A Survey on Session-based Recommender Systems <s> An Overview of Challenges in SBRS. <s> The importance of contextual information has been recognized by researchers and practitioners in many disciplines, including e-commerce personalization, information retrieval, ubiquitous and mobile computing, data mining, marketing, and management. While a substantial amount of research has already been performed in the area of recommender systems, most existing approaches focus on recommending the most relevant items to users without taking into account any additional contextual information, such as time, location, or the company of other people (e.g., for watching movies or dining out). In this chapter we argue that relevant contextual information does matter in recommender systems and that it is important to take this information into account when providing recommendations. We discuss the general notion of context and how it can be modeled in recommender systems. Furthermore, we introduce three different algorithmic paradigms – contextual prefiltering, post-filtering, and modeling – for incorporating contextual information into the recommendation process, discuss the possibilities of combining several contextaware recommendation techniques into a single unifying approach, and provide a case study of one such combined approach. Finally, we present additional capabilities for context-aware recommenders and discuss important and promising directions for future research. <s> BIB001 </s> A Survey on Session-based Recommender Systems <s> An Overview 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> BIB002 </s> A Survey on Session-based Recommender Systems <s> An Overview of Challenges in SBRS. <s> Users express their preferences for items in diverse forms, through their liking for items, as well as through the sequence in which they consume items. The latter, referred to as “sequential preference”, manifests itself in scenarios such as song or video playlists, topics one reads or writes about in social media, etc. The current approach to modeling sequential preferences relies primarily on the sequence information, i.e., which item follows another item. However, there are other important factors, due to either the user or the context, which may dynamically affect the way a sequence unfolds. In this work, we develop generative modeling of sequences, incorporating dynamic user-biased emission and context-biased transition for sequential preference. Experiments on publicly-available real-life datasets as well as synthetic data show significant improvements in accuracy at predicting the next item in a sequence. <s> BIB003
Driven by the own characteristics of session data, the challenges in SBRS mainly come from three overall aspects: the inner-session aspect (i.e., internal aspect of a session), the inter-session aspect, and the outer-session aspect (i.e, external aspect of a session). In addition, these aspects interact with each other, as shown in Fig. 3 . We illustrate these three aspects of challenges one by one below. (a) Inner-session challenges. Inner-session challenges refer to the challenges inside a session. As shown in Section 3.2 and Fig. 2 , there is a quite complex structure within a session. To be specific, a hierarchical structure consists of multiple levels, from items contained in this session to the corresponding features of the items, and to the corresponding values of these features. Accordingly, inner-session challenges include all the challenges on the item level, the feature level, the feature value level and the interactions between these levels, which are listed on the right side of Fig. 2 . The specific challenges on each level will be demonstrated in detail in Section 3.3.2. (b) Inter-session challenges. Inter-session challenges mean the challenges associated with the interactions between sessions, containing the challenges on the session level listed on the right side of Fig. 2 . Some typical challenges include the session heterogeneity, the sequential dependency between sessions, and the dynamics of sessions. The detailed illustrations of these challenges are provided in Section 3.3.2. (c) Outer-session challenges. Outer-session challenges mainly contain three classes: the challenges on the domain level, the challenges of modelling the context associated with a session, and the interactions between the outer-session challenges and the inter-session challenges, as illustrated in Fig. 3 . Specific challenges in each class are detailed in Fig. 2 and Section 3.3.2. The session-associated context refers to the circumstance where a session happens. It mainly contains the contextual factors such as time, venue, weather, season and users. Context should be considered in an SBRS because different contexts can bring uncertainty and dynamics to the session evolution BIB003 and thus make a huge difference on a user's real-time preferences BIB001 . The corresponding challenges lie in the heterogeneity of different contextual factors and the complex coupling relationships between them BIB002 . For instance, it is quite challenging to appropriately model the collaborative effect between venue and season since much uncertainty exists here.