Fast online learning through offline initialization for time-sensitive recommendation

Agarwal, Deepak; Chen, Bee-Chung; Elango, Pradheep

doi:10.1145/1835804.1835894

Cited by 57 publications

(35 citation statements)

References 24 publications

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“…Recently, matrix factorization has become a popular direction for collaborative filtering [2,11,15,16]. These methods are shown to be effective in many applications.…”

Section: Collaborative Filteringmentioning

confidence: 99%

Learning binary codes for collaborative filtering

Zhou

Zha

2012

Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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This paper tackles the efficiency problem of making recommendations in the context of large user and item spaces. In particular, we address the problem of learning binary codes for collaborative filtering, which enables us to efficiently make recommendations with time complexity that is independent of the total number of items. We propose to construct binary codes for users and items such that the preference of users over items can be accurately preserved by the Hamming distance between their respective binary codes. By using two loss functions measuring the degree of divergence between the training and predicted ratings, we formulate the problem of learning binary codes as a discrete optimization problem. Although this optimization problem is intractable in general, we develop effective relaxations that can be efficiently solved by existing methods. Moreover, we investigate two methods to obtain the binary codes from the relaxed solutions. Evaluations are conducted on three public-domain data sets and the results suggest that our proposed method outperforms several baseline alternatives.

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“…Recently, matrix factorization has become a popular direction for collaborative filtering [2,11,15,16]. These methods are shown to be effective in many applications.…”

Section: Collaborative Filteringmentioning

confidence: 99%

Learning binary codes for collaborative filtering

Zhou

Zha

2012

Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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“…Depending on different applications and settings, there are many variants of the problem such as selecting articles published on portal websites [Agarwal et al 2008;Agarwal et al 2010], news personalization [Das et al 2007;Li et al 2010], computational advertising [Broder 2008;Richardson et al 2007] and many others. Since the Web content optimization problem is a variation of personalized recommendation problems, we summarize below some previous work in recommender systems that are relevant to our work.…”

Section: Personalized Recommendation and Content Optimizationmentioning

confidence: 99%

Exploiting User Preference for Online Learning in Web Content Optimization Systems

Bian

Long

et al. 2014

ACM Trans. Intell. Syst. Technol.

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Web portal services have become an important medium to deliver digital content, e.g. news, advertisements, etc., to Web users in a timely fashion. To attract more users to various content modules on the Web portal, it is necessary to design a recommender system that can effectively achieve Web portal content optimization by automatically estimating content items' attractiveness and relevance to users' interests. The state-of-the-art online learning methodology adapts dedicated pointwise models to independently estimate the attractiveness score for each candidate content item. Although such pointwise models can be easily adapted for online recommendation, there still remain a few critical problems. First, this pointwise methodology fails to use invaluable user preferences between content items. Moreover, the performance of pointwise models decreases drastically when facing the problem of sparse learning samples. To address these problems, we propose to explore a new dynamic pairwise learning methodology for Web portal content optimization, in which we exploit dynamic user preferences extracted based on users' actions on portal services to compute the attractiveness scores of content items. In this paper, we introduce two specific pairwise learning algorithms, a straightforward graph-based algorithm and a formalized Bayesian modeling one. Experiments on large-scale data from a commercial Web portal demonstrate the significant improvement of pairwise methodologies over the baseline pointwise models. Further analysis illustrates that our new pairwise learning approaches can benefit personalized recommendation more than pointwise models, since the data sparsity is more critical for personalized content optimization.

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“…The Fast Online Bilinear Factor Model (FOBFM) [1] addresses the related task of click through rate prediction. They combine offline training with online updates in a principled framework.…”

Section: Related Workmentioning

confidence: 99%

Real-time top-n recommendation in social streams

Diaz-Aviles

Drumond

Schmidt-Thieme

et al. 2012

Proceedings of the Sixth ACM Conference on Recommender Systems

133

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The Social Web is successfully established, and steadily growing in terms of users, content and services. People generate and consume data in real-time within social networking services, such as Twitter, and increasingly rely upon continuous streams of messages for real-time access to fresh knowledge about current affairs. In this paper, we focus on analyzing social streams in real-time for personalized topic recommendation and discovery. We consider collaborative filtering as an online ranking problem and present Stream Ranking Matrix Factorization -RMFX -, which uses a pairwise approach to matrix factorization in order to optimize the personalized ranking of topics. Our novel approach follows a selective sampling strategy to perform online model updates based on active learning principles, that closely simulates the task of identifying relevant items from a pool of mostly uninteresting ones. RMFX is particularly suitable for large scale applications and experiments on the 476 million Twitter tweets dataset show that our online approach largely outperforms recommendations based on Twitter's global trend, and it is also able to deliver highly competitive Top-N recommendations faster while using less space than Weighted Regularized Matrix Factorization (WRMF), a state-of-the-art matrix factorization technique for Collaborative Filtering, demonstrating the efficacy of our approach.

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Fast online learning through offline initialization for time-sensitive recommendation

Cited by 57 publications

References 24 publications

Learning binary codes for collaborative filtering

Learning binary codes for collaborative filtering

Exploiting User Preference for Online Learning in Web Content Optimization Systems

Real-time top-n recommendation in social streams

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