A Bipartite Graph-Based Recommender for Crowdfunding with Sparse Data

Computational Intelligence and Neuroscience

et al. 2022

It is a critical task to provide recommendation on implicit feedback, and one of the biggest challenges is extreme data sparsity. To tackle the problem, a graph kernel-based link prediction method is proposed in this paper for recommending crowdfunding projects combining graph computing with collaborative filtering. First of all, an investor-project bipartite graph is established based on transaction histories. Then, a random walk graph kernel is constructed and computed, and a one-class SVM classifier is built for link prediction based on implicit feedback. At last, top N recommendations are made according to the ranking of investor-project pairs. Comparative experiments are conducted and the results show that the proposed method achieves the best performance on extremely sparse implicit feedback and outperforms baselines. This paper is of help to improve the success rate of crowdfunding by personalized recommendation and is of significance to enrich the research in recommendation systems.

Section: Literature Reviewmentioning

confidence: 99%

Recommending Crowdfunding Project: A Graph Kernel-Based Link Prediction Method for Extremely Sparse Implicit Feedback

Yin

Chen

Computational Intelligence and Neuroscience

et al. 2022

“…Interactive Visualization. Although machine learning based methods [5,15,21,22] can efficiently predict potential investors for a specific project, they cannot explain why those investors are predicted. Contrarily, we provide founders with interactive visualizations that allow them to analyze potential investors intuitively based on the levels of detail they select.…”

Section: System Overviewmentioning

confidence: 99%

Who Will Support My Project? Interactive Search of Potential Crowdfunding Investors Through inSearch

Zhang

CHI Conference on Human Factors in Computing Systems Extended Abstracts

et al. 2022

Crowdfunding provides project founders with a convenient way to reach online investors. However, it is challenging for founders to find the most potential investors and successfully raise money for their projects on crowdfunding platforms. A few machine learning based methods have been proposed to recommend investors' interest in a specific crowdfunding project, but they fail to provide project founders with explanations in detail for these recommendations, thereby leading to an erosion of trust in predicted investors. To help crowdfunding founders find truly interested investors, we conducted semi-structured interviews with four crowdfunding experts and presents inSearch, a visual analytic system. inSearch allows founders to search for investors interactively on crowdfunding platforms. It supports an effective overview of potential investors by leveraging a Graph Neural Network to model investor preferences. Besides, it enables interactive exploration and comparison of the temporal evolution of different investors' investment details. CCS CONCEPTS• Human-centered computing → Visual analytics.

“…For example, Benin [8] compared the application of various machine learning algorithms in crowdfunding platforms, such as gradient boosting tree, Bayesian belief nets collaborative filtering, latent semantic collaborative filtering etc. Wang and Chen [9] proposed a bipartite graph-based collaborative filtering model by combining collaborative filtering and personal rank.…”

Section: Recommendation In Crowdfunding Platformsmentioning

confidence: 99%

Deep Collaborative Filtering: A Recommendation Method for Crowdfunding Project Based on the Integration of Deep Neural Network and Collaborative Filtering

Yin

Mathematical Problems in Engineering

Zhao

et al. 2022

In real recommendation systems, implicit feedback data is more common and easier to obtain, and recommendation algorithms based on such data will be more applicable. However, implicit feedback data cannot directly express user preferences. Meanwhile, data sparsity caused by massive data is still an urgent problem to be solved in recommendation system. In response to this phenomenon, this paper proposes a deep collaborative filtering algorithm. In the perspective of implicit feedback, this method uses the advantages of convolutional neural network for effective learning of the nonlinear interaction of users and items and the characteristics of collaborative filtering algorithm for modeling the linear interaction of users and items and combines the two methods for recommendation. Finally, the baseline method is set up and the comparative experiment and parameter adjustment is carried out. The experimental results show that the proposed algorithm has significantly improved the recommendation accuracy on public dataset (Yahoo! Movie). The parameter adjustment results show that, under the condition of uniformly collecting negative feedback data and setting a certain number of convolution layers, the sparser the data is, the better the recommendation performs. As a result, this paper has made some progress in solving the problem of data sparsity and enriching the research of recommendation system.