Exploring recommendations in internet of things

@article{Yao2014ExploringRI,
  title={Exploring recommendations in internet of things},
  author={Lina Yao and Quan Z. Sheng and Anne H. H. Ngu and Helen Ashman and Xue Li},
  journal={Proceedings of the 37th international ACM SIGIR conference on Research \& development in information retrieval},
  year={2014},
  url={https://api.semanticscholar.org/CorpusID:1530485}
}
A unified probabilistic based framework is proposed by fusing information across relationships between users and things to make more accurate recommendations to solve the things recommendation problem in Internet of Things.
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52 Citations
Highly Influential Citations
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Background Citations
31
Methods Citations
4

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52 Citations

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10 References

A Model for Discovering Correlations of Ubiquitous Things

A novel approach for discovering things correlation based on user, temporal, and spatial information captured from usage events of things, using a spatio-temporal graph and a social graph to model things usage contextual information and user-thing relationships respectively is proposed.

TrustWalker: a random walk model for combining trust-based and item-based recommendation

A random walk model combining the trust-based and the collaborative filtering approach for recommendation is proposed, which allows us to define and to measure the confidence of a recommendation.

SoRec: social recommendation using probabilistic matrix factorization

A factor analysis approach based on probabilistic matrix factorization to solve the data sparsity and poor prediction accuracy problems by employing both users' social network information and rating records is proposed.

Personalized recommendation on dynamic content using predictive bilinear models

This work proposes a feature-based machine learning approach to personalized recommendation that is capable of handling the cold-start issue effectively and results in an offline model with light computational overhead compared with other recommender systems that require online re-training.

Factorization meets the neighborhood: a multifaceted collaborative filtering model

The factor and neighborhood models can now be smoothly merged, thereby building a more accurate combined model and a new evaluation metric is suggested, which highlights the differences among methods, based on their performance at a top-K recommendation task.

Like like alike — Joint Friendship and Interest Propagation in Social Networks Shuang

This paper proposes a framework that exploits homophily to establish an integrated network linking a user to interested services and connecting different users with common interests, upon which both friendship and interests could be efficiently propagated.

Kernelized Probabilistic Matrix Factorization: Exploiting Graphs and Side Information

A new matrix completion algorithm— Kernelized Probabilistic Matrix Factorization (KPMF), which effectively incorporates external side information into the matrix factorization process, and can be easily used with other types of side information as well.

Probabilistic Matrix Factorization

The Probabilistic Matrix Factorization (PMF) model is presented, which scales linearly with the number of observations and performs well on the large, sparse, and very imbalanced Netflix dataset and is extended to include an adaptive prior on the model parameters.

Semi-supervised Learning on Directed Graphs

A regularization framework for functions defined over nodes of a directed graph that forces the classification function to change slowly on densely linked subgraphs is proposed.

Centrality in sociocognitive networks and social influence : An illustration in a group decision-making context

Social influence in consensus formation was examined using a notion of sociocognitive network. Given the robustness of shared information in determining group decisions, the authors propose the…