Modeling Mobile User Actions for Purchase Recommendation using Deep Memory Networks

Gligorijevic, Djordje; Gligorijević, Jelena; Raghuveer, Aravindan; Grbovic, Mihajlo; Obradović, Zoran

doi:10.1145/3209978.3210138

Cited by 18 publications

(10 citation statements)

References 5 publications

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“…Recently, models with representation learning capabilities have also been proposed for CTR and CVR prediction tasks, e.g., factorization machines [15] for CVR or deep residual networks [18] and Siamese networks [8] for CTR that tackle problems of learning nonlinear interactions of features. Also, more prominently, models that capture information from the sequence such are RNNs have been proposed recently [1,5,6,21] and they reportedly perform significantly better than their non-sequential counterparts. Moreover [1] and [22] have used sequences of events from diverse data sources, while [1] has additionally proposed adding temporal information of events as an additional source of information to better model representations for conversion attribution task.…”

Section: Modeling Users' Conversion Predictionmentioning

confidence: 99%

“…An example of one such sequence or trail is provided in the Figure 1 where we observe multiple interactions of the user with different online properties such as mobile and desktop search, email receipts, reading news and interacting with ads. Modeling sequences of user events has been proposed in the past with great success [6,21], however, to the best of our knowledge, it hasn't been used for strictly prospective modeling of users. Moreover, utilizing activities data to the full extent such as temporal aspect has been largely ignored when modeling conversions in DA.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Prospective Modeling of Users for Online Display Advertising via Deep Time-Aware Model

Gligorijevic

Gligorijević

Flores

2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

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Prospective display advertising poses a particular challenge for large advertising platforms. The existing machine learning algorithms are easily biased towards the highly predictable retargeting events that are often non-eligible for the prospective campaigns, thus exhibiting a decline in advertising performance. To that end, efforts are made to design powerful models that can learn from signals of various strength and temporal impact collected about each user from different data sources and provide a good quality and early estimation of users' conversion rates. In this study, we propose a novel deep time-aware approach designed to model sequences of users' activities and capture implicit temporal signals of users' conversion intents. On several real-world datasets, we show that the proposed approach consistently outperforms other, previously proposed approaches by a significant margin while providing interpretability of signal impact to conversion probability. CCS CONCEPTS • Information systems → Display advertising; Web mining; • Computing methodologies → Machine learning.

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Section: Modeling Users' Conversion Predictionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prospective Modeling of Users for Online Display Advertising via Deep Time-Aware Model

Gligorijevic

Gligorijević

Flores

2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

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“…In sequence recommendation, memory neural networks have received increasing attention recently due to their promising performance [30]. Chen et al [28] first introduce the memory mechanism into the recommendation system.…”

Section: B Memory Neural Networkmentioning

confidence: 99%

A Neighbor-Guided Memory-Based Neural Network for Session-Aware Recommendation

2020

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Session-aware recommendation is a special form of session-based recommendation, where users' historical interactions before the current session are available. Among the existing session-aware recommendation studies, recurrent neural network (RNN) is a popular choice to model users' current intent of the ongoing session as well as their general preference implied in previous sessions. However, these RNN-based methods present limited memory so as to have difficulty in characterizing long-term behaviour. In addition, when modeling a user's preference, existing studies mainly refer to his (her) own sessions, while ignore the collaborative information from sessions of other users that may share similar tastes. To tackle the above problems, we propose a neighbor-guided memory-based neural network (MNN) for session-aware recommendation task, which comprehensively considers users' short-term intent, long-term preference and cross-sessions information to yield the final recommendations. Specifically, we design a long-term memory generator to capture users' general preference from their historical sessions, meanwhile leverage the neighbor sessions of current session to obtain cross-session collaborative information. Furthermore, with the guidance of long-term memory and cross-session information, we employ a short-term memory generator to yield users' ongoing intent, which serves as the dominating part of recommendation. Extensive experimental results on three real world datasets show the effectiveness of our model, with improvements up to 12.0% on 30MUSIC, 26.5% on NOWPLAYING and 13.5% on TMALL in terms of Recall@20, respectively. Analysis on current session length and the number of historical interacted items comprehensively demonstrates the superiority of our proposal on processing long sessions and modeling users' long-term behaviour.

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“…In this paper, we explore impact of user sentiment towards a brand (computed as an aggregate of sentiments in relevant news articles read by the user) on the ad click and conversion behavior. In theory, such brand-user sentiment features can be readily consumed by CTR and conversion prediction models used in the advertising industry (which can be as simple as logistic regression [6,24], or more complex deep neural networks as in [16,29]). Again, to the best of our knowledge, there is no prior work quantizing the effect of news article sentiments on ad click and conversion behavior with respect to a brand (or its competitors in the same product category).…”

Section: Online Advertising and Brand Sentimentmentioning

confidence: 99%

Inferring Advertiser Sentiment in Online Articles using Wikipedia Footnotes

Mishra

Pappu

Bhamidipati

2019

Companion Proceedings of the 2019 World Wide Web Conference

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Online advertising platforms in partnerships with media companies typically have access to an online user's history of viewed articles. If a concerned brand (advertiser) plans to run advertisement campaigns on users exposed to negative articles, it is essential to first identify articles with negative sentiment about the brand. For an advertising platform, scalable identification of such articles with little human-annotation effort is necessary for launching campaigns soon after an advertiser signs up. In this context, generic sentiment analysis tools suffer from the lack of contextual world knowledge associated with the advertiser. Human annotation of articles for supervised approaches is laborious and painstaking. To address these problems, we propose the use of publicly available Wikipedia footnote references for an advertiser, and propagate their sentiment to several articles related to the advertiser. In particular, our proposed approach has three components: (i) automatically find Wikipedia references which have negative sentiment about an advertiser, (ii) learn distributed representations (doc2vec) of article texts referred in footnotes and other unlabeled articles, and (iii) inferring sentiment in unlabeled articles using label propagation (from references) in the doc2vec space. Our experiments spanning three real brands, and data from a major advertising platform (Yahoo Gemini) show significant lifts in sentiment inference compared to existing baselines. In addition, we share valuable insights on how article sentiment influences the online activities of a user with respect to a brand. CCS CONCEPTS• Information systems → Online advertising.

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Modeling Mobile User Actions for Purchase Recommendation using Deep Memory Networks

Cited by 18 publications

References 5 publications

Prospective Modeling of Users for Online Display Advertising via Deep Time-Aware Model

Prospective Modeling of Users for Online Display Advertising via Deep Time-Aware Model

A Neighbor-Guided Memory-Based Neural Network for Session-Aware Recommendation

Inferring Advertiser Sentiment in Online Articles using Wikipedia Footnotes

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