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Hybrid Recommender Systems: Survey and Experiments
- R. Burke
- Computer ScienceUser Modeling and User-Adapted Interaction
- 4 November 2002
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.
Hybrid Web Recommender Systems
- R. Burke
- Computer ScienceThe Adaptive Web
This chapter surveys the space of two-part hybrid recommender systems, comparing four different recommendation techniques and seven different hybridization strategies and finds that cascade and augmented hybrids work well, especially when combining two components of differing strengths.
Knowledge-based recommender systems
- R. Burke
- Computer Science
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.
Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness
This study shows that both user-based and item-based algorithms are highly vulnerable to specific attack models, but that hybrid algorithms may provide a higher degree of robustness.
Web search personalization with ontological user profiles
This work presents an approach to personalized search that involves building models of user context as ontological profiles by assigning implicitly derived interest scores to existing concepts in a domain ontology.
Context-aware music recommendation based on latenttopic sequential patterns
This paper presents a context-aware music recommender system which infers contextual information based on the most recent sequence of songs liked by the user, and uses topic modeling to determine a set of latent topics for each song, representing different contexts.
The FindMe Approach to Assisted Browsing
This article illustrates the idea of instance-based browsing, which involves structuring retrieval around the critiquing of previously retrieved examples, and retrieval strategies, or knowledge-based heuristics for finding relevant information, with examples of working FindMe systems.
Personalized recommendation in social tagging systems using hierarchical clustering
This work presents a personalization algorithm for recommendation in folksonomies which relies on hierarchical tag clusters and presents extensive experimental results on two real world dataset, suggesting that guysonomies encompassing only one topic domain, rather than many topics, present an easier target for recommendation.
Question Answering from Frequently Asked Question Files: Experiences with the FAQ FINDER System
- R. Burke, K. Hammond, V. Kulyukin, S. Lytinen, Noriko Tomuro, Scott Schoenberg
- Computer ScienceAI Mag.
- 20 June 1997
This technical report describes FAQ Finder, a natural language question answering system that uses files of frequently asked questions as its knowledge base, and describes the design and the current implementation of the system and its support components.
Classification features for attack detection in collaborative recommender systems
This paper proposes and studies different attributes derived from user profiles for their utility in attack detection and shows that a machine learning classification approach that includes attributesderived from attack models is more successful than more generalized detection algorithms previously studied.