Matching Recommendation Technologies and Domains
Published 5 October 2010
Robin Burke, Maryam Ramezani
Citations74
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
This chapter aims to assist researchers and developers to identify the recommendation technologies that are most likely to be applicable to different domains of recommendation.
Abstract
Recommender systems form an extremely diverse body of technologies and approaches. The chapter aims to assist researchers and developers to identify the recommendation technologies that are most likely to be applicable to different domains of recommendation. Unlike other taxonomies of recommender systems, our approach is centered on the question of knowledge: what knowledge does a recommender system need in order to function, and where does that knowledge come from? Different recommendation domains (books vs condominiums, for example) provide different opportunities for the gathering and application of knowledge. These considerations give rise to a mapping between domain characteristics and recommendation technologies.
Keywords
Computer ScienceDecision Sciences
IEEE Transactions on Knowledge and Data EngineeringToward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions
10,115 Citations2005Gediminas Adomavičius, Alexander Tuzhilin
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.
Item-based collaborative filtering recommendation algorithms
9,017 Citations2001Badrul Sarwar, George Karypis +2 more
This paper analyzes item-based collaborative ltering techniques and suggests 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.
ACM Transactions on Information SystemsEvaluating collaborative filtering recommender systems
5,762 Citations2004Jonathan L. Herlocker, Joseph A. Konstan +2 more
The key decisions in evaluating collaborative filtering recommender systems are reviewed: 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.
IEEE ExpertData mining and knowledge discovery: making sense out of data
4,643 Citations1996U.M. Feyyad
Find loads of the data mining and knowledge discovery making sense out of data book catalogues in this site as the choice of you visiting this page.
arXiv (Cornell University)Empirical Analysis of Predictive Algorithms for Collaborative Filtering
4,515 Citations2013John S. Breese, David Heckerman +1 more
User Modeling and User-Adapted InteractionHybrid Recommender Systems: Survey and Experiments
3,764 Citations2002Robin Burke
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, and shows that semantic ratings obtained from the knowledge- based part of the system enhance the effectiveness of collaborative filtering.
Communications of the ACMRecommender systems
3,685 Citations1997Paul Resnick, Hal R. Varian
This special section includes descriptions of five recommender systems, which provide recommendations as inputs, which the system then aggregates and directs to appropriate recipients, and which combine evaluations with content analysis.
ACM SIGIR ForumAn Algorithmic Framework for Performing Collaborative Filtering
2,552 Citations2017Jonathan L. Herlocker, Joseph A. Konstan +2 more
Elsevier eBooksNewsWeeder: Learning to Filter Netnews
2,022 Citations1995Ken Lang
The results show that a learning algorithm based on the Minimum Description Length (MDL) principle was able to raise the percentage of interesting articles to be shown to users from 14% to 52% on average.
An algorithmic framework for performing collaborative filtering
1,973 Citations1999Jonathan L. Herlocker, Joseph A. Konstan +2 more
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage.
Explaining collaborative filtering recommendations
1,698 Citations2000Jonathan L. Herlocker, Joseph A. Konstan +1 more
This paper presents experimental evidence that shows that providing explanations can improve the acceptance of ACF systems, and presents a model for explanations based on the user's conceptual model of the recommendation process.
Data Mining and Knowledge DiscoveryE-Commerce Recommendation Applications
1,518 Citations2001J. Ben Schafer, Joseph A. Konstan +1 more
An explanation of how recommender systems are related to some traditional database analysis techniques is presented, and a taxonomy ofRecommender systems is created, including the inputs required from the consumers, the additional knowledge required from a database, the ways the recommendations are presented to consumers,The technologies used to create the recommendations, and the level of personalization of the recommendations.
Google news personalization
1,511 Citations2007Abhinandan Das, Mayur Datar +2 more
This paper describes the approach to collaborative filtering for generating personalized recommendations for users of Google News using MinHash clustering, Probabilistic Latent Semantic Indexing, and covisitation counts, and combines recommendations from different algorithms using a linear model.
Application of Dimensionality Reduction in Recommender System - A Case Study
1,493 Citations2000Badrul Sarwar, George Karypis +2 more
This paper presents two different experiments where one technology called Singular Value Decomposition (SVD) is explored to reduce the dimensionality of recommender system databases and suggests that SVD has the potential to meet many of the challenges ofRecommender systems, under certain conditions.
