Quote Recommendation in Dialogue using Deep Neural Network

Lee, Hanbit; Ahn, Yeonchan; Lee, Haejun; Ha, Seungdo; Lee, Sang-goo

doi:10.1145/2911451.2914734

Cited by 51 publications

(48 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The designed experiments to test our proposed approaches make use of the MovieLens 1M 1 [45] dataset and the FilmTrust [44] dataset. The tested classification quality measures will mainly be the Precision and Recall ones [1].…”

Section: Experiments and Resultsmentioning

confidence: 99%

Classification-based Deep Neural Network Architecture for Collaborative Filtering Recommender Systems

Bobadilla¹,

Ortega²,

Gutiérrez³

et al. 2020

IJIMAI

View full text Add to dashboard Cite

This paper proposes a scalable and original classification-based deep neural architecture. Its collaborative filtering approach can be generalized to most of the existing recommender systems, since it just operates on the ratings dataset. The learning process is based on the binary relevant/non-relevant vote and the binary voted/ non-voted item information. This data reduction provides a new level of abstraction and it makes possible to design the classification-based architecture. In addition to the original architecture, its prediction process has a novel approach: it does not need to make a large number of predictions to get recommendations. Instead to run forward the neural network for each prediction, our approach runs forward the neural network just once to get a set of probabilities in its categorical output layer. The proposed neural architecture has been tested by using the MovieLens and FilmTrust datasets. A state-of-the-art baseline that outperforms current competitive approaches has been used. Results show a competitive recommendation quality and an interesting quality improvement on large number of recommendations, consistent with the architecture design. The architecture originality makes it possible to address a broad range of future works.

show abstract

Section: Experiments and Resultsmentioning

confidence: 99%

Classification-based Deep Neural Network Architecture for Collaborative Filtering Recommender Systems

Bobadilla¹,

Ortega²,

Gutiérrez³

et al. 2020

IJIMAI

View full text Add to dashboard Cite

show abstract

“…Fourth, it selects scientific papers with higher relevance scores as the recommended list. Lee et al [28] first used a CNN to model the dialogue text information in Twitter and then added LSTM to the CNN layer to establish the dialogue timing relationship. Then, superimposed the Sotfmax unit layer on the top to perform predictions to complete the quotation recommendation on Twitter.…”

Section: Text-based Recommendation Systemmentioning

confidence: 99%

“…Predicting the next location: A recurrent model with spatial and temporal contexts [47] https://github.com/yongqyu/STRNN A neural network approach to jointly modelling https://github.com/thunlp/JNTM A content-collaborative recommender that exploits WordNet-based user profiles for neighbourhood formation [49] https://github.com/groveco/content-engine Collaborative metric learning [52] https://github.com/changun/CollMetric [9] Pinsage 0.591 0.67 [10] STACR-GCN 0.895 [11] NGNN 0.7701 0.96 [12] GraphRec 0.9794 [13] GC-SAN 0.284 0.535 [14] KGAT 0.149 [15] GC-MC 0.905 [17] CR-CA 0.418 [18] Node2vec 0.224 0.155 0.441 [22] IDGCCF 0.976 0.882 0.876 0.976 Text-based recommendation [26] CO-Attention 0.311 0.286 [27] LSTM-CAV 0.724 0.309 [28] CNNRNN 0.532 0.416 0.698 [29] DL 0.288 [30] GRU-MTL 0.605 Behavior-based recommendation system [7] SA-UserCF 0.1571 0.082 0.171 [36] DBNCF 0.7742…”

Section: Behavior-based Recommendation Systemmentioning

confidence: 99%

Recommendation System Based on Heterogeneous Feature: A Survey

Wang

Gong

2020

IEEE Access

View full text Add to dashboard Cite

Recommendation systems have become an important field of research in computer science and physics. In recent years, breakthroughs have been achieved in social, biological, and research cooperation networks. With the popularization of big data and deep learning technology development, graph structures are increasingly being used to represent large-scale and complex data in the real world. In this paper, we reviewed the progress made in recommendation systems research in the past 20 years and comprehensively classified recommender systems based on the heterogeneous input features. We introduced layering in the classification of recommendation systems. Furthermore, we proposed a new hierarchical classification model of recommendation systems divided into three layers: feature input, feature learning, and output layers. In the feature learning layer, existing recommendation systems were divided into graph-based, text-based, behavior-based, spatiotemporal-based, and hybrid recommendation systems. Additionally, we provided evaluation index, open-source implementation , experimental comparison and the relative merits for each recommendation method. Subsequently, future development directions of recommendation systems are discussed.

show abstract

“…Commonly used hybrid recommendation algorithms include weighted hybrid recommendation algorithm, cross-harmonic recommendation algorithm and meta-model mixed recommendation algorithm [24]. For example, Lee [25] learned semantic representation from the context of user conversations by combining recurrent neural networks and convolutional neural networks. Dai [26] proposed a dynamic recommendation algorithm that combines the convolutional neural network and multivariate point process by learning the co-evolutionary model of user-commodity implied features.…”

Section: Hybrid Modelmentioning

confidence: 99%

BRScS: A Hybrid Recommendation Model Fusing Multi-source Heterogeneous Data

Yang

Wang

et al. 2020

Preprint

View full text Add to dashboard Cite

Recommendation systems are often used to solve the problem of information overload on the Internet. Many types of data can be used for recommendation, and fusing diﬀerent types of data can make recommendation more accurate. Most existing fusion recommendation models simply combine the recommendation results from diﬀerent data instead of fully fusing multi-source heterogeneous data to make recommendations. Furthermore, users’ choices are usually aﬀected by their direct and even indirect friends’ preferences. This paper proposes a hybrid recommendation model BRScS (an acronym for BPR-Review-Score-Social). It fully fuses social data, score, and review together, uses improved BPR model to optimize the ranking, and trains them in a joint representation learning framework to get the top-N recommendations. User trust model is used to introduce social relationships into the rating and review data, PV-DBOW model is used to process the review data, and fully connected neural network is used to process the rating data. Experiments on Yelp public dataset show that the BRScS algorithm proposed outperforms other recommendation algorithms such as BRSc, UserCF, HRSc. BRScS model is also scalable and can fuse new type of data easily.

show abstract

Quote Recommendation in Dialogue using Deep Neural Network

Cited by 51 publications

References 10 publications

Classification-based Deep Neural Network Architecture for Collaborative Filtering Recommender Systems

Classification-based Deep Neural Network Architecture for Collaborative Filtering Recommender Systems

Recommendation System Based on Heterogeneous Feature: A Survey

BRScS: A Hybrid Recommendation Model Fusing Multi-source Heterogeneous Data

Contact Info

Product

Resources

About