A Contextual Recurrent Collaborative Filtering framework for modelling sequences of venue checkins

作者：

Highlights：

• CRCF integrates the state-of-the-art Contextual Recurrent Architecture (CARA) to effectively capture the users’ short-term preferences from their sequence of checkins by incorporating the contextual information associated with their successive checkins.

• We show that high quality venue recommendations for both normal and cold-start users can be generated by the proposed CRCF model.

• We show that sequential geo-based negative sampling approach can improve the effectiveness and robustness of a state-of-the-art neural-network based venue recommendation approach.

摘要

•We propose the CRCF - the Contextual Recurrent Collaborative Filtering framework - which leverages users’ preferred context and the contextual information associated with the users’ sequence of checkins to model the users’ short-term preferences using a GRU-based RNN component.•CRCF integrates the state-of-the-art Contextual Recurrent Architecture (CARA) to effectively capture the users’ short-term preferences from their sequence of checkins by incorporating the contextual information associated with their successive checkins.•We show that high quality venue recommendations for both normal and cold-start users can be generated by the proposed CRCF model.•We show that sequential geo-based negative sampling approach can improve the effectiveness and robustness of a state-of-the-art neural-network based venue recommendation approach.

论文关键词：Recommendation System,Collaborative Filtering,Deep Neural Networks,Location-Based Social Networks

论文评审过程：Received 28 February 2019, Revised 6 July 2019, Accepted 27 July 2019, Available online 4 September 2019, Version of Record 20 October 2020.

论文官网地址：https://doi.org/10.1016/j.ipm.2019.102092