Recurrent Poisson Factorization for Temporal Recommendation

Hosseini, Seyed Abbas; Khodadadi, Ali; Alizadeh, Keivan; Arabzadeh, Ali; Farajtabar, Mehrdad; Zha, Hongyuan; Rabiee, Hamid R.

doi:10.1109/tkde.2018.2879796

Cited by 28 publications

(11 citation statements)

References 16 publications

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“…HRPF and DHPR: Two Poisson factorization models proposed in (Hosseini et al 2018) that do not require user-network as auxiliary features. These models, however, do not directly model the timedependencies between the future and the past, thus cannot quantify activity self-excitement.…”

Section: Baselinesmentioning

confidence: 99%

Relaxed Clustered Hawkes Process for Procrastination Modeling in MOOCs

Yao,

Zhao,

Sahebi

et al. 2021

Preprint

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Hawkes processes have been shown to be efficient in modeling bursty sequences in a variety of applications, such as finance and social network activity analysis. Traditionally, these models parameterize each process independently and assume that the history of each point process can be fully observed. Such models could however be inefficient or even prohibited in certain real-world applications, such as in the field of education, where such assumptions are violated. Motivated by the problem of detecting and predicting student procrastination in students Massive Open Online Courses (MOOCs) with missing and partially observed data, in this work, we propose a novel personalized Hawkes process model (RCHawkes-Gamma) that discovers meaningful student behavior clusters by jointly learning all partially observed processes simultaneously, without relying on auxiliary features. Our experiments on both synthetic and real-world education datasets show that RCHawkes-Gamma can effectively recover student clusters and their temporal procrastination dynamics, resulting in better predictive performance of future student activities. Our further analyses of the learned parameters and their association with student delays show that the discovered student clusters unveil meaningful representations of various procrastination behaviors in students.

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Section: Baselinesmentioning

confidence: 99%

Relaxed Clustered Hawkes Process for Procrastination Modeling in MOOCs

Yao,

Zhao,

Sahebi

et al. 2021

Preprint

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“…We used the last 20 (computed empirically) inter-arrival times as features. (2) HPP: we also used a homogeneous Poisson process (HPP) [5] which models an exponentially distributed inter-arrival times with a fix rate λ. The rate parameter was learned based on the maximum likelihood approach.…”

Section: Baselines and Evaluation Metricsmentioning

confidence: 99%

Modeling User Return Time Using Inhomogeneous Poisson Process

Akbari

Cetoli²,

Bragaglia³

et al. 2019

Lecture Notes in Computer Science

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For Intelligent Assistants (IA), user activity is often used as a lag metric for user satisfaction or engagement. Conversely, predictive leading metrics for engagement can be helpful with decision making and evaluating changes in satisfaction caused by new features. In this paper, we propose User Return Time (URT), a fine grain metric for gauging user engagement. To compute URT, we model continuous inter-arrival times between users' use of service via a log Gaussian Cox process (LGCP), a form of inhomogeneous Poisson process which captures the irregular variations in user usage rate and personal preferences typical of an IA. We show the effectiveness of the proposed approaches on predicting the return time of users on real-world data collected from an IA. Experimental results demonstrate that our model is able to predict user return times reasonably well and considerably better than strong baselines that make the prediction based on past utterance frequency.

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“…It is worth emphasizing that temporal dynamic learning is different from time-aware recommendations. The former aims to find changes of users or items over time, while the latter intends to suggest users with the interaction time on items [33], [34].…”

Section: Temporal Dynamic Learningmentioning

confidence: 99%

SEMAX: Multi-Task Learning for Improving Recommendations

Zhang

Chow

2019

IEEE Access

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Personalization plays an essential role in recommender systems, in which the key task is to predict personalized ratings of users on new items. Recently, a lot of work investigates deep learning-based collaborative filtering techniques to increase the accuracy of rating prediction. However, most exiting works focus on the recommendation task itself. Actually, the multi-task learning exploits an inductive transfer mechanism to enhance the generalization performance of the main task by using the domain information contained in other related tasks. Multi-task learning has shown effectiveness in various real-world problems, including regression and machine translation. To this end, this study proposes a new framework, called SEMAX that extends our previous model SEMA via multi-task learning for improving recommendations, in which the recommendation task gets the domain information from the other task. Specifically, in the recommendation task, SEMAX learns semantic meanings from texts and temporal dynamics from text sequences for both users and items based on our developed hierarchical and symmetrical recurrent neural networks (RNNs) with the long short-term memory. Furthermore, SEMAX exploits the related task that predicts the rating of a text written by a user for an item to reinforce the recommendation task that predicts the rating of the user on the item, because the text can be an important predictor of the rating given by the user to the item. Moreover, SEMAX predicts the rating of a text based on an attention mechanism to choose user-item-specific words so as to generalize the performance of learned word embeddings, user and item representations. Finally, we conduct a comprehensive evaluation for SEMAX using two large-scale realworld review datasets collected from Amazon and Yelp. The experimental results show that the SEMAX achieves significantly superior performance compared to other state-of-the-art recommendation techniques. INDEX TERMS Rating prediction, semantic meanings, temporal dynamics, multi-task learning, attention.

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Recurrent Poisson Factorization for Temporal Recommendation

Cited by 28 publications

References 16 publications

Relaxed Clustered Hawkes Process for Procrastination Modeling in MOOCs

Relaxed Clustered Hawkes Process for Procrastination Modeling in MOOCs

Modeling User Return Time Using Inhomogeneous Poisson Process

SEMAX: Multi-Task Learning for Improving Recommendations

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