Learning and Revising User Profiles: The Identification ofInteresting Web Sites
TL;DR: The use of a naive Bayesian classifier is described, and it is demonstrated that it can incrementally learn profiles from user feedback on the interestingness of Web sites and may easily be extended to revise user provided profiles.
Abstract: We discuss algorithms for learning and revising user profiles that can determine which World Wide Web sites on a given topic would be interesting to a user. We describe the use of a naive Bayesian classifier for this task, and demonstrate that it can incrementally learn profiles from user feedback on the interestingness of Web sites. Furthermore, the Bayesian classifier may easily be extended to revise user provided profiles. In an experimental evaluation we compare the Bayesian classifier to computationally more intensive alternatives, and show that it performs at least as well as these approaches throughout a range of different domains. In addition, we empirically analyze the effects of providing the classifier with background knowledge in form of user defined profiles and examine the use of lexical knowledge for feature selection. We find that both approaches can substantially increase the prediction accuracy.
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TL;DR: 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.
Abstract: 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.
9,873 citations
Cites background or methods from "Learning and Revising User Profiles..."
...On the other hand, [77] use a Bayesian classifier in order to estimate the probability that a document...
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...accuracy [77]....
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...For example, based on a set of Web pages that were rated as “relevant” or “irrelevant” by the user, [77] use the naïve Bayesian classifier [31] to classify unrated Web pages....
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...In addition to using traditional profile features, such as keywords and simple user demographics [69, 77], more advanced profiling techniques based on data mining rules [1, 34], sequences [63], and signatures [26] that describe user’s interests can be used to build user profiles....
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...(7) Moreover, [77] use the assumption that keywords are independent and, therefore, the above probability is proportional to...
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TL;DR: From basic techniques to the state-of-the-art, this paper attempts to present a comprehensive survey for CF techniques, which can be served as a roadmap for research and practice in this area.
Abstract: As one of the most successful approaches to building recommender systems, collaborative filtering (CF) uses the known preferences of a group of users to make recommendations or predictions of the unknown preferences for other users. In this paper, we first introduce CF tasks and their main challenges, such as data sparsity, scalability, synonymy, gray sheep, shilling attacks, privacy protection, etc., and their possible solutions. We then present three main categories of CF techniques: memory-based, modelbased, and hybrid CF algorithms (that combine CF with other recommendation techniques), with examples for representative algorithms of each category, and analysis of their predictive performance and their ability to address the challenges. From basic techniques to the state-of-the-art, we attempt to present a comprehensive survey for CF techniques, which can be served as a roadmap for research and practice in this area.
3,406 citations
Cites methods from "Learning and Revising User Profiles..."
...A content-based recommender then uses heuristic methods or classification algorithms to make recommendations [112]....
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TL;DR: An overview of recommender systems as well as collaborative filtering methods and algorithms is provided, which explains their evolution, provides an original classification for these systems, identifies areas of future implementation and develops certain areas selected for past, present or future importance.
Abstract: Recommender systems have developed in parallel with the web. They were initially based on demographic, content-based and collaborative filtering. Currently, these systems are incorporating social information. In the future, they will use implicit, local and personal information from the Internet of things. This article provides an overview of recommender systems as well as collaborative filtering methods and algorithms; it also explains their evolution, provides an original classification for these systems, identifies areas of future implementation and develops certain areas selected for past, present or future importance.
2,639 citations
Cites methods from "Learning and Revising User Profiles..."
...This task is resolved traditionally by using heuristic methods [198,15,79] or classification algorithms, such us: rule induction [65,119], nearest neighbors methods [236,27], Rocchio’s algorithm [131,16], linear classifiers [113], and probabilistic methods [175,160,84]....
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01 Jan 2007
TL;DR: This chapter discusses content-based recommendation systems, i.e., systems that recommend an item to a user based upon a description of the item and a profile of the user's interests, which are used in a variety of domains ranging from recommending web pages, news articles, restaurants, television programs, and items for sale.
Abstract: This chapter discusses content-based recommendation systems, i.e., systems that recommend an item to a user based upon a description of the item and a profile of the user's interests. Content-based recommendation systems may be used in a variety of domains ranging from recommending web pages, news articles, restaurants, television programs, and items for sale. Although the details of various systems differ, content-based recommendation systems share in common a means for describing the items that may be recommended, a means for creating a profile of the user that describes the types of items the user likes, and a means of comparing items to the user profile to determine what to recommend. The profile is often created and updated automatically in response to feedback on the desirability of items that have been presented to the user.
2,428 citations
Cites background or methods from "Learning and Revising User Profiles..."
...Some representative systems included WebWatcher [16] and Syskill & Webert [29]....
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..., for learning a user profile from unstructured text ([15], [3], [29])....
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...The naïve Bayes classifier has been used in several content-based recommendation systems including Syskill & Webert [29]....
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...Arguably, the decision tree bias is not ideal for unstructured text classification tasks [29]....
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01 Jan 2011
TL;DR: The role of User Generated Content is described as a way for taking into account evolving vocabularies, and the challenge of feeding users with serendipitous recommendations, that is to say surprisingly interesting items that they might not have otherwise discovered.
Abstract: Recommender systems have the effect of guiding users in a personal- ized way to interesting objects in a large space of possible options. Content-based recommendation systems try to recommend items similar to those a given user has liked in the past. Indeed, the basic process performed by a content-based recom- mender consists in matching up the attributes of a user profile in which preferences and interests are stored, with the attributes of a content object (item), in order to recommend to the user new interesting items. This chapter provides an overview of content-based recommender systems, with the aim of imposing a degree of order on the diversity of the different aspects involved in their design and implementation. The first part of the chapter presents the basic concepts and terminology of content- based recommender systems, a high level architecture, and their main advantages and drawbacks. The second part of the chapter provides a review of the state of the art of systems adopted in several application domains, by thoroughly describ- ing both classical and advanced techniques for representing items and user profiles. The most widely adopted techniques for learning user profiles are also presented. The last part of the chapter discusses trends and future research which might lead towards the next generation of systems, by describing the role of User Generated Content as a way for taking into account evolving vocabularies, and the challenge of feeding users with serendipitous recommendations, that is to say surprisingly interesting items that they might not have otherwise discovered.
1,582 citations
Cites background or methods from "Learning and Revising User Profiles..."
...The naı̈ve Bayes classifier has been used in several content-based recommendation systems, such as Syskill &Webert [70, 68], NewsDude [12],Daily Learner [13], LIBRA [65] and ITR [27, 83]....
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...used in the Syskill & Webert [70, 68] recommender system....
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...Personal WebWatcher [62, 63], Syskill & Webert [70, 68], ifWeb [4], Amalthea [66], and WebMate [23]....
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References
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TL;DR: This chapter contains sections titled: The Problem, The Generalized Delta Rule, Simulation Results, Some Further Generalizations, Conclusion.
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"Learning and Revising User Profiles..." refers methods in this paper
...We also use multi-layer networks trained with error backpropagation (Rumelhart et al., 1986)....
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TL;DR: In this paper, the problem of the generalized delta rule is discussed and the Generalized Delta Rule is applied to the simulation results of simulation results in terms of the generalized delta rule.
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