NDLI: Propagating Users' Similarity towards Improving Recommender Systems

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WI 2014 Non-Program Committee Reviewers - I

Quantum Cognition

Structural Vulnerability Analysis of Overlapping Communities in Complex Networks

Cloud Application Development Methodology

Multi-agent Information Diffusion Model for Twitter

Adaptive Landmark Selection Strategies for Fast Shortest Path Computation in Large Real-World Graphs

Enumerating Communities for a Deeper Understanding of Community Finding

Big Data - Characterizing an Emerging Research Field Using Topic Models

Efficient Spatio-textual Similarity Join Using MapReduce

Aspect-Based Similar Entity Search in Semantic Knowledge Graphs with Diversity-Awareness and Relaxation

Learning Concise Pattern for Interlinking with Extended Version Space

SORM: A Social Opinion Relevance Model

Collecting Conceptualized Relations from Terabytes of Web Texts for Understanding Unknown Terms

Person Identification between Different Online Social Networks

Obtaining Technology Insights from Large and Heterogeneous Document Collections

A New Sentence Similarity Method Based on a Three-Layer Sentence Representation

Multi-feature and DAG-Based Multi-tree Matching Algorithm for Automatic Web Data Mining

A Comparison of Mobile Rule Engines for Reasoning on Semantic Web Based Health Data

Locality-Sensitive Hashing for Massive String-Based Ontology Matching

Semantic Heuristic Search in Collaborative Networks: Measures and Contexts

An Approach for Learning and Construction of Expressive Ontology from Text in Natural Language

Mining Twitter Data with Resource Constraints

Conjunctive Query Programming: A Paradigm for Knowledge Engineering of Optimization Problems in the Semantic Web

Finding the Needle in the Haystack: Identifying Business Communities in Internet Traffic

NCDREC: A Decomposability Inspired Framework for Top-N Recommendation

Optimizing Personalized Ranking in Recommender Systems with Metadata Awareness

Improving Personalized Ranking in Recommender Systems with Multimodal Interactions

A Link Prediction Approach for Item Recommendation with Complex Number

SemanticSVD++: Incorporating Semantic Taste Evolution for Predicting Ratings

Propagating Users' Similarity towards Improving Recommender Systems

Online Recommender System for Radio Station Hosting: Experimental Results Revisited

Fuzziness and Ontology in Personalization of Selection Processes in the Semantic Web

Sentence-Based Plot Classification for Online Review Comments

Summarizing Search Results with Community-Based Question Answering

Lazy Walks Versus Walks with Backstep: Flavor of PageRank

A Machine Learning Approach to SPARQL Query Performance Prediction

Improving Biodiversity Data Retrieval through Semantic Search and Ontologies

Online Retweet Recommendation with Item Count Limits

Extracting Attributes and Synonymous Attributes from Online Encyclopedias

Credibility Microscope: Relating Web Page Credibility Evaluations to Their Textual Content

Harnessing Social Signals to Enhance a Search

Relevant Sources of Information Are Not Necessarily Popular Ones

Hybrid Algorithm for Precise Recommendation from Almost Infinite Set of Websites

Combining Query Terms Extension and Weight Correlative for Expert Finding

Recommender System for Crowdsourcing Platform Witology

Feature Selection and Term Weighting

Improving Collaborative Filtering Based Recommenders Using Topic Modelling

Dynamic Learning of Keyword-Based Preferences for News Recommendation

Link Prediction Based on Multi-steps Resource Allocation

Fuzzy Subjective Sentiment Phrases: A Context Sensitive and Self-Maintaining Sentiment Lexicon

On the Effectiveness of Emergent Task Allocation of Virtual Programmer Teams

Identification of Opinion Leaders Based on User Clustering and Sentiment Analysis

Indirect Keyword Recommendation

Tweet Sentiment Analytics with Context Sensitive Tone-Word Lexicon

An Ontology for Guiding Performance Testing

Ontology as a Base for State Space of Expert Systems

Learning of Legal Ontology Supporting the User Queries Satisfaction

Multi-agent Based System for Multilingual Ontologies Maintenance

An Approach to Join Ontologies and Their Reuse in the Construction of Application Ontologies

Interpreting Discovered Patterns in Terms of Ontology Concepts

Detecting and Correlating Video-Based Event Patterns: An Ontology Driven Approach

Mapping Word Senses of Middle Ancient Chinese to WordNet

Application of TextRank Algorithm for Credibility Assessment

Integrating Pinyin to Improve Spelling Errors Detection for Chinese Language

Exploratory Study of Relationships among Statement Credibility, Context, and Semantic Similarity

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Propagating Users' Similarity towards Improving Recommender Systems

Content Provider	ACM Digital Library
Author	Satsiou, Anna Tassiulas, Leandros
Abstract	In this paper we examine an advanced collaborative filtering method that uses similarity transitivity concepts. By propagating "similarity" between users, in a similar way as with "trust", we can significantly expand the space of potential recommenders and system's coverage, improving also the recommendations' accuracy. While "trust" information might be missing or be misleading and incorrect, "similarity" between two users can be directly calculated using the information from users' item ratings. A recent study observed a strong correlation between trust and preference similarity in online rating systems, therefore it makes sense that transitivity concepts can also be applied to "similarity", much as they are applied to "trust". In contrast to a vast amount of work that seeks to exploit existed social information, like trust, from social networks to improve the recommendation process, we propose the other way round towards the same goal: use similarity transitivity concepts exploiting the rating history of the recommender system's users to lead to the formation of new relationships and even social communities that were not previously existed. We propose a novel similarity propagation scheme to confront the data sparsity problem in recommender systems and evaluate our method over two datasets with different characteristics, exhibiting a much higher recommendation coverage and better accuracy than classical collaborative filtering methods even under very sparse data conditions.
Starting Page	221
Ending Page	228
Page Count	8
File Format	PDF
ISBN	9781479941438
DOI	10.1109/WI-IAT.2014.37
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-08-11
Access Restriction	Subscribed
Subject Keyword	Recommender systems similarity transitivity collaborative filtering data sparsity cold-start problem
Content Type	Text
Resource Type	Article

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