A Dual Input-aware Factorization Machine for CTR Prediction

Lu, Wantong; Yu, Yanfang; Chang, Yongzhe; Wang, Zhen; Li, Chenhui; Yuan, Bo

doi:10.24963/ijcai.2020/434

Cited by 44 publications

(36 citation statements)

References 12 publications

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“…Native ad is a special kind of display ads which has similar form with the native content displayed in online websites [25]. CTR prediction for display ads has been extensively studied [5,10,19,21,23,37,44,45]. Diferent from search ads where the search query triggering ad impressions can provide clear user intent, in display ads there is no explicit user intent [45].…”

Section: Related Work 21 Ctr Predictionmentioning

confidence: 99%

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FedCTR: Federated Native Ad CTR Prediction with Cross-platform User Behavior Data

Lyu³

et al. 2022

ACM Trans. Intell. Syst. Technol.

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Native ad is a popular type of online advertisement which has similar forms with the native content displayed on websites. Native ad click-through rate (CTR) prediction is useful for improving user experience and platform revenue. However, it is challenging due to the lack of explicit user intent, and user behaviors on the platform with native ads may be insufficient to infer users’ interest in ads. Fortunately, user behaviors exist on many online platforms which can provide complementary information for user interest mining. Thus, leveraging multi-platform user behaviors is useful for native ad CTR prediction. However, user behaviors are highly privacy-sensitive, and the behavior data on different platforms cannot be directly aggregated due to user privacy concerns and data protection regulations. Existing CTR prediction methods usually require centralized storage of user behavior data for user modeling, which cannot be directly applied to the CTR prediction task with multi-platform user behaviors. In this paper, we propose a federated native ad CTR prediction method named FedCTR, which can learn user interest representations from cross-platform user behaviors in a privacy-preserving way. On each platform a local user model learns user embeddings from the local user behaviors on that platform. The local user embeddings from different platforms are uploaded to a server for aggregation, and the aggregated ones are sent to the ad platform for CTR prediction. Besides, we apply local differential privacy (LDP) and differential privacy (DP) to the local and aggregated user embeddings respectively for better privacy protection. Moreover, we propose a federated framework for collaborative model training with distributed models and user behaviors. Extensive experiments on real-world dataset show that FedCTR can effectively leverage multi-platform user behaviors for native ad CTR prediction in a privacy-preserving manner.

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Section: Related Work 21 Ctr Predictionmentioning

confidence: 99%

“…• DIFM [23], a dual input-aware factorization machine for CTR Prediction. Same ad and behavior features are used.…”

Section: Ctr Prediction Performancementioning

confidence: 99%

FedCTR: Federated Native Ad CTR Prediction with Cross-platform User Behavior Data

Lyu³

et al. 2022

ACM Trans. Intell. Syst. Technol.

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“…Recently, input-dependent models have shown effectiveness in various domains, such as language modeling [27], [28] and computer vision [29], [30]. In recommendation, IFM [31] and DIFM [32] are presented to re-weight the representations of features and weights for different input instances before performing feature interactions. Inspired by these studies, we design a dynamic transformer encoder which performs an individual attention network on the self-attention layer and enables modeling user-specific intra-item patterns unveiled by the user intentions.…”

Section: Related Workmentioning

confidence: 99%

Attention over Self-attention:Intention-aware Re-ranking with Dynamic Transformer Encoders for Recommendation

Lin¹,

Zang²,

Wang³

et al. 2022

Preprint

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Re-ranking models refine the item recommendation list generated by the prior global ranking model with intra-item relationships. However, most existing re-ranking solutions refine recommendation list based on the implicit feedback with a shared re-ranking model, which regrettably ignore the intra-item relationships under diverse user intentions. In this paper, we propose a novel Intention-aware Re-ranking Model with Dynamic Transformer Encoder (RAISE), aiming to perform user-specific prediction for each target user based on her intentions. Specifically, we first propose to mine latent user intentions from text reviews with an intention discovering module (IDM). By differentiating the importance of review information with a co-attention network, the latent user intention can be explicitly modeled for each user-item pair. We then introduce a dynamic transformer encoder (DTE) to capture user-specific intra-item relationships among item candidates by seamlessly accommodating the learnt latent user intentions via IDM. As such, RAISE is able to perform user-specific prediction without increasing the depth (number of blocks) and width (number of heads) of the prediction model. Empirical study on four public datasets shows the superiority of our proposed RAISE, with up to 13.95%, 12.30%, and 13.03% relative improvements evaluated by Precision, MAP, and NDCG respectively.

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“…The Hit Radio (HR) [35] represents the hit rate, which is the proportion of learners who have K correct recommendations in the recommended course list. The denominator GT represents all test sets, and the numerator NumberHits@K represents the sum of the number of test sets in each learner's Top-N list.…”

Section: Evaluation Indicatorsmentioning

confidence: 99%

A Deep Learning Framework for Multimodal Course Recommendation Based on LSTM+Attention

et al. 2022

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With the impact of COVID-19 on education, online education is booming, enabling learners to access various courses. However, due to the overload of courses and redundant information, it is challenging for users to quickly locate courses they are interested in when faced with a massive number of courses. To solve this problem, we propose a deep course recommendation model with multimodal feature extraction based on the Long- and Short-Term Memory network (LSTM) and Attention mechanism. The model uses course video, audio, and title and introduction for multimodal fusion. To build a complete learner portrait, user demographic information, explicit and implicit feedback data were added. We conducted extensive and exhaustive experiments based on real datasets, and the results show that the AUC obtained a score of 79.89%, which is significantly higher than similar algorithms and can provide users with more accurate recommendation results in course recommendation scenarios.

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A Dual Input-aware Factorization Machine for CTR Prediction

Cited by 44 publications

References 12 publications

FedCTR: Federated Native Ad CTR Prediction with Cross-platform User Behavior Data

FedCTR: Federated Native Ad CTR Prediction with Cross-platform User Behavior Data

Attention over Self-attention:Intention-aware Re-ranking with Dynamic Transformer Encoders for Recommendation

A Deep Learning Framework for Multimodal Course Recommendation Based on LSTM+Attention

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