An Efficient Second-Order Approach to Factorize Sparse Matrices in Recommender Systems

Luo, Xin; Zhou, MengChu; Li, Shuai; Xia, Yunni; You, Zhu-Hong; Zhu, Qingsheng; Leung, Hareton

doi:10.1109/tii.2015.2443723

Cited by 106 publications

(24 citation statements)

References 44 publications

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“…However, the fact is that the number of users is less than the papers, and even the most popular papers may have a few ratings. While building the user-item rating matrix in the collaborative filtering method, researchers find that rating matrix is very sparse, there are too few ratings and too few correlations between users [111]. If most of the papers have few ratings and each user only rates on a few papers, it is hard to find the similar neighbours for users.…”

Section: B Sparsitymentioning

confidence: 99%

Scientific Paper Recommendation: A Survey

et al. 2019

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Globally, the recommendation services have become important due to the fact that they support e-commerce applications and different research communities. Recommender systems have a large number of applications in many fields, including economic, education, and scientific research. Different empirical studies have shown that the recommender systems are more effective and reliable than the keyword-based search engines for extracting useful knowledge from massive amounts of data. The problem of recommending similar scientific articles in scientific community is called scientific paper recommendation. Scientific paper recommendation aims to recommend new articles or classical articles that match researchers' interests. It has become an attractive area of study since the number of scholarly papers increases exponentially. In this paper, we first introduce the importance and advantages of the paper recommender systems. Second, we review the recommendation algorithms and methods, such as Content-based, collaborative filtering, graph-based, and hybrid methods. Then, we introduce the evaluation methods of different recommender systems. Finally, we summarize the open issues in the paper recommender systems, including cold start, sparsity, scalability, privacy, serendipity, and unified scholarly data standards. The purpose of this survey is to provide comprehensive reviews on the scholarly paper recommendation.

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Section: B Sparsitymentioning

confidence: 99%

Scientific Paper Recommendation: A Survey

et al. 2019

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“…Traditional recommendation models, such as item-based [1], user-based [17] and hybrid algorithms [18] all have been shown the capability of providing higher quality recommendations in various domains [19], [20]. With the increasing number of users and items emerged, the scalability [21], [22], the efficiency [23], [24] and the stability [25] have been studied to extend the traditional recommendation algorithms to meet the huge computation requirement.…”

Section: Related Work a Recommender Systemmentioning

confidence: 99%

Movie Recommendation via Markovian Factorization of Matrix Processes

Zhang

Mao

2019

IEEE Access

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The success of the probabilistic matrix factorization (PMF) model has inspired the rapid development of collaborative filtering algorithms, among which timeSVD++ has demonstrated great performance advantage in solving the movie rating prediction problem. Allowing the model to evolve over time, timeSVD++ accounts for ''concept drift'' in collaborative filtering by heuristically modifying the quadratic optimization problem derived from the PMF model. As such, timeSVD++ no longer carries any probabilistic interpretation. This lack of frameworks makes the generalization of timeSVD++ to other collaborative filtering problems rather difficult. This paper presents a new model family termed Markovian factorization of matrix process (MFMP). On one hand, MFMP models, such as timeSVD++, are capable of capturing the temporal dynamics in the dataset, and on the other hand, they also have clean probabilistic formulations, allowing them to adapt to a wide spectrum of collaborative filtering problems. Two simple example models in this family are introduced for the prediction of movie ratings using time-stamped rating data. The experimental study using MovieLens dataset demonstrates that the two models, although simple and primitive, already have comparable or even better performance than timeSVD++ and a standard tensor factorization model. INDEX TERMS Recommender system, collaborative filtering, matrix factorization.

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“…As a result, it becomes more and more difficult for users to obtain digital contents or services they actually need. As one important solution for information overload problem, recommender systems [1], [2] can reduce search cost effectively and offer users personalized contents or services from enormous amounts of available data. Generally, traditional recommendation methods include collaborative filtering, content-based, context-aware and hybrid models [3].…”

Section: Introductionmentioning

confidence: 99%

Sequential Recommendation Based on Multivariate Hawkes Process Embedding With Attention

Wang

Zhang

Xiang

et al. 2022

IEEE Trans. Cybern.

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Recommender systems are important approaches for dealing with the information overload problem in big data era, and various kinds of auxiliary information, including time and sequential information, can help to improve the performance of retrieval and recommendation tasks. However, it is still a challenging problem how to fully exploit such information to achieve high-quality recommendation results and improve users' experience. In this work, we present a novel sequential recommendation model named Multivariate Hawkes Process Embedding with attention (MHPE-a), which combines a temporal point process with attention mechanism to predict the items that the target user may interact with according to her/his historical records. Specifically, the proposed approach MHPE-a can model users' sequential patterns in their temporal interaction sequences accurately with a multivariate Hawkes process. Then, we perform accurate sequential recommendation to satisfy target users' realtime requirement based on their preferences obtained with MHPE-a from their historical records. Especially, an attention mechanism is used to leverage users' long/short-term preferences adaptively to achieve accurate sequential recommendation. Extensive experiments are conducted on two real-world datasets (lastfm and gowalla), and the results show that MHPE-a achieves better performance than state-of-the-art baselines.

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An Efficient Second-Order Approach to Factorize Sparse Matrices in Recommender Systems

Cited by 106 publications

References 44 publications

Scientific Paper Recommendation: A Survey

Scientific Paper Recommendation: A Survey

Movie Recommendation via Markovian Factorization of Matrix Processes

Sequential Recommendation Based on Multivariate Hawkes Process Embedding With Attention

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