Learning optimal ranking with tensor factorization for tag recommendation

Rendle, Steffen; Marinho, Leandro Balby; Νανόπουλος, Αλέξανδρος; Schmidt-Thieme, Lars

doi:10.1145/1557019.1557100

Cited by 296 publications

(209 citation statements)

References 16 publications

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“…Similar claims have been registered in various research areas such as human action recognition in videos [32], hand written digit recognition [59], image compression [60], object recognition [58], gait recognition [54], Electroencephalogram (EEG) classification [37], Anomaly detection in streaming data [52], dimensionality reduction [39], tag recommendation systems [48] and Link…”

Section: Motivation: Inspirations From Applications Of Tensor Analysismentioning

confidence: 55%

A tensor-based selection hyper-heuristic for cross-domain heuristic search

Asta

Özcan

2015

Information Sciences

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Hyper-heuristics have emerged as automated high level search methodologies that manage a set of low level heuristics for solving computationally hard problems. A generic selection hyper-heuristic combines heuristic selection and move acceptance methods under an iterative single point-based search framework. At each step, the solution in hand is modified after applying a selected heuristic and a decision is made whether the new solution is accepted or not. In this study, we represent the trail of a hyper-heuristic as a third order tensor. Factorization of such a tensor reveals the latent relationships between the low level heuristics and the hyper-heuristic itself. The proposed learning approach partitions the set of low level heuristics into two equal subsets where heuristics in each subset are associated with a separate move acceptance method. Then a multi-stage hyper-heuristic is formed and while solving a given problem instance, heuristics are allowed to operate only in conjunction with the associated acceptance method at each stage. To the best of our knowledge, this is the first time tensor analysis of the space of heuristics is used as a data science approach to improve the performance of a hyper-heuristic in the prescribed manner. The empirical results across six different problem domains from a benchmark indeed indicate the success of the proposed approach.

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Section: Motivation: Inspirations From Applications Of Tensor Analysismentioning

confidence: 55%

A tensor-based selection hyper-heuristic for cross-domain heuristic search

Asta

Özcan

2015

Information Sciences

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“…Due to the usefulness of tag recommendation, many methods have been proposed from different perspectives (Heymann et al, 2008;Krestel et al, 2009;Rendle et al, 2009;Liu et al, 2012;Ding et al, 2013). Heymann et al (Heymann et al, 2008) investigated the tag recommendation problem using the data collected from social bookmarking system.…”

Section: Related Workmentioning

confidence: 99%

Hashtag Recommendation Using Dirichlet Process Mixture Models Incorporating Types of Hashtags

Gong

Zhang

Huang

2015

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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In recent years, the task of recommending hashtags for microblogs has been given increasing attention. Various methods have been proposed to study the problem from different aspects. However, most of the recent studies have not considered the differences in the types or uses of hashtags. In this paper, we introduce a novel nonparametric Bayesian method for this task. Based on the Dirichlet Process Mixture Models (DPMM), we incorporate the type of hashtag as a hidden variable. The results of experiments on the data collected from a real world microblogging service demonstrate that the proposed method outperforms stateof-the-art methods that do not consider these aspects. By taking these aspects into consideration, the relative improvement of the proposed method over the state-of-theart methods is around 12.2% in F1-score.

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“…It has been shown that highorder relational modeling can improve the performance of CF systems in many scenarios, for instance in social tag recommendations [24,26].Here, we focus on one particular three-way relationship: recipient-producer-category of the update. We associate latent factors η x ∈ R k for these three types of entities.…”

Section: Latent Factor Modelsmentioning

confidence: 99%

“…and applied to social media as well (e.g., [24,29]). However, we do not choose the Tucker decomposition for our settings because not only it requires to pre-specify the dimensionality of all factors separately, but also does not guarantee uniqueness of the decomposition result.…”

Section: Latent Factor Modelsmentioning

confidence: 99%

Learning to rank social update streams

Liu

Bekkerman

Adler

et al. 2012

Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval

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As online social media further integrates deeper into our lives, we spend more time consuming social update streams that come from our online connections. Although social update streams provide a tremendous opportunity for us to access information on-the-fly, we often complain about its relevance. Some of us are flooded with a steady stream of information and simply cannot process it in full. Ranking the incoming content becomes the only solution for the overwhelmed users. For some others, in contrast, the incoming information stream is pretty weak, and they have to actively search for relevant information which is quite tedious. For these users, augmenting their incoming content flow with relevant information from outside their first-degree network would be a viable solution. In that case, the problem of relevance becomes even more prominent.In this paper, we start an open discussion on how to build effective systems for ranking social updates from a unique perspective of LinkedIn -the largest professional network in the world. More specifically, we address this problem as an intersection of learning to rank, collaborative filtering, and clickthrough modeling, while leveraging ideas from information retrieval and recommender systems. We propose a novel probabilistic latent factor model with regressions on explicit features and compare it with a number of non-trivial baselines. In addition to demonstrating superior performance of our model, we shed some light on the nature of social updates on LinkedIn and how users interact with them, which might be applicable to social update streams in general.

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Learning optimal ranking with tensor factorization for tag recommendation

Cited by 296 publications

References 16 publications

A tensor-based selection hyper-heuristic for cross-domain heuristic search

A tensor-based selection hyper-heuristic for cross-domain heuristic search

Hashtag Recommendation Using Dirichlet Process Mixture Models Incorporating Types of Hashtags

Learning to rank social update streams

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