A fusion collaborative filtering method for sparse data in recommender systems

Feng, Chong; Liang, Jiye; Song, Peng; Wang, Zhiqiang

doi:10.1016/j.ins.2020.02.052

Cited by 85 publications

(39 citation statements)

References 20 publications

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“…Though there exist various methods for addressing the sparsity problem in collaborative filtering, the complication increases with the increase in the size of the sparsity data. This problem is addressed in [16] where the linear relations and the nonlinear relations between the users are extracted, with the help of which multi factor similarity measure is calculated.…”

Section: Related Workmentioning

confidence: 99%

Profile and Rating Similarity Analysis for Recommendation Systems Using Deep Learning

Palaniappan¹,

Selvaraj²

2022

Computer Systems Science and Engineering

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Section: Related Workmentioning

confidence: 99%

Profile and Rating Similarity Analysis for Recommendation Systems Using Deep Learning

Palaniappan¹,

Selvaraj²

2022

Computer Systems Science and Engineering

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“…However, most methods measure similarity based on the correlated ratings between users. Although users' behavior have been considered differently [62], [63], there is no relevant published research regarding user behavior probability as a primary factor of similarity.…”

Section: Related Workmentioning

confidence: 99%

Rating Prediction in Recommender Systems Based on User Behavior Probability and Complex Network Modeling

Lin

et al. 2021

IEEE Access

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In recommender systems, measuring user similarity is essential for predicting a user's ratings on items. Most traditional works calculate the similarity based on historical ratings shared between two users, without considering the probability of users' different behaviors. To address this issue, our work designs a similarity measure based on the users' historical behavior probabilities. Based on the similarity method, a complex network of user relationships is modeled. The user degree and community information of the modeled network, as well as the number of shared ratings between users, are used with the proposed similarity measure to design a rating prediction algorithm for recommender systems. Experiments based on MovieLens and Netflix data sets show that this method is effective and can successfully improve the accuracy of rating predictions and reduce the number of neighbors required to achieve the optimal prediction accuracy. Our research shows that in a complex system, the relationship between users can be effectively measured by the users' historical behavior probability, providing a new perspective for future research on similarity measurement.INDEX TERMS complex network modeling, recommender systems, degree centrality, link prediction, user behavior probability.

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“…We design a set of experiments to measure the system performance in the presence of varying levels of sparsity. The most efficient way to create a sparse environment is removing records, as described in Feng et al [56] who randomly removed ratings in the test data. In our experiment, we adopt the number of reviews per items to indicate the level of sparsity.…”

Section: Performance Wrt Sparsity (Q2)mentioning

confidence: 99%

Improving Explainable Recommendations by Deep Review-Based Explanations

Ouyang

Lawlor

2021

IEEE Access

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Many e-commerce sites encourage their users to write product reviews, in the knowledge that they exert a considerable influence on users' decision-making processes. These snippets of real-world experience provide an essential source of data for interpretable recommendations. However, current methods relying on user-generated content to make recommendations can run into problems because of well-known issues with reviews, such as noise, sparsity and irrelevant content. On the other hand, recent advances in text generation methods demonstrate significant text quality improvements and show promise in their ability to address these problems. In this paper, we develop two character-level deep neural network-based personalised review generation models, and improve recommendation accuracy by generating high-quality text which meets the input criteria of text-aware recommender systems.To make fair comparisons, we train review-aware recommender systems by human written reviews and attain advanced recommendations by feeding generated reviews at the inference step.Our experiments are conducted on four large review datasets from multiple domains. We leverage our methods' performance by comparing with non-review based recommender systems and advanced review-aware recommender systems. The results demonstrate that we beat baselines on a range of metrics and obtain state-of-the-art performance on both rating prediction and top-N ranking. Our sparsity experiments validate that our generation models can produce high-quality text to tackle the sparsity problem. We also demonstrate the generation of useful reviews so that we can achieve up to 13.53% RMSE improvements. For explanation evaluation, quantitative analyses reveal good understandable scores for our generated review-based explanations, and qualitative case studies substantiate we can capture critical aspects in generating explanations.

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A fusion collaborative filtering method for sparse data in recommender systems

Cited by 85 publications

References 20 publications

Profile and Rating Similarity Analysis for Recommendation Systems Using Deep Learning

Profile and Rating Similarity Analysis for Recommendation Systems Using Deep Learning

Rating Prediction in Recommender Systems Based on User Behavior Probability and Complex Network Modeling

Improving Explainable Recommendations by Deep Review-Based Explanations

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