Addressing the Item Cold-start Problem by Attribute-driven Active Learning

Zhu, Yu; Lin, Jinhao; He, Shibi; Wang, Beidou; Guan, Ziyu; Liu, Haifeng

doi:10.48550/arxiv.1805.09023

Cited by 2 publications

(2 citation statements)

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“…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. These solutions, on the other hand, have the disadvantage of necessitating the updating of the decomposition each time a new user is added or a rating is introduced in a matrix.…”

Section: Related Literaturementioning

confidence: 99%

“…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]. Generating a profile of a user or an item is relatively a much complicated task.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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.

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Section: Related Literaturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%