ACM SIGKDD Explorations NewsletterWeb mining research
1,439 Citations2000Raymond Kosala, Hendrik Blockeel
This paper surveys the research in the area of Web mining, point out some confusions regarded the usage of the term Web mining and suggest three Web mining categories, which are then situate some of the research with respect to these three categories.
Information RetrievalEigentaste: A Constant Time Collaborative Filtering Algorithm
1,434 Citations2001Ken Goldberg, Theresa Roeder +2 more
This work compares Eigentaste to alternative algorithms using data from Jester, an online joke recommending system, and uses the Normalized Mean Absolute Error (NMAE) measure to compare performance of different algorithms.
Content-based book recommending using learning for text categorization
1,326 Citations2000Raymond J. Mooney, Loriene Roy
This work describes a content-based book recommending system that utilizes information extraction and a machine-learning algorithm for text categorization and shows initial experimental results demonstrate that this approach can produce accurate recommendations.
Artificial Intelligence ReviewA Framework for Collaborative, Content-Based and Demographic Filtering
1,307 Citations1999Michael J. Pazzani
The types of information available to determine whether to recommend a particular page to a particular user are described and how each type of information may be used individually and an approach to combining recommendations from multiple sources are discussed.
Communications of the ACMAutomatic personalization based on Web usage mining
1,225 Citations2000Bamshad Mobasher, Robert Cooley +1 more
The ability to track users’ browsing behavior down to individual mouse clicks has brought the vendor and end customer closer than ever before, and it is now possible for a vendor to personalize his product message for individual customers at a massive scale, a phenomenon that is being referred to as mass customization.
Machine LearningLearning and Revising User Profiles: The Identification of Interesting Web Sites
1,200 Citations1997Michael J. Pazzani, Daniel Billsus
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.
ACM Transactions on Information SystemsIncorporating contextual information in recommender systems using a multidimensional approach
1,186 Citations2005Gediminas Adomavičius, Ramesh Sankaranarayanan +2 more
A multidimensional (MD) approach to recommender systems that can provide recommendations based on additional contextual information besides the typical information on users and items used in most of the currentRecommender systems is presented.
Knowledge-based recommender systems
754 Citations2000Robin Burke
Recommendations made by recommender systems can help users navigate through large information spaces of product descriptions, news articles or other items, and are an increasingly important tool in the on-line information and e-commerce burgeon.
Combining collaborative filtering with personal agents for better recommendations
679 Citations1999Nathaniel Good, J. Ben Schafer +5 more
This paper shows that a CF framework can be used to combine personal IF agents and the opinions of a community of users to produce better recommendations than either agents or users can produce alone.
Artificial Intelligence ReviewA Taxonomy of Recommender Agents on the Internet
637 Citations2003Miquel Montaner, Beatriz López +1 more
A state-of-the-art taxonomy of intelligent recommender agents on the Internet and a cross-dimensional analysis with the aim of providing a starting point for researchers to construct their own recommender system.
Clustering Methods for Collaborative Filtering
629 Citations1998Lyle Ungar, Dean P. Foster
A formal statistical model of collaborative filtering is presented, and different algorithms for estimating the model parameters including variations of K-means clustering and Gibbs Sampling are compared.
User Modeling and User-Adapted InteractionUser Modeling for Adaptive News Access
480 Citations2000Daniel Billsus, Michael J. Pazzani
The induction of hybrid user models that consist of separate models for short-term and long-term interests are proposed, and it is suggested that effective personalization can be achieved without requiring any extra effort from the user.
Feature Selection for Unbalanced Class Distribution and Naive Bayes
432 Citations1999Dunja Mladenić, Marko Grobelnik
This paper describes an approach to feature subset selection that takes into account problem speciics and learning algorithm characteristics, and shows that considering domain and algorithm characteristics signiicantly improves the results of classiication.
Lecture notes in computer scienceContext-Aware Recommendations in the Mobile Tourist Application COMPASS
350 Citations2004Mark van Setten, Stanislav Pokraev +1 more
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 and describes how this integration has been accomplished.
Towards the Semantic Web: Collaborative Tag Suggestions
342 Citations2006Zhichen Xu, Yun Fu +2 more
This work defines a set of general criteria for a good tagging system and proposes a collaborative tag suggestion algorithm using these criteria to spot high-quality tags and employs a goodness measure for tags derived from collective user authorities to combat spam.
Constraint-based recommender systems
259 Citations2008Alexander Felfernig, Robin Burke
A taxonomy of recommendation knowledge sources and algorithmic approaches is introduced and the most prevalent techniques of constraint-based recommendation are discussed, and open research issues are outlined.
Matching People and Jobs: A Bilateral Recommendation Approach
215 Citations2006Jochen Malinowski, Tobias Keim +2 more
This work presents an approach applying two distinct recommendation systems to the field in order to improve the match between people and jobs and presents first validation test runs from a student experiment showing promising results.
IEEE Pervasive ComputingSupporting Context-Aware Media Recommendations for Smart Phones
204 Citations2006Zhiwen Yu, Xingshe Zhou +4 more
A hybrid recommendation approach to synergize content-based, Bayesian-classifier, and rule-based methods for media recommendation, adaptation, and delivery for smart phones is proposed.
User Modeling and User-Adapted InteractionExploiting Query Repetition and Regularity in an Adaptive Community-Based Web Search Engine
192 Citations2004Barry Smyth, Evelyn Balfe +4 more
It is argued that I-SPY strikes a useful balance between search personalization and user privacy, by offering a unique form of anonymous personalization, and in doing so may very well provide privacy-conscious Web users with an acceptable approach to personalized search.
Artificial Intelligence ReviewExplanation in Recommender Systems
177 Citations2005David McSherry
It is shown how the relevance of any question the user is asked can be explained in terms of its ability to discriminate between competing cases, thus giving users a unique insight into the recommendation process.
Survey of Preference Elicitation Methods
166 Citations2004Li Chen, Pearl Pu
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.
Harvesting social knowledge from folksonomies
164 Citations2006Harris Wu, Mohammad Zubair +1 more
This paper presents designs that enhance collaborative tagging systems to meet some key challenges: community identification, ontology generation, user and document recommendation.
A music recommendation system based on music data grouping and user interests
161 Citations2001Hung-Chen Chen, Arbee L. P. Chen
The Music Recommendation System (MRS) is designed to provide a personalized service of music recommendation, which is based on the favorite degrees of the users to the music groups.
IEEE Transactions on Software EngineeringApproximate Structural Context Matching: An Approach to Recommend Relevant Examples
159 Citations2006Reid Holmes, Robert J. Walker +1 more
The structural context needed to query the repository is extracted automatically from the code, freeing the developer from learning a query language or from writing their code in a particular style, avoiding the need for handcrafted examples.
Knowledge-Based SystemsInformation access in context
123 Citations2001Jay Budzik, Kristian J. Hammond +1 more
Watson is presented, a system which gathers contextual information in the form of the text of the document the user is manipulating, in order to proactively retrieve documents from distributed information repositories related to task at hand, as well as process explicit requests in the context of this task.
Using Singular Value Decomposition Approximation for Collaborative Filtering
122 Citations2006Sheng Zhang, Weihong Wang +3 more
A novel algorithm is presented that incorporates SVD approximation into the EM procedure to reduce the overall computational cost while maintaining accurate predictions, and a new framework for collaborating filtering in distributed recommendation systems that allows users to maintain their own rating profiles for privacy is proposed.
Lecture notes in computer scienceCombining Usage, Content, and Structure Data to Improve Web Site Recommendation
114 Citations2004Li Jia, Osmar R. Zai͏̈ane
This paper investigates a novel web recommender system, which combines usage data, content data, and structure data in a web site to generate user navigational models and proposes an evaluation mechanism to measure the quality of recommender systems.
Computational Statistics & Data AnalysisDetection of unknown computer worms based on behavioral classification of the host
100 Citations2008Robert Moskovitch, Yuval Elovici +1 more
This paper focuses on the feasibility of accurately detecting unknown worm activity in individual computers while minimizing the required set of features collected from the monitored computer.
Web Page Recommender System based on Folksonomy Mining for ITNG ’06 Submissions
94 Citations2006Shoei Niwa, Takuo Doi +1 more
This paper proposes the way to construct a new type of Web page recommender system covering all over the Internet, by using folksonomy and social bookmark which are getting very popular in these days.
MultiMediaMiner
89 Citations1998Osmar R. Zai͏̈ane, Jiawei Han +3 more
The construction of a multimedia data cube which facilitates multiple dimensional analysis of multimedia data, primarily based on visual content, and the mining of multiple kinds of knowledge, including summarization, comparison, classification, association, and clustering.
Artificial Intelligence ReviewInteractive Critiquing forCatalog Navigation in E-Commerce
78 Citations2002Robin Burke
An interactive, incremental, case-based, critiquing approach to solving the problem of e-commerce catalogs, whichphasizes products over features or constraints, and uses case- based reasoning techniques for its product retrieval.
Fighting Information Overflow with Personalized Comprehensive Information Access: A Proactive Job Recommender
78 Citations2007Danielle Lee, Peter Brusilovsky
This paper introduces a comprehensive job recommender system that can retrieve open jobs with different methods, ranging from exploring to searching, and four different kinds of recommendations are implemented.
Lecture notes in computer scienceExplanations and Case-Based Reasoning: Foundational Issues
76 Citations2004Thomas Roth–Berghofer
This paper is a first pass on examining issues concerning explanations produced by CBR systems from the knowledge containers perspective and discusses what naturally can be explained by each of the four knowledge containers in relation to scientific, conceptual, and cognitive explanations.
Recommender System Based on Consumer Product Reviews
65 Citations2006Silvana Aciar, Debbie Zhang +2 more
A new recommender system that is built on consumer product reviews is proposed and a prioritizing mechanism is developed for the system.
Lecture notes in computer scienceA Comparative Study of Compound Critique Generation in Conversational Recommender Systems
64 Citations2006Jiyong Zhang, Pearl Pu
This paper proposes an alternative approach for generating compound critiques based on the multi-attribute utility theory (MAUT), which automatically updates the weights of the product attributes as the result of the interactive critiquing process and is shown to be more efficient than the Apriori algorithm.
Using Data Mining and Recommender Systems to Facilitate Large-Scale, Open, and Inclusive Requirements Elicitation Processes
61 Citations2008Carlos Castro-Herrera, Chuan Duan +2 more
A new process and related framework is introduced that utilizes data mining and recommender technologies to create an open, scalable, and inclusive requirements elicitation process capable of supporting projects with thousands of stakeholders.
Evaluation of an ontology-content based filtering method for a personalized newspaper
54 Citations2008Veronica Maidel, Peretz Shoval +2 more
A new ontological-content-based method for ranking the relevancy of items in the electronic newspapers domain is proposed, which yields significantly better quality of filtering compared to using only common concepts.
Content-based music filtering system with editable user profile
48 Citations2006Yoshinori Hijikata, Kazuhiro Iwahama +1 more
This work proposes a content-based filtering system for music data by using a decision tree that can eliminate noise features, which are not related to the user's preference, and can allow the user to edit the learned user profile.
Knowledge-based interactive selling of financial services with FSAdvisor
48 Citations2005Alexander Felfernig, Alfred Kiener
In this paper the knowledge-based recommender application FSAdvisor (Financial Services Advisor) is described which assists sales representatives in determining personalized financial service portfolios for their customers by integrating model-based diagnosis, constraint satisfaction and personalization thus supporting customer-oriented sales dialogs.
Lecture notes in computer scienceExplaining the Pros and Cons of Conclusions in CBR
47 Citations2004David McSherry
An evidential approach to explanation is presented in which a key role is played by techniques for the discovery of features that support or oppose the predicted outcome in case-based reasoning approaches to classification and diagnosis.
Conversational recommenders with adaptive suggestions
42 Citations2007Paolo Viappiani, Pearl Pu +1 more
A strategy for producing suggestions that exploits prior knowledge of preference distributions and can adapt relative to users' reactions to the displayed examples is proposed and evaluated with simulations using data acquired by previous interactions with real users.
An analysis of the use of tags in a blog recommender system
40 Citations2007Conor Hayes, Paolo Avesani +1 more
The Tr score, a score based on the proportion of high-frequency tags in a cluster, is introduced and demonstrated that it is strongly correlated with cluster strength, and can be used as an independent means of verifying topic integrity in a clusters-based recommender system.
Adaptive Recommender Systems for Travel Planning
37 Citations2008Tariq Mahmood, Francesco Ricci⋆ +2 more
This paper presents the methodology behind the adaptive conversational recommender system and a summarization of the most important issues which have been addressed in order to validate the approach in an online context with real users.
ACM Transactions on Internet TechnologyIntroduction to intelligent techniques for Web personalization
36 Citations2007Sarabjot Singh Anand, Bamshad Mobasher
Being data driven; Web personalization is also achieved through the implementation of all the phases of a typical data mining cycle including data collection, preprocessing, pattern discovery and evaluation, in an off-line mode, and finally the deployment of the knowledge in realtime to mediate between the user and the Web.
A collaborative constraint-based meta-level recommender
29 Citations2008Markus Zanker
A system that learns rule-based preferences from successful interactions in historic transaction data is proposed as a meta-level hybrid that employs collaborative filtering to derive preferences from a user's nearest neighbors that are processed by a knowledge-based RS to derive recommendations.
Smart Radio - Building Music Radio On the Fly
28 Citations2001Conor Hayes, Pádraig Cunningham
The development of a networked music application at Trinity College Dublin which uses streaming audio technology and collaborative recommendation techniques to allow users build, manage and share music programmes.
Koba4MS: Selling Complex Products and Services Using Knowledge-Based Recommender Technologies
28 Citations2006Alexander Felfernig
This paper presents the domain-independent knowledge-based recommender system Koba4MS which assists customers and sales representatives by guaranteeing the consistency and appropriateness of proposed solutions, identifying additional selling opportunities and by providing intelligent explanations for identified results.
Lecture notes in computer scienceIntegrating User Data and Collaborative Filtering in a Web Recommendation System
23 Citations2002Paolo Buono, Maria Francesca Costabile +2 more
The approach is based on the integration of data the system collects about users, both explicitly and implicitly, and a classical collaborative filtering technique in order to provide appropriate recommendations to the user in any circumstances during the visit of the on-line fair catalogue.
Bookmark hierarchies and collaborative recommendation
22 Citations2006Ben Markines, Lubomira Stoilova +1 more
A semantic similarity measure for URLs that takes advantage both of the hierarchical structure in the bookmark files of individual users, and of collaborative filtering across users to build a recommendation and search engine from ranking algorithms based on popularity and novelty measures extracted from the similarity-induced network.
A Generic Multipurpose recommender System for Contextual Recommendations
19 Citations2007Christian Räck, Stefan Arbanowski +1 more
A generic framework that delivers "contextual recommendations" that are based on the combination of previously gathered user feedback data, context data, and ontology-based content categorization schemes is proposed.
Lecture notes in computer scienceKnowledge Discovery from User Preferences in Conversational Recommendation
12 Citations2005María Salamó, J.P. Reilly +2 more
Three different strategies for knowledge discovery from user preferences that improve recommendation efficiency in a conversational system using critiquing are described and it is demonstrated that while the strategies work well separately, their combined effort has the potential to considerably increase recommendation efficiency even further.
Informed Recommender Agent: Utilizing Consumer Product Reviews through Text Mining
11 Citations2006Silvana Aciar, Debbie Zhang +2 more
An automatic mapping process using text mining techniques is presented, which learns the attributes of the ontology from the semantic features in the review comments using supervised learning techniques.
ACM SIGKDD Explorations NewsletterMaking the most of your data
10 Citations2007Saharon Rosset, Claudia Perlich +1 more
The ideas and methodologies developed in addressing the KDD Cup 2007 How Many Ratings task are described and discussed, and how they contributed to the authors' success are discussed.
Providing context-aware personalization through cross-context reasoning of user modeling data
8 Citations2007Shlomo Berkovsky, Lora Aroyo +5 more
This work focuses on developing mechanisms for cross-context reasoning, i.e., inferences linking user model data in two different contexts, that can augment the typically sparse user models, by inferring the missing information from other contextual conditions, and can better support context-aware personalization.
Lecture notes in computer scienceRetrieval and Configuration of Life Insurance Policies
8 Citations2005Alexander Tartakovski, Martin Schaaf +1 more
Each insurance product is represented as a generalized case and therefore similarity assessment and indexing of case bases consisting of generalized cases is one of the topics of this paper.
ACM SIGKDD Explorations NewsletterKDD Cup 2007 task 1 winner report
7 Citations2007Miklós Kurucz, András A. Benczúr +4 more
This paper presents the prediction method for Task 1 "Who Rated What in 2006" where the goal is to predict which users rated which movies in 2006, and uses the combination of the following methods.
Lecture notes in computer scienceMixed-Initiative Relaxation of Constraints in Critiquing Dialogues
6 Citations2007David McSherry, David W. Aha
A new version of progressive critiquing is presented in which mixed-initiative relaxation of constraints introduced by usercritiquing choices that depart from this strategy greatly reduces their impact on dialogue outcomes.
IEEE Intelligent Systems and their ApplicationsIntegrating and using large databases of text, images, video, and audio
6 Citations1999Alex Hauptmann
There is no clear categorization or organization of the various research efforts concerning mixed-media databases concerning TIVA sources, but intelligent, content-understanding systems can greatly improve the usefulness of the huge quantities of existing material from these sources.
…