Trust-Based Collaborative Filtering: Tackling the Cold Start Problem Using Regular Equivalence

2018-07-18 · Tomislav Duricic, Emanuel Lacic, Dominik Kowald, Elisabeth Lex

User-based Collaborative Filtering (CF) is one of the most popular approaches to create recommender systems. This approach is based on finding the most relevant k users from whose rating history we can extract items to recommend. CF, however, suffers from data sparsity and the cold-start problem since users often rate only a small fraction of available items. One solution is to incorporate additional information into the recommendation process such as explicit trust scores that are assigned by users to others or implicit trust relationships that result from social connections between users. Such relationships typically form a very sparse trust network, which can be utilized to generate recommendations for users based on people they trust. In our work, we explore the use of a measure from network science, i.e. regular equivalence, applied to a trust network to generate a similarity matrix that is used to select the k-nearest neighbors for recommending items. We evaluate our approach on Epinions and we find that we can outperform related methods for tackling cold-start users in terms of recommendation accuracy.

📄 PDF Abstract BibTeX arXiv:1807.06839

Code (0)

등록된 구현이 없습니다.

Tasks

Collaborative Filtering Recommendation Systems

Empirical Comparison of Graph Embeddings for Trust-Based Collaborative Filtering

2020-03-30 · Tomislav Duricic, Hussain Hussain, Emanuel Lacic, Dominik Kowald 외

In this work, we study the utility of graph embeddings to generate latent user representations for trust-based collaborative filtering. In a cold-start setting, on three publicly available datasets, we evaluate approache…

Collaborative Filtering Network Embedding

Contrastive Collaborative Filtering for Cold-Start Item Recommendation

2023-02-04 · Zhihui Zhou, Lilin Zhang, Ning Yang

The cold-start problem is a long-standing challenge in recommender systems. As a promising solution, content-based generative models usually project a cold-start item's content onto a warm-start item embedding to capture…

Collaborative Filtering Contrastive Learning Recommendation Systems

A Hybrid Latent Variable Neural Network Model for Item Recommendation

2014-06-09 · Michael R. Smith, Tony Martinez, Michael Gashler

Collaborative filtering is used to recommend items to a user without requiring a knowledge of the item itself and tends to outperform other techniques. However, collaborative filtering suffers from the cold-start problem…

Collaborative Filtering

Wasserstein Collaborative Filtering for Item Cold-start Recommendation

2019-09-10 · Yitong Meng, Guangyong Chen, Benben Liao, Jun Guo 외

The item cold-start problem seriously limits the recommendation performance of Collaborative Filtering (CF) methods when new items have either none or very little interactions. To solve this issue, many modern Internet a…