RecGAN

Bharadhwaj, Homanga; Park, Homin; Lim, Brian Y.

doi:10.1145/3240323.3240383

Cited by 55 publications

(13 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, because the prediction engine is founded on simple hand-crafted properties, it is not relevant to a large range of activities [ 31 ]. Although the RecGAN [ 11 ] RNN-GAN based technique has been found to perform well in cold-start recommendations, there is no theoretical justification for that though.…”

Section: Related Literaturementioning

confidence: 99%

“…To lighten the consequences of data sparsity, many modifications for user-based CF have already been proposed recently [ 11 , 32 ]. A singular vector decomposition [ 33 ] was implemented to concentrate particular user matrices for dimensionality reduction, and similarity measurements [ 12 ] were applied for grouping users and objects on a similarity basis.…”

Section: Related Literaturementioning

confidence: 99%

“…For example, rating data for many applications is relatively sparse or rating is not available for users (user cold-start) or objects (item cold-start) which compromises the quality of the recommendation process [ 8 , 9 ]. Cold-start and sparsity are two prominent and hot issues in RSs, and numerous solutions have been presented [ 10 , 11 ]. However, they struggle to deal with it effectively, particularly in circumstances of sparse input, such as when a high number of users and items exist but just a few people have rated each item [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Deep transfer learning with multimodal embedding to tackle cold-start and sparsity issues in recommendation system

et al. 2022

View full text Add to dashboard Cite

Recommender systems (RSs) have become increasingly vital in the modern information era and connected economy. They play a key role in business operations by generating personalized suggestions and minimizing information overload. However, the performance of traditional RSs is limited by data sparseness and cold-start issues. Though deep learning-based recommender systems (DLRSs) are very popular, they underperform when considering rating matrices with sparse entries. Despite their performance improvements, DLRSs also suffer from data sparsity, cold start, serendipity, and generalizability issues. We propose a multistage model that uses multimodal data embedding and deep transfer learning for effective and personalized product recommendations, and is designed to overcome data sparsity and cold-start issues. The proposed model includes two phases. In the first—offline—phase, a deep learning technique is implemented to learn hidden features from a large image dataset (targeting new item cold start), and a multimodal data embedding is used to produce dense user feature and item feature vectors (targeting user cold start). This phase produces three different similarity matrices that are used as inputs for the second—online—phase to generate a list of top-n relevant items for a target user. We analyzed the accuracy and effectiveness of the proposed model against the existing baseline RSs using a Brazilian E-commerce dataset. The results show that our model scored 0.5882 for MAE and 0.4011 for RMSE which is lower than baseline RSs which indicates that the model achieved an improved accuracy and was able to minimize the typical cold start and data sparseness issues during the recommendation process.

show abstract

Section: Related Literaturementioning

confidence: 99%

Section: Related Literaturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Deep transfer learning with multimodal embedding to tackle cold-start and sparsity issues in recommendation system

et al. 2022

View full text Add to dashboard Cite

show abstract

“…In this section, we will discuss some recommender systems using the RNN 21−24 and the GAN, 17,19,25 respectively.…”

Section: Related Workmentioning

confidence: 99%

“…IRGAN 17 is a unified framework, which takes advantage of both the generative model and discriminative model and can be used in the web search, item recommendation and question answering. In Reference [25], RecGAN is proposed, which combines RNN and GAN to improve recommendation performance. Different from the proposed TagRec in this paper, RecGAN leverages RNN to extract time feature from the interactive information between the users and items, while TagRec is to process the social information.…”

Section: Related Workmentioning

confidence: 99%