Recommender Systems

@article{Lu2012RecommenderS,
  title={Recommender Systems},
  author={Linyuan Lu and Mat{\'u}{\vs} Medo and Chi Ho Yeung and Yicheng Zhang and Zi-Ke Zhang and Tao Zhou},
  journal={ArXiv},
  year={2012},
  volume={abs/1202.1112}
}
Network-based recommendation algorithms: A review
Classification and Comparison of the Hybrid Collaborative Filtering Systems
TLDR
A comprehensive survey of hybrid CF systems is provided, which provides a classification for these systems, explains their strengths or weaknesses and compares their performance in dealing with the main limitations of CF.
Classifications of Recommender Systems : A review
TLDR
The various techniques are diagrammatically illustrated which on one hand helps a naïve researcher in this field to accommodate the on-going researches and establish a strong base, on the other hand it focuses on different categories of the recommender systems with deep technical discussions.
Survey on the Objectives of Recommender System: Measures, Solutions, Evaluation Methodology, and New Perspectives
Recently, recommender systems have played an increasingly important role in a wide variety of commercial applications to help users find favourite products. Research in the recommender system field
Evaluating Collaborative Filtering Recommender Algorithms: A Survey
TLDR
It is shown that there is no golden recommendation algorithm showing the best performance in all evaluation metrics, and that one should carefully consider the evaluation criteria in choosing a recommendation algorithm for a particular application.
A systematic review of group recommender systems techniques
  • R. Katarya
  • Computer Science
    2017 International Conference on Intelligent Sustainable Systems (ICISS)
  • 2017
TLDR
This paper focuses on evaluation techniques of the group recommender system (GRS) domain and includes the reputed papers that highlight, analyze and synthesized study only on the GRS domain.
An Effective Recommender Algorithm for Cold-Start Problem in Academic Social Networks
TLDR
A novel approach to address cold-start problem in recommender systems by incorporating social networking features as enhanced content-based algorithm using social networking (ECSN), which considers the submitted ratings of faculty mates and friends besides user’s own preferences.
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References

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Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions
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
Solving the apparent diversity-accuracy dilemma of recommender systems
TLDR
This paper introduces a new algorithm specifically to address the challenge of diversity and shows how it can be used to resolve this apparent dilemma when combined in an elegant hybrid with an accuracy-focused algorithm.
Trust in recommender systems
TLDR
This paper proposes that the trustworthiness of users must be an important consideration in guiding recommendation and presents two computational models of trust and shows how they can be readily incorporated into standard collaborative filtering frameworks in a variety of ways.
Addressing cold-start problem in recommendation systems
TLDR
A hybrid model based on the analysis of two probabilistic aspect models using pure collaborative filtering to combine with users' information is developed to address cold-start - that is, giving recommendations to novel users who have no preference on any items.
Hybrid Recommender Systems: Survey and Experiments
  • R. Burke
  • Computer Science
    User Modeling and User-Adapted Interaction
  • 2004
TLDR
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.
A Social Network-Based Recommender System (SNRS)
TLDR
This chapter presents a new paradigm of recommender systems which can utilize information in social networks, including user preferences, item’s general acceptance, and influence from social friends, and develops a probabilistic model to make personalized recommendations from such information.
Evaluating collaborative filtering recommender systems
TLDR
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.
Adaptive information filtering for dynamic recommender systems
TLDR
Two incremental diffusion-based algorithms for the personalized recommendations, which integrate some pieces of local and fast updatings to achieve the approximate results, and do not cumulate over time, which is to say, the global recomputing is unnecessary.
Recommender systems with social regularization
TLDR
This paper proposes a matrix factorization framework with social regularization, which can be easily extended to incorporate other contextual information, like social tags, etc, and demonstrates that the approaches outperform other state-of-the-art methods.
A Survey Paper on Recommender Systems
TLDR
This paper provides ways to evaluate efficiency, scalability and accuracy of recommender system, and analyzes different algorithms of user based and item based techniques for recommendation generation.
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