首页|Enhancing Recommendation with Denoising Auxiliary Task
Enhancing Recommendation with Denoising Auxiliary Task
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万方数据
维普
The historical interaction sequences of users play a crucial role in training recommender systems that can ac-curately predict user preferences.However,due to the arbitrariness of user behaviors,the presence of noise in these se-quences 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 the novel self-supervised Auxiliary Task Joint Training(ATJT)method aimed at more accurately reweighting noisy sequences in recommender systems.Specifically,we strategically se-lect subsets from users'original sequences and perform random replacements to generate artificially replaced noisy se-quences.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 ef-fectiveness of introducing the self-supervised auxiliary task to enhance the base model's performance.
万方数据
维普
