Learning to Warm Up Cold Item Embeddings for Cold-start Recommendation with Meta Scaling and Shifting Networks

Yongchun Zhu

Abstract:

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.