recommender system called meta-recommenders. Meta-recommenders provide users with personalized control over the generation of a single recommendation list formed from a combination of rich data using multiple information sources and recommendation techniques. We discuss experiments conducted to aid in the design of interfaces for a meta-recommender in the domain of movies. We demonstrate that meta-recommendations fill a gap in the current design of recommender systems. Finally, we consider the challenges of building real-world, usable meta-recommenders across a variety of domains.

Method for rating items within a recommendation system based on additional knowledge of item relationships

Abstract: A computerized method and corresponding means for rating an item within a recommendation system, that exploits additional external knowledge or the relationships between the ratable items to implicitly derive, from a first item rated explicitly by a certain user, implicit ratings for items related to the explicitly rated item. In response to a first explicit rating for a first item, the following steps are performed: determining, for the first item, a first set related items based on a predefined item relationship; storing, within the recommendation system, the first explicit rating of the first item; and storing, within the recommendation system, first implicit ratings for the first set of related items.

Comparison-Based Recommendation

Abstract: Recommender systems combine user profiling and filtering techniques to provide more pro-active and personal information retrieval systems, and have been gaining in popularity as a way of overcoming the ubiquitous information overload problem. Many recommender systems operate as interactive systems that seek feedback from the end-user as part of the recommendation process to revise the user's query. In this paper we examine different forms of feedback that have been used in the past and focus on a low-cost preference-based feedback model, which to date has been very much under utilised. In particular we describe and evaluate a novel comparison-based recommendation framework which is designed to utilise preference-based feedback. Specifically, we present results that highlight the benefits of a number of new query revision strategies and evidence to suggest that the popular more-like-this strategy may be flawed.

Enabling a recommendation system to provide user-to-user recommendations

Abstract: A computerized method and corresponding means for rating an item within a recommendation system. In a recommendation scheme, each of a multitude of users U and each of a multitude of items I is included in a profile P(U,I) that comprises ratings. Based on the similarity between a given user and the multitude of users in terms of the ratings, a subset of users is selected who have interest similar to those of the given user.

Method and apparatus for generating recommendations based on user preferences and environmental characteristics

Abstract: A recommendation system is disclosed that generates recommendations for one or more items based on user preferences and one or more environmental factors. The user's preferences are learned under various environmental conditions using an environmental data collection system. The observed environmental conditions may include, for example, location, characteristics of the location, weather or characteristics of the user's motion, such as a rate of movement. For each positive and negative behavioral example, a number of attributes of the selected item are classified in the user profile together with the prevailing environmental conditions. When recommending an item, the disclosed recommender retrieves the user preferences and evaluates the current environmental conditions. A recommendation score can be generated for each available item based on the user's demonstrated preferences under similar environmental conditions, such as in the same or a similar geographic area or under similar weather conditions.

Helping online customers decide through Web personalization

Abstract: The Web-based personalization system proposed here uses both collaborative filtering and Web usage mining to give online shoppers the personalized recommendations they need to purchase products more intelligently.

Knowledge Bases and User Profiling in Travel and Hospitality Recommender Systems.

Abstract: Recommender systems for the travel and hospitality industries attempt to emulate offline travel agents by providing users with knowledgeable travel suggestions. The ultimate goal is to help the user in the travel planning phase trough offering a comfortable Wlderstanding of the options and also giving a select set of alternatives. This paper presents a novel approach for constructing such systems: a) creating a domain specific dialog model. b) semi-automatically building a knowledge base of ratings for the items of interest (i.e. destinations. airfare, hotel. vacation packages). and c) generating personalized recommendations ordered by relevancy. Items of interest are selected to best fit the needs of travelers, based on their individuality, interests and preferences. Explicit and tacit user feedback, as well as the extrapolation of individual user interests through attribute-based collaborative filtering, allows the system to learn rich profiles and refine its knowledge base, generating ever-improving recommendations. Empirical results confirm the hypothesis that recommender systems tend to accelerate the decision-making process by showing an improvement in look..to-buy ratios of up to 4.95 times, when compared to normal purchases on a ski travel e-commerce site.