In-memory, distributed content-based recommender system

Dooms, Simon; Audenaert, Pieter; Fostier, Jan; Pessemier, Toon De; Martens, Luc

doi:10.1007/s10844-013-0276-1

Cited by 19 publications

(6 citation statements)

References 28 publications

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“…When a recommender system needs distributed computing however, often a MapReduce paradigm is involved (e.g., [39,27,43,11,12]). This requires computational steps to be rewritten as map and reduce functions which is not straightforward to do and causes overhead thus reducing parallel efficiency [13]. In this work we show how the system can embrace distributed computing without the need for MapReduce.…”

Section: Related Workmentioning

confidence: 99%

Online optimization for user-specific hybrid recommender systems

Dooms

Pessemier

Martens

2014

Multimed Tools Appl

Self Cite

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User-specific hybrid recommender systems aim at harnessing the power of multiple recommendation algorithms in a user-specific hybrid scenario. While research has previously focused on self-learning hybrid configurations, such systems are often too complex to take out of the lab and are seldom tested against realworld requirements. In this work, we describe a self-learning user-specific hybrid recommender system and assess its ability towards meeting a set of pre-defined requirements relevant to online recommendation scenarios: responsiveness, scalability, system transparency and user control. By integrating a client-server architectural design, the system was able to scale across multiple computing nodes in a very flexible way. A specific user-interface for a movie recommendation scenario is proposed to illustrate system transparency and user control possibilities, which integrate directly in the hybrid recommendation process. Finally, experiments were performed focusing both on weak and strong scaling scenarios on a high performance computing environment. Results showed performance to be limited only by the slowest integrated recommendation algorithm with very limited hybrid optimization overhead.

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Section: Related Workmentioning

confidence: 99%

Online optimization for user-specific hybrid recommender systems

Dooms

Pessemier

Martens

2014

Multimed Tools Appl

Self Cite

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“…(1) To solve the problem of dependence of traditional calculation methods on common scoring items, the Bhattacharyya similarity is applied to traditional calculation methods. (2) We introduce trust weight to improve the calculation of direct trust, and according to the trust transmission mechanism and the six-degree separation theory, the indirect similarity calculation is improved and integrated into the user's trust degree. (3) is paper proposes a hybrid recommendation algorithm which combines user interest and trust, which effectively alleviates the problem of data sparsity in collaborative filtering recommendation system.…”

Section: Introductionmentioning

confidence: 99%

“…Successfully used in e-commerce and academic research [ 1 ]. There are many classifications of recommendation systems, such as content-based recommendation [ 2 ] and knowledge-based recommendation [ 3 ] and collaborative filtering. Collaborative filtering is the most prominent and commonly used recommendation technology [ 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

A Recommendation Approach for Rating Prediction Based on User Interest and Trust Value

Hai-long

Sun

Cheng

et al. 2021

Computational Intelligence and Neuroscience

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Collaborative filtering recommendation algorithm is one of the most researched and widely used recommendation algorithms in personalized recommendation systems. Aiming at the problem of data sparsity existing in the traditional collaborative filtering recommendation algorithm, which leads to inaccurate recommendation accuracy and low recommendation efficiency, an improved collaborative filtering algorithm is proposed in this paper. The algorithm is improved in the following three aspects: firstly, considering that the traditional scoring similarity calculation excessively relies on the common scoring items, the Bhattacharyya similarity calculation is introduced into the traditional calculation formula; secondly, the trust weight is added to accurately calculate the direct trust value and the trust transfer mechanism is introduced to calculate the indirect trust value between users; finally, the user similarity and user trust are integrated, and the prediction result is generated by the trust weighting method. Experiments show that the proposed algorithm can effectively improve the prediction accuracy of recommendations.

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“…To alleviate these problems and improve rating prediction accuracy, many other prediction models have been proposed, including association rule-based models [10], content-based models [11], and knowledge-based models [12]. However, the above models are only applied from a static perspective to profiling user needs and hard to obtain a high-quality prediction framework for item predictions.…”

Section: Introductionmentioning

confidence: 99%

A Novel Implicit Trust Recommendation Approach for Rating Prediction

Liu

Ren

et al. 2020

IEEE Access

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Rating predictions, as an application that is widely used in recommender systems, have gradually become a valuable way which can help user narrow down their choices quickly and make wise decisions from the vast amount of information. However, most existing collaborative recommendation models suffer from poor accuracy due to data sparsity and cold start problems that recommender systems contain only a few explicit data. To solve this problem, a new implicit trust recommendation approach (ITRA) is proposed to generate item rating prediction by mining and utilizing user implicit information in recommender systems. Specifically, user trust neighbor set that has similar preference and taste with a target user is first obtained by trust expansion strategy via user trust diffusion features in a trust network. Then, the trust ratings mined from user trust neighbors are used to compute trust similarity among users based on user collaborative filtering model. Finally, using the above filtered trust ratings and user trust similarity, the prediction results are generated by a trust weighting method. In addition, the empirical experiments are conducted on three real-world datasets, and the results demonstrate that our rating prediction model has obvious advantages over the state-of-the-art comparison methods in terms of the accuracy of recommendations.

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In-memory, distributed content-based recommender system

Cited by 19 publications

References 28 publications

Online optimization for user-specific hybrid recommender systems

Online optimization for user-specific hybrid recommender systems

A Recommendation Approach for Rating Prediction Based on User Interest and Trust Value

A Novel Implicit Trust Recommendation Approach for Rating Prediction

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