Semi-supervised Collaborative Filtering by Text-enhanced Domain Adaptation

• Main Authors: Yu, Wenhui, Lin, Xiao, Ge, Junfeng, Ou, Wenwu, Qin, Zheng
• Format: Journal Article
• Language: English
• Published: 28-06-2020
• Subjects: Computer Science - Information Retrieval; Computer Science - Learning; Statistics - Machine Learning
• DOI: 10.48550/arxiv.2007.07085

Data sparsity is an inherent challenge in the recommender systems, where most
of the data is collected from the implicit feedbacks of users. This causes two
difficulties in designing effective algorithms: first, the majority of users
only have a few interactions with the system and there is no enough data for
learning; second, there are no negative samples in the implicit feedbacks and
it is a common practice to perform negative sampling to generate negative
samples. However, this leads to a consequence that many potential positive
samples are mislabeled as negative ones and data sparsity would exacerbate the
mislabeling problem. To solve these difficulties, we regard the problem of
recommendation on sparse implicit feedbacks as a semi-supervised learning task,
and explore domain adaption to solve it. We transfer the knowledge learned from
dense data to sparse data and we focus on the most challenging case -- there is
no user or item overlap. In this extreme case, aligning embeddings of two
datasets directly is rather sub-optimal since the two latent spaces encode very
different information. As such, we adopt domain-invariant textual features as
the anchor points to align the latent spaces. To align the embeddings, we
extract the textual features for each user and item and feed them into a domain
classifier with the embeddings of users and items. The embeddings are trained
to puzzle the classifier and textual features are fixed as anchor points. By
domain adaptation, the distribution pattern in the source domain is transferred
to the target domain. As the target part can be supervised by domain
adaptation, we abandon negative sampling in target dataset to avoid label
noise. We adopt three pairs of real-world datasets to validate the
effectiveness of our transfer strategy. Results show that our models outperform
existing models significantly.