Collaborative Intent Prediction with Real-Time Contextual Data

Sun, Yu; Yuan, Nicholas Jing; Xie, Xing; McDonald, Kieran; Zhang, Rui

doi:10.1145/3041659

Cited by 61 publications

(22 citation statements)

References 44 publications

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“…Biased MF is proposed to further enhance the performance of traditional MF in the problem of rating prediction. Researchers in [23,24,26,35,37,40] introduced extra information like review texts and social relations into MF so as to address the rating sparsity issue. Among numerous MF-based approaches, SVD++ has been proven to be the best single model in terms of fitting user ratings.…”

Section: User-based Collaborative Filtering Modelsmentioning

confidence: 99%

Deep Item-based Collaborative Filtering for Top-N Recommendation

Xue

Wang

et al. 2019

ACM Trans. Inf. Syst.

251

126

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Item-based Collaborative Filtering (short for ICF) has been widely adopted in recommender systems in industry, owing to its strength in user interest modeling and ease in online personalization. By constructing a user's profile with the items that the user has consumed, ICF recommends items that are similar to the user's profile. With the prevalence of machine learning in recent years, significant processes have been made for ICF by learning item similarity (or representation) from data. Nevertheless, we argue that most existing works have only considered linear and shallow relationship between items, which are insufficient to capture the complicated decision-making process of users.In this work, we propose a more expressive ICF solution by accounting for the nonlinear and higher-order relationship among items. Going beyond modeling only the second-order interaction (e.g., similarity) between two items, we additionally consider the interaction among all interacted item pairs by using nonlinear neural networks. Through this way, we can effectively model the higher-order relationship among items, capturing more complicated effects in user decision-making. For example, it can differentiate which historical itemsets in a user's profile are more important in affecting the user to make a purchase decision on an item. We treat this solution as a deep variant of ICF, thus term it as DeepICF. To justify our proposal, we perform empirical studies on two public datasets from MovieLens and Pinterest. Extensive experiments verify the highly positive effect of higher-order item interaction modeling with nonlinear neural networks. Moreover, we demonstrate that by more fine-grained second-order interaction modeling with attention network, the performance of our DeepICF method can be further improved.

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Section: User-based Collaborative Filtering Modelsmentioning

confidence: 99%

Deep Item-based Collaborative Filtering for Top-N Recommendation

Xue

Wang

et al. 2019

ACM Trans. Inf. Syst.

251

126

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“…Typically, the well-known regression-based CF method MF, associating each user an item with a latent vector, modeling the rating score of the user by the inner product of the vectors, achieved the best performance in the Netflix challenge. Literature [24], [25], [27], [34], [38], [42] extended MF with additional information such as review texts and social influence.…”

Section: Related Workmentioning

confidence: 99%

Item-Based Collaborative Memory Networks for Recommendation

2020

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Item-based Collaborative Filtering (ICF or Item-based CF) has been widely applied for recommender systems in industrial scenarios, owing to its efficiency in user preference modeling and flexibility in online personalization. It captures a user's preference from his or her historically interacted items, recommending new items that are the most similar to the user's preference. Recently, several works propose advanced neural network architectures to learn item similarities from rating data, by modeling items as latent vectors and learning model parameters by optimizing a recommendation-aware objective function. While much literature attempts to use classical neural networks such as multi-layer perception (MLP), to learn item similarities, there has been relatively less work employing memory networks for ICF, which can more accurately record the detailed information about entities than classical neural networks. Therefore, in this paper, we propose a powerful Item-based Collaborative Memory Network (ICMN) for ICF, which bases on the architecture of Memory Networks. Besides, a neural attention mechanism is adopted to focus on the most important historically interacted items. The core of our ICMN is the cooperation of external and internal memory and the contribution of the neural attention mechanism. Compare to the state-of-the-art ICF methods, our ICMN possesses the merit of powerful representation capability. Extensive experiments on two datasets demonstrate the effectiveness of ICMN. To the best of our knowledge, this is the first attempt that applies memory networks for ICF. INDEX TERMS End-to-end memory networks, Item-based collaborative filtering, Attention mechanism.

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“…Traditional GCNs can only capture the homogeneous relationships among homogeneous entities, which overlooks the rich information among heterogeneous relationships. To address this issue, we use meta-paths [25] as the guidance to capture the heterogeneous context information in a HIN via GCN. In this way, the heterogeneous relationships are utilized in a more natural and intuitive way.…”

Section: Introductionmentioning

confidence: 99%

Attentional Graph Convolutional Networks for Knowledge Concept Recommendation in MOOCs in a Heterogeneous View

Gong

Wang

et al. 2020

Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval

111

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Massive open online courses (MOOCs) are becoming a modish way for education, which provides a large-scale and open-access learning opportunity for students to grasp the knowledge. To attract students' interest, the recommendation system is applied by MOOCs providers to recommend courses to students. However, as a course usually consists of a number of video lectures, with each one covering some specific knowledge concepts, directly recommending courses overlook students' interest to some specific knowledge concepts. To fill this gap, in this paper, we study the problem of knowledge concept recommendation. We propose an end-to-end graph neural network based approach called Attentional Heterogeneous Graph Convolutional Deep Knowledge Recommender (ACKRec) for knowledge concept recommendation in MOOCs. Like other recommendation problems, it suffers from sparsity issue. To address this issue, we leverage both content information and context information to learn the representation of entities via graph convolution network. In addition to students and knowledge concepts, we consider other types of entities (e.g., courses, videos, teachers) and construct a heterogeneous information network (HIN) to capture the corresponding fruitful semantic relationships among different types of entities and incorporate them into the representation learning process. Specifically, we use meta-path on the HIN to guide the propagation of students' preferences. With the help of these meta-paths, the students' preference distribution with respect to a candidate knowledge concept can be captured. Furthermore, we propose an attention mechanism to adaptively fuse the context * The first two authors contributed equally.

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Collaborative Intent Prediction with Real-Time Contextual Data

Cited by 61 publications

References 44 publications

Deep Item-based Collaborative Filtering for Top-N Recommendation

Deep Item-based Collaborative Filtering for Top-N Recommendation

Item-Based Collaborative Memory Networks for Recommendation

Attentional Graph Convolutional Networks for Knowledge Concept Recommendation in MOOCs in a Heterogeneous View

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