Recommender systems provide relevant items and information to users by profiling users and items in various ways. Growth of online information systems has led to an abundance of data that is heterogeneous, noisy, and changes rapidly. The data used by recommender systems, in forms of implicit or explicit user feedback, follow the same trend: the feedback can be in various formats, such as ratings, online behaviors, or textual reviews, and collected from multiple resources, the collected feedback is uncertain, and user taste and item popularities can change over time. In this workshop, the focus is on recommender systems’ data heterogeneity: collected feedback with various types, collected from various domains, contexts, or applications.
While the data heterogeneity provides multiple views to users’ preferences, and thus, may be helpful in recommending more related items to users, it may also add more noise and uncertainty to the data and lead to weaker recommendations. Cross-domain recommender systems and transfer learning approaches propose to effectively take advantage of such diversity of viewpoints to provide better-quality recommendations and resolve issues such as the cold-start problem.
The emerging research on cross-domain, context-aware and multi-criteria recommender systems, has proved to be successful. Given the recent availability of cross-domain datasets and novelty of the topic, we organize the 2nd workshop on intelligent recommender systems by knowledge transfer and learning (RecSysKTL) held in conjunction with ACM RecSys 2018. This workshop intends to create a medium to generate more practical and efficient predictive models or recommendation approaches by leveraging user feedbacks or preferences from multiple domains.