Results 11 - 20
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283
E-Commerce Recommendation Applications
, 2001
"... Recommender systems are being used by an ever-increasing number of E-commerce sites to help consumers find products to purchase. What started as a novelty has turned into a serious business tool. Recommender systems use product knowledge -- either hand-coded knowledge provided by experts or "mi ..."
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Cited by 205 (1 self)
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Recommender systems are being used by an ever-increasing number of E-commerce sites to help consumers find products to purchase. What started as a novelty has turned into a serious business tool. Recommender systems use product knowledge -- either hand-coded knowledge provided by experts or "mined" knowledge learned from the behavior of consumers -- to guide consumers through the often-overwhelming task of locating products they will like. In this article we present an explanation of how recommender systems are related to some traditional database analysis techniques. We examine how recommender systems help E-commerce sites increase sales and analyze the recommender systems at six market-leading sites. Based on these examples, we create a taxonomy of recommender systems, including the inputs required from the consumers, the additional knowledge required from the database, the ways the recommendations are presented to consumers, the technologies used to create the recommendations, and t...
Evaluation of Item-Based Top-N Recommendation Algorithms
, 2000
"... 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 N items that will be of interest to a certain user. User-based Collaborative filtering is the mos ..."
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Cited by 178 (3 self)
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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 N 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 that in typical commercial applications can grow to be several millions. To address these scalability concerns item-based recommendation techniques have been developed that analyze the user-item matrix to identify relations between the different items, and use these relations to compute the list of recommendations. In this paper we present one such class of item-based recommendation algorithms that first determine the similarities between the various ite...
Probabilistic models for unified collaborative and content-based recommendation in sparsedata environments
- In UAI ’01, 437–444
, 2001
"... Recommender systems leverage product and community information to target products to consumers. Researchers have developed collaborative recommenders, content-based recommenders, and a few hybrid systems. We propose a unified probabilistic framework for merging collaborative and content-based recomm ..."
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Cited by 171 (9 self)
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Recommender systems leverage product and community information to target products to consumers. Researchers have developed collaborative recommenders, content-based recommenders, and a few hybrid systems. We propose a unified probabilistic framework for merging collaborative and content-based recommendations. We extend Hofmann’s (1999) aspect model to incorporate three-way co-occurrence data among users, items, and item content. The relative influence of collaboration data versus content data is not imposed as an exogenous parameter, but rather emerges naturally from the given data sources. However, global probabilistic models coupled with standard EM learning algorithms tend to drastically overfit in the sparsedata situations typical of recommendation applications. We show that secondary content information can often be used to overcome sparsity. Experiments on data from the ResearchIndex library of Computer Science publications show that appropriate mixture models incorporating secondary data produce significantly better quality recommenders than-nearest neighbors (-NN). Global probabilistic models also allow more general inferences than local methods like-NN. 1
Getting to Know You: Learning New User Preferences in Recommender Systems
, 2002
"... 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 collabo ..."
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Cited by 145 (12 self)
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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 preexisting 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.
Applying Associative Retrieval Techniques to Alleviate the Sparsity Problem in Collaborative Filtering
- ACM Transactions on Information Systems
, 2004
"... 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 o ..."
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Cited by 135 (12 self)
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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
Collaborative Filtering Recommender Systems
, 2007
"... One of the potent personalization technologies powering the adaptive web is collaborative filtering. Collaborative filtering (CF) is the process of filtering or evaluating items through the opinions of other people. CF technology brings together the opinions of large interconnected communities on ..."
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Cited by 113 (2 self)
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One of the potent personalization technologies powering the adaptive web is collaborative filtering. Collaborative filtering (CF) is the process of filtering or evaluating items through the opinions of other people. CF technology brings together the opinions of large interconnected communities on the web, supporting filtering of substantial quantities of data. In this chapter we introduce the core concepts of collaborative filtering, its primary uses for users of the adaptive web, the theory and practice of CF algorithms, and design decisions regarding rating systems and acquisition of ratings. We also discuss how to evaluate CF systems, and the evolution of rich interaction interfaces. We close the chapter with discussions of the challenges of privacy particular to a CF recommendation service and important open research questions in the field.
Unifying collaborative and content-based filtering
- In ICML
, 2004
"... 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 w ..."
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Cited by 110 (2 self)
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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. 1.
Beyond Recommender Systems: Helping People Help Each Other
- HCI in the New Millennium
, 2001
"... The Internet and World Wide Web have brought us into a world of endless possibilities: interactive Web sites to experience, music to listen to, conversations to participate in, and every conceivable consumer item to order. But this world also is one of endless choice: how can we select from a hug ..."
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Cited by 96 (1 self)
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The Internet and World Wide Web have brought us into a world of endless possibilities: interactive Web sites to experience, music to listen to, conversations to participate in, and every conceivable consumer item to order. But this world also is one of endless choice: how can we select from a huge universe of items of widely varying quality? Computational recommender systems have emerged to address this issue. They enable people to share their opinions and benefit from each other's experience. We present a framework for understanding recommender systems and survey a number of distinct approaches in terms of this framework. We also suggest two main research challenges: (1) helping people form communities of interest while respecting personal privacy, and (2) developing algorithms that combine multiple types of information to compute recommendations. In HCI In The New Millennium, Jack Carroll, ed., Addison-Wesley, 2001 p. 2 of 21 Introduction The new millennium is an age of i...
Hybrid Web Recommender Systems
, 2007
"... Adaptive web sites may offer automated recommendations generated through any number of well-studied techniques including collaborative, content-based and knowledge-based recommendation. Each of these techniques has its own strengths and weaknesses. In search of better performance, researchers have ..."
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Cited by 92 (1 self)
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Adaptive web sites may offer automated recommendations generated through any number of well-studied techniques including collaborative, content-based and knowledge-based recommendation. Each of these techniques has its own strengths and weaknesses. In search of better performance, researchers have combined recommendation techniques to build hybrid recommender systems. This chapter surveys the space of two-part hybrid recommender systems, comparing four different recommendation techniques and seven different hybridization strategies. Implementations of 41 hybrids including some novel combinations are examined and compared. The study finds that cascade and augmented hybrids work well, especially when combining two components of differing strengths.
Exploiting hierarchical domain structure to compute similarity
- ACM Trans. Inf. Syst
"... The notion of similarity between objects nds use in many contexts, e.g., in search engines, collaborative ltering, and clustering. Objects being compared often are modeled as sets, with their similarity traditionally determined based on set intersection. Intersection-based measures do not accurately ..."
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Cited by 84 (0 self)
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The notion of similarity between objects nds use in many contexts, e.g., in search engines, collaborative ltering, and clustering. Objects being compared often are modeled as sets, with their similarity traditionally determined based on set intersection. Intersection-based measures do not accurately capture similarity in certain domains, such as when the data is sparse or when there are known relationships between items within sets. We propose new measures that exploit a hierarchical domain structure in order to produce more intuitive similarity scores. We also extend our similarity measures to provide appropriate results in the presence of multisets (also handled unsatisfactorily by traditional measures), e.g., to correctly compute the similarity between customers who buy several instances of the same product (say milk), or who buy several products in the same category (say dairy products). We also provide an experimental comparison of our measures against traditional similarity measures, and describe an informal user study that evaluated how well our measures match human intuition. 1
