Optimizing Factorization Machines for Top-N Context-Aware Recommendations

Yuan, Fajie; Guo, Guibing; Jose, Joemon M.; Chen, Long; Yu, Haitao; Zhang, Weinan

doi:10.1007/978-3-319-48740-3_20

Cited by 13 publications

(14 citation statements)

References 25 publications

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“…Researchers attempted to fill the gap where relations between users and items in contextual-aware recommender systems are missing. In other words, some context-aware recommender systems require supervision, manual tuning, or use very strict assumptions [148] [149], or assume that different contexts have the same weights [150]. Yuan et al [148] proposed to optimize factorization machines (FM) for the item recommendation task based on implicit feedback (number of purchases, clicks, played songs, ..., etc.).…”

Section: B Contextual Awareness In Recommender Systemsmentioning

confidence: 99%

“…In other words, some context-aware recommender systems require supervision, manual tuning, or use very strict assumptions [148] [149], or assume that different contexts have the same weights [150]. Yuan et al [148] proposed to optimize factorization machines (FM) for the item recommendation task based on implicit feedback (number of purchases, clicks, played songs, ..., etc.). This was done by (1) adopting a FM as ranking function to model the interaction between context information (i.e., the duration a song is played), then apply it to the learningto-rank (LtR) method by using pairwise cross-entropy (CE) loss, this procedure is called RankingFM; and (2) comparing the top-N recommendation performances of RankingFM by adapting the original lambda weighting function (Lamb-daRank) with two alternative sampling schemes, this process is called LambdaFM.…”

Section: B Contextual Awareness In Recommender Systemsmentioning

confidence: 99%

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Contextual Awareness in Human-Advanced-Vehicle Systems: A Survey

et al. 2019

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Autonomous vehicles are becoming a reality in places with advanced infrastructure to support their operations. In crowded places, harsh environments, missions that require these vehicles to be aware of the context in which they are operating, and situations requiring continuous coordination with humans such as in disaster relief, advanced-vehicle systems (AVSs) need to be better contextually aware. The vast literature referring to ''context-aware systems'' is still sparse, focusing on very limited forms of contextual awareness. It requires a structured approach to bring it together to truly realize contextual awareness in AVSs. This paper uses a human-AVSs (HAVSs) lens to polarize the literature in a coherent form suitable for designing distributed HAVSs. We group the relevant literature into two categories: contextual-awareness related to the vehicle infrastructure itself that enables AVSs to operate, and contextual-awareness related to HAVSs. The former category focuses on the communication backbone for AVSs including ad-hoc networks, services, wireless communication, radio systems, and the cyber security and privacy challenges that arise in these contexts. The latter category covers recommender systems, which are used to coordinate the actions that sit at the interface of the human and AVSs, human-machine interaction issues, and the activity recognition systems as the enabling technology for recommender systems to operate autonomously. The structured analysis of the literature has identified a number of open research questions and opportunities for further research in this area.

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Section: B Contextual Awareness In Recommender Systemsmentioning

confidence: 99%

Section: B Contextual Awareness In Recommender Systemsmentioning

confidence: 99%

Contextual Awareness in Human-Advanced-Vehicle Systems: A Survey

et al. 2019

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“…For instance, TFMAP [27] employs TF to capture the 3-way user-item-context relations, and learns it by maximizing Mean Average Precision (MAP); in the same light, CARS explores multiple objective functions to train a novel TF model. Ranking FM [18,31,32,10], on the other side, aims to exploit FM as the rating function to model the pairwise feature interaction, and to build the ranking algorithm by maximizing various ranking measures such as the Area Under the ROC Curve (AUC) and the Normalized Discount Cumulative Gain (NDCG).…”

Section: Feature-based Recommendationmentioning

confidence: 99%

“…Examples for implicit feedback are clicks, purchases, watched videos or played songs, which are much cheaper to obtain since a user does not have to express his taste explicitly. Implicit feedback is often one-class [22], i.e., only positive class is available, and thus algorithms optimized for multiple classes cannot be directly applied for implicit feedback data [31,32]. In this paper, we study the problem of optimizing item ranking with implicit feedback and feature information.…”

Section: Introductionmentioning

confidence: 99%

BoostFM

Yuan¹,

Guo

Jose

et al. 2017

Proceedings of the 22nd International Conference on Intelligent User Interfaces

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Feature-based matrix factorization techniques such as Factorization Machines (FM) have been proven to achieve impressive accuracy for the rating prediction task. However, most common recommendation scenarios are formulated as a top-N item ranking problem with implicit feedback (e.g., clicks, purchases) rather than explicit ratings. To address this problem, with both implicit feedback and feature information, we propose a feature-based collaborative boosting recommender called BoostFM, which integrates boosting into factorization models during the process of item ranking. Specifically, BoostFM is an adaptive boosting framework that linearly combines multiple homogeneous component recommenders, which are repeatedly constructed on the basis of the individual FM model by a re-weighting scheme. Two ways are proposed to efficiently train the component recommenders from the perspectives of both pairwise and listwise Learning-to-Rank (L2R). The properties of our proposed method are empirically studied on three real-world datasets. The experimental results show that BoostFM outperforms a number of state-of-the-art approaches for top-N recommendation.

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“…The other side of the intuition is that low ranked positive items contribute similarly as high ranked non-positive items during the training process, which would be provided with more details later. 3 Due to space limitations, we leave out more details and refer the interested readers to [29,19] for the technical part.…”

Section: Lambda Strategiesmentioning

confidence: 99%

LambdaFM

Yuan

Guo

Jose

et al. 2016

Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

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State-of-the-art item recommendation algorithms, which apply Factorization Machines (FM) as a scoring function and pairwise ranking loss as a trainer (PRFM for short), have been recently investigated for the implicit feedback based context-aware recommendation problem (IFCAR). However, good recommenders particularly emphasize on the accuracy near the top of the ranked list, and typical pairwise loss functions might not match well with such a requirement. In this paper, we demonstrate, both theoretically and empirically, PRFM models usually lead to non-optimal item recommendation results due to such a mismatch. Inspired by the success of LambdaRank, we introduce Lambda Factorization Machines (LambdaFM), which is particularly intended for optimizing ranking performance for IFCAR. We also point out that the original lambda function suffers from the issue of expensive computational complexity in such settings due to a large amount of unobserved feedback. Hence, instead of directly adopting the original lambda strategy, we create three effective lambda surrogates by conducting a theoretical analysis for lambda from the top-N optimization perspective. Further, we prove that the proposed lambda surrogates are generic and applicable to a large set of pairwise ranking loss functions. Experimental results demonstrate LambdaFM significantly outperforms state-of-the-art algorithms on three real-world datasets in terms of four standard ranking measures.

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Optimizing Factorization Machines for Top-N Context-Aware Recommendations

Cited by 13 publications

References 25 publications

Contextual Awareness in Human-Advanced-Vehicle Systems: A Survey

Contextual Awareness in Human-Advanced-Vehicle Systems: A Survey

BoostFM

LambdaFM

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