A Survey on Session-based Recommender Systems <s> A Categorization of Challenges in SBRS. <s> Abstract Complex applications such as big data analytics involve different forms of coupling relationships that reflect interactions between factors related to technical, business (domain-specific) and environmental (including socio-cultural and economic) aspects. There are diverse forms of couplings embedded in poor-structured and ill-structured data. Such couplings are ubiquitous, implicit and/or explicit, objective and/or subjective, heterogeneous and/or homogeneous, presenting complexities to existing learning systems in statistics, mathematics and computer sciences, such as typical dependency, association and correlation relationships. Modeling and learning such couplings thus is fundamental but challenging. This paper discusses the concept of coupling learning, focusing on the involvement of coupling relationships in learning systems. Coupling learning has great potential for building a deep understanding of the essence of business problems and handling challenges that have not been addressed well by existing learning theories and tools. This argument is verified by several case studies on coupling learning, including handling coupling in recommender systems, incorporating couplings into coupled clustering, coupling document clustering, coupled recommender algorithms and coupled behavior analysis for groups. <s> BIB001 </s> A Survey on Session-based Recommender Systems <s> A Categorization of Challenges in SBRS. <s> ABSTRACT While recommendation plays an increasingly critical role in our living, study, work, and entertainment, the recommendations we receive are often for irrelevant, duplicate, or uninteresting products and services. A critical reason for such bad recommendations lies in the intrinsic assumption that recommended users and items are independent and identically distributed (IID) in existing theories and systems. Another phenomenon is that, while tremendous efforts have been made to model specific aspects of users or items, the overall user and item characteristics and their non-IIDness have been overlooked. In this paper, the non-IID nature and characteristics of recommendation are discussed, followed by the non-IID theoretical framework in order to build a deep and comprehensive understanding of the intrinsic nature of recommendation problems, from the perspective of both couplings and heterogeneity. This non-IID recommendation research triggers the paradigm shift from IID to non-IID recommendation research and can hopefully deliver informed, relevant, personalized, and actionable recommendations. It creates exciting new directions and fundamental solutions to address various complexities including cold-start, sparse data-based, cross-domain, group-based, and shilling attack-related issues. <s> BIB002
Generally, all the specific challenges on all levels in SBRS can be divided into the following four categories: heterogeneity, coupling, other complexities, and their interactions BIB001 BIB002  . (a) The heterogeneity within each level: refers to different elements on each level that has different characteristics and thus cannot be treated equally or be modelled in the same way. • Value heterogeneity: the feature values from one feature are often distributed differently and thus cannot be treated equally. For instance, some values may be more frequent than others. • Feature heterogeneity: an item usually contains different types of features, including categorical and numerical ones, these heterogeneous features cannot be modelled in the same way. • Item heterogeneity: items in a session also have different distributions (e.g., some are quite popular while most of others may rarely occur). Particularly, the contextual items in an SBRS are usually heterogeneous due to their different relevance scales to the next item(s).