A Survey on Session-based Recommender Systems <s> Session-based Recommendations Considering More Contextual Factors <s> This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multicriteria ratings, and a provision of more flexible and less intrusive types of recommendations. <s> BIB001 </s> A Survey on Session-based Recommender Systems <s> Session-based Recommendations Considering More Contextual Factors <s> Context-aware recommender systems (CARS) generate more relevant recommendations by adapting them to the specific contextual situation of the user. This article explores how contextual information can be used to create more intelligent and useful recommender systems. It provides an overview of the multifaceted notion of context, discusses several approaches for incorporating contextual information in recommendation process, and illustrates the usage of such approaches in several application areas where different types of contexts are exploited. The article concludes by discussing the challenges and future research directions for context-aware recommender systems. <s> BIB002 </s> A Survey on Session-based Recommender Systems <s> Session-based Recommendations Considering More Contextual Factors <s> Over the past two decades, a large amount of research effort has been devoted to developing algorithms that generate recommendations. The resulting research progress has established the importance of the user-item (U-I) matrix, which encodes the individual preferences of users for items in a collection, for recommender systems. The U-I matrix provides the basis for collaborative filtering (CF) techniques, the dominant framework for recommender systems. Currently, new recommendation scenarios are emerging that offer promising new information that goes beyond the U-I matrix. This information can be divided into two categories related to its source: rich side information concerning users and items, and interaction information associated with the interplay of users and items. In this survey, we summarize and analyze recommendation scenarios involving information sources and the CF algorithms that have been recently developed to address them. We provide a comprehensive introduction to a large body of research, more than 200 key references, with the aim of supporting the further development of recommender systems exploiting information beyond the U-I matrix. On the basis of this material, we identify and discuss what we see as the central challenges lying ahead for recommender system technology, both in terms of extensions of existing techniques as well as of the integration of techniques and technologies drawn from other research areas. <s> BIB003 </s> A Survey on Session-based Recommender Systems <s> Session-based Recommendations Considering More Contextual Factors <s> The emergence of smart mobile devices has given rise to the development of context-aware systems that utilize sensors to collect available data about users. This data, in turn, is used in order to improve various services for the user. The development of such applications is inherently complex, since these applications adapt to changing context information, such as: physical context, computational context, and user tasks. Context information is gathered from a variety of sources that differ in the quality of information they produce and that are often failure-prone. Our study is part of a growing research effort that examines how data collected from mobile devices can be utilized to infer users' behavior and environment. We propose novel approaches that use a rich set of mobile sensors in order to infer unexplored users' contexts in personal models. We also suggest utilizing these high dimensional sensors, which represent users' context for a CARS (context-aware recommender system). For this purpose, we suggest several methods