期刊
出版社
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3404835.3462843
关键词
Cold-start Recommendation; Item ID Embedding; Warm Up; Meta Network
资金
- National Key Research and Development Program of China [2018YFB1004300]
- National Natural Science Foundation of China [61773361, U1836206, U1811461]
In this study, a meta warm-up framework is proposed to tackle the cold-start problem in recommender systems. By utilizing meta scaling and shifting networks, cold item ID embeddings are transformed for better model fitting and stability against noise. The evaluation results demonstrate superior performance and compatibility of the proposed model on popular benchmarks.
Recently, embedding techniques have achieved impressive success in recommender systems. However, the embedding techniques are data demanding and suffer from the cold-start problem. Especially, for the cold-start item which only has limited interactions, it is hard to train a reasonable item ID embedding, called cold ID embedding, which is a major challenge for the embedding techniques. The cold item ID embedding has two main problems: (1) A gap is existing between the cold ID embedding and the deep model. (2) Cold ID embedding would be seriously affected by noisy interaction. However, most existing methods do not consider both two issues in the cold-start problem, simultaneously. To address these problems, we adopt two key ideas: (1) Speed up the model fitting for the cold item ID embedding (fast adaptation). (2) Alleviate the influence of noise. Along this line, we propose Meta Scaling and Shifting Networks to generate scaling and shifting functions for each item, respectively. The scaling function can directly transform cold item ID embeddings into warm feature space which can fit the model better, and the shifting function is able to produce stable embeddings from the noisy embeddings. With the two meta networks, we propose Meta Warm Up Framework (MWUF) which learns to warm up cold ID embeddings. Moreover, MWUF is a general framework that can be applied upon various existing deep recommendation models. The proposed model is evaluated on three popular benchmarks, including both recommendation and advertising datasets. The evaluation results demonstrate its superior performance and compatibility.
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