Convolutional Matrix Factorization for Document Context-Aware Recommendation

Kim, Yejin; Park, Chanyoung; Oh, Jinoh; Lee, Sungyoung; Yu, Hwanjo

doi:10.1145/2959100.2959165

Cited by 651 publications

(388 citation statements)

References 15 publications

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“…Since we set the depths of f X and f Y the same for all experiments, we define the depth of DACONA as the depth of f X or that of f Y . DACONA-k denotes DACONA with depth k. The structures of DACONA-1, DACONA-2, DACONA-3, DACONA-4, and DACONA-5 are [10], [20,10], [40,20,10], [80,40,20,10], and [160,80,40,20,10], respectively. Figure 5 shows RMSEs of DACONA-1 to DACONA-5 on real-world datasets.…”

Section: E Neural Network (Q4)mentioning

confidence: 99%

“…However, they only utilize a rating matrix although additional information is available in many services. In recent years, numbers of algorithms have been proposed to use additional information (e.g., social networks [11]- [16], item characteristics [5], [10], [17], and item synopsis [18]- [20]) as well as rating data to improve the performance of recommendation, and they have shown that utilizing both rating data and auxiliary data helps improve the accuracy of rating prediction; thus, effective usage of auxiliary data beyond the rating data has become an important issue in recommendation. We call them as data context-aware recommendation, which is different from context-aware recommendation systems (CARS) that consider users' specific situation (e.g., time, place and weather, etc).…”

Section: Introductionmentioning

confidence: 99%

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Data Context Adaptation for Accurate Recommendation with Additional Information

Jeon

Koo

Kang

2019

2019 IEEE International Conference on Big Data (Big Data)

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Given a sparse rating matrix and an auxiliary matrix of users or items, how can we accurately predict missing ratings considering different data contexts of entities? Many previous studies proved that utilizing the additional information with rating data is helpful to improve the performance. However, existing methods are limited in that 1) they ignore the fact that data contexts of rating and auxiliary matrices are different, 2) they have restricted capability of expressing independence information of users or items, and 3) they assume the relation between a user and an item is linear.We propose DACONA, a neural network based method for recommendation with a rating matrix and an auxiliary matrix. DACONA is designed with the following three main ideas. First, we propose a data context adaptation layer to extract pertinent features for different data contexts. Second, DACONA represents each entity with latent interaction vector and latent independence vector. Unlike previous methods, both of the two vectors are not limited in size. Lastly, while previous matrix factorization based methods predict missing values through the inner-product of latent vectors, DACONA learns a non-linear function of them via a neural network. We show that DACONA is a generalized algorithm including the standard matrix factorization and the collective matrix factorization as special cases. Through comprehensive experiments on real-world datasets, we show that DACONA provides the state-of-the-art accuracy.

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Section: E Neural Network (Q4)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data Context Adaptation for Accurate Recommendation with Additional Information

Jeon

Koo

Kang

2019

2019 IEEE International Conference on Big Data (Big Data)

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“…The approach uses an adaptive item clustering algorithm to address the cold start problem and improve the learning speed. On the other hand, authors of [69] proposed a context-aware recommendation model that integrates convolutional neural network (CNN) into probabilistic matrix factorization (PMF) to capture the contextual information of a document to enhance the rating accuracy. The authors of [70] have also introduced a flexible architecture using neural citation network (NCN) capable of incorporating author metadata for context-aware citation recommendation.…”

Section: (2) E-documents Domainmentioning

confidence: 99%

Context-Aware Recommender System: A Review of Recent Developmental Process and Future Research Direction

et al. 2017

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Intelligent data handling techniques are beneficial for users; to store, process, analyze and access the vast amount of information produced by electronic and automated devices. The leading approach is to use recommender systems (RS) to extract relevant information from the vast amount of knowledge. However, early recommender systems emerged without the cognizance to contextualize information regarding users' recommendations. Considering the historical methodological limitations, Context-Aware Recommender Systems (CARS) are now deployed, which leverage contextual information in addition to the classical two-dimensional search processes, providing better-personalized user recommendations. This paper presents a review of recent developmental processes as a fountainhead for the research of a context-aware recommender system. This work contributes by taking an integrated approach to the complete CARS developmental process, unlike other review papers, which only address a specific aspect of the CARS process. First, an in-depth review is presented pertaining to the state-of-the-art and classified literature, considering the domain of the application models, filters, extraction and evaluation approaches. Second, viewpoints are presented relating to the extraction of literature with analysis on the merit and demerit of each, and the evolving processes between them. Finally, the outstanding challenges and opportunities for future research directions are highlighted.

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“…For example, [19] proposed to employ a convolutional neural network to facilitate the learning of matrix factorization. Similarly, [35] utilized an attention-based convolutional neural network for modeling review documents, and achieved state-of-the-art results in the task of rating prediction.…”

Section: Deep Neural Network In Natural Language Processingmentioning

confidence: 99%

“…(v) JMARS: Jointly Modeling Aspects, Ratings, and Sentiments (JMARS) [8] is another state-of-the-art probabilistic model that combines collaborative filtering and topic modeling. (vi) ConvMF+: Convolutional Matrix Factorization (ConvMF) [19] is a newly proposed recommendation model that employs a convolutional neural network for learning item features from item review documents. ConvMF+ refers to the ConvMF model initialized with pre-trained word embeddings.…”

Section: Baseline Modelsmentioning

confidence: 99%

Coevolutionary Recommendation Model

Dong

Smyth

2018

Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18

122

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Collaborative filtering (CF) is a common recommendation approach that relies on user-item ratings. However, the natural sparsity of user-item rating data can be problematic in many domains and settings, limiting the ability to generate accurate predictions and effective recommendations. Moreover, in some CF approaches latent features are often used to represent users and items, which can lead to a lack of recommendation transparency and explainability. User-generated, customer reviews are now commonplace on many websites, providing users with an opportunity to convey their experiences and opinions of products and services. As such, these reviews have the potential to serve as a useful source of recommendation data, through capturing valuable sentiment information about particular product features. In this paper, we present a novel deep learning recommendation model, which co-learns user and item information from ratings and customer reviews, by optimizing matrix factorization and an attention-based GRU network. Using real-world datasets we show a significant improvement in recommendation performance, compared to a variety of alternatives. Furthermore, the approach is useful when it comes to assigning intuitive meanings to latent features to improve the transparency and explainability of recommender systems. ACM Reference Format:

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Convolutional Matrix Factorization for Document Context-Aware Recommendation

Cited by 651 publications

References 15 publications

Data Context Adaptation for Accurate Recommendation with Additional Information

Data Context Adaptation for Accurate Recommendation with Additional Information

Context-Aware Recommender System: A Review of Recent Developmental Process and Future Research Direction

Coevolutionary Recommendation Model

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