arXiv subjects

Jinyun Fang

Publications and source records attributed to Jinyun Fang.

3 recordsLinked to original sources

arXiv2024

Enhancing Recommendation with Denoising Auxiliary Task

The historical interaction sequences of users plays a crucial role in training recommender systems that can accurately predict user preferences. However, due to the arbitrariness of user behavior, the presence of noise in these sequences poses a challenge to predicting their next actions in recommender systems. To address this issue, our motivation is based on the observation that training noisy sequences and clean sequences (sequences without noise) with equal weights can impact the performance of the model. We propose a novel self-supervised Auxiliary Task Joint Training (ATJT) method aimed at more accurately reweighting noisy sequences in recommender systems. Specifically, we strategically select subsets from users' original sequences and perform random replacements to generate artificially replaced noisy sequences. Subsequently, we perform joint training on these artificially replaced noisy sequences and the original sequences. Through effective reweighting, we incorporate the training results of the noise recognition model into the recommender model. We evaluate our method on three datasets using a consistent base model. Experimental results demonstrate the effectiveness of introducing self-supervised auxiliary task to enhance the base model's performance.

cs.IR↗

arXiv2023

VM-Rec: A Variational Mapping Approach for Cold-start User Recommendation

The cold-start problem is a common challenge for most recommender systems. The practical application of most cold-start methods is hindered by the deficiency in auxiliary content information for users. Moreover, most methods necessitate simultaneous updates to the extensive parameters of recommender models, leading to significant training costs, particularly in large-scale industrial scenarios. We observe that the model can generate expressive embeddings for warm users with relatively more interactions. Initially, these users were cold-start users, and after transitioning to warm users, they exhibit clustering patterns in their embeddings with consistent initial interactions. Based on this motivation, we propose a Variational Mapping approach for cold-start user Recommendation (VM-Rec), mapping from few initial interactions to expressive embeddings for cold-start users. Specifically, we encode the initial interactions into a latent representation, where each dimension disentangledly signifies the degree of association with each warm user. Subsequently, we utilize this latent representation as the parameters for the mapping function, mapping (decoding) it into an expressive embedding, which can be integrated into a pre-trained recommender model directly. Our method is evaluated on three datasets using the same base model, demonstrating superior performance compared to other popular cold-start methods.

cs.IR↗

arXiv2011

Anomaly Sequences Detection from Logs Based on Compression

Mining information from logs is an old and still active research topic. In recent years, with the rapid emerging of cloud computing, log mining becomes increasingly important to industry. This paper focus on one major mission of log mining: anomaly detection, and proposes a novel method for mining abnormal sequences from large logs. Different from previous anomaly detection systems which based on statistics, probabilities and Markov assumption, our approach measures the strangeness of a sequence using compression. It first trains a grammar about normal behaviors using grammar-based compression, then measures the information quantities and densities of questionable sequences according to incrementation of grammar length. We have applied our approach on mining some real bugs from fine grained execution logs. We have also tested its ability on intrusion detection using some publicity available system call traces. The experiments show that our method successfully selects the strange sequences which related to bugs or attacking.

cs.LG↗