Adaptive Bayesian personalized ranking for heterogeneous implicit feedbacks

Pan, Weike; Zhong, Hao; Xu, Chong‐Yu; Ming, Zhong

doi:10.1016/j.knosys.2014.09.013

Cited by 157 publications

(71 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It also uses the same type of implicit feedback, that is, homogeneous implicit feedback. The BRP [58] method inspired other works, such as the ABRP [59] which generalizes the BRP [58] for homogeneous implicit feedback. The main assumption of the BRP model is that if an item has been viewed by the user then it is assumed that the user prefers this item over all other non-observed items.…”

Section: Implicit and Explicit Feedbackmentioning

confidence: 99%

See 1 more Smart Citation

Utilizing transfer learning for in-domain collaborative filtering

Grolman

Bar

Shapira

et al. 2016

Knowledge-Based Systems

View full text Add to dashboard Cite

Section: Implicit and Explicit Feedbackmentioning

confidence: 99%

“…In addition, this solution biases the recommendation results because some of the missing data might be positive. However, these studies [58,59] showed how important it is to use implicit data in recommender systems.…”

Section: Implicit and Explicit Feedbackmentioning

confidence: 99%

Utilizing transfer learning for in-domain collaborative filtering

Grolman

Bar

Shapira

et al. 2016

Knowledge-Based Systems

View full text Add to dashboard Cite

“…As contrasted with the articles discussed, Frey and Dueck [10] presented a representative examplar procedure not requiring reduction to a univariate model. To allocate additional replications, the indifference zone procedures used a least-favorable configuration where the optimal computing budget allocation and Bayesian decision-theoretic methods used an average case analysis [5,8,33]. All three procedures are applicable to both two-stage and sequential procedures.…”

Section: Statistical Selection Methods and Random Search Methodsmentioning

confidence: 99%

Optimized combinatorial clustering for stochastic processes

Kim¹,

Lee

Song

et al. 2017

Cluster Comput

View full text Add to dashboard Cite

As a new data processing era like Big Data, Cloud Computing, and Internet of Things approaches, the amount of data being collected in databases far exceeds the ability to reduce and analyze these data without the use of automated analysis techniques, data mining. As the importance of data mining has grown, one of the critical issues to emerge is how to scale data mining techniques to larger and complex databases so that it is particularly imperative for computationally intensive data mining tasks such as identifying natural clusters of instances. In this paper, we suggest an optimized combinatorial clustering algorithm for noisy performance which is essential for large data with random sampling. The algorithm outperforms conventional approaches through various numerical and qualitative thresholds like mean and standard deviation of accuracy and computation speed.

show abstract

“…The entry f ui represents the proportion of photos favored by u that are related to POI i, and f ui = 0 when no user favoring patterns are observed from u for i. Distinguished from the binary implicit feedback in conventional recommender systems [Pan et al 2015], the user-POI matrices Z and F convey richer graded information to discriminate different degrees of user preferences.…”

Section: Pairwise Preference Miningmentioning

confidence: 99%

Augmented Collaborative Filtering for Sparseness Reduction in Personalized POI Recommendation

Cui

Shen

Nie

et al. 2017

ACM Trans. Intell. Syst. Technol.

View full text Add to dashboard Cite

As mobile device penetration increases, it has become pervasive for images to be associated with locations in the form of geotags. Geotags bridge the gap between the physical world and the cyberspace, giving rise to new opportunities to extract further insights into user preferences and behaviors. In this article, we aim to exploit geotagged photos from online photo-sharing sites for the purpose of personalized Point-of-Interest (POI) recommendation. Owing to the fact that most users have only very limited travel experiences, data sparseness poses a formidable challenge to personalized POI recommendation. To alleviate data sparseness, we propose to augment current collaborative filtering algorithms along from multiple perspectives. Specifically, hybrid preference cues comprising user-uploaded and user-favored photos are harvested to study users' tastes. Moreover, heterogeneous high-order relationship information is jointly captured from user social networks and POI multimodal contents with hypergraph models. We also build upon the matrix factorization algorithm to integrate the disparate sources of preference and relationship information, and apply our approach to directly optimize user preference rankings. Extensive experiments on a large and publicly accessible dataset well verified the potential of our approach for addressing data sparseness and offering quality recommendations to users, especially for those who have only limited travel experiences.

show abstract

Adaptive Bayesian personalized ranking for heterogeneous implicit feedbacks

Cited by 157 publications

References 23 publications

Utilizing transfer learning for in-domain collaborative filtering

Utilizing transfer learning for in-domain collaborative filtering

Optimized combinatorial clustering for stochastic processes

Augmented Collaborative Filtering for Sparseness Reduction in Personalized POI Recommendation

Contact Info

Product

Resources

About