Tutorial: Sequence-Aware Recommender Systems
ACM Computing Surveys(2018)
摘要
The majority of research works in the field of collaborative filtering recommender systems is based on the assumption that the input to the recommendation algorithms is a matrix containing user-item interactions. In reality, however, the input often is a sequence of various types of user-item interactions that are recorded over time and where we can have multiple data points per user-item pair. These sequential logs contain a variety of useful information that can be leveraged in the recommendation process, e.g., to predict the immediate next action of a user or to detect short-term trends in the community.In this tutorial we review what we call sequence-aware recommenders, i.e., approaches that aim to exploit the information in sequential interaction logs for a variety of different purposes. We in particular focus on sequential and session-based recommendation techniques and discuss algorithmic proposals as well as evaluation challenges.
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关键词
Recommender Systems,Sequence-Awareness,Session-based Recommendation
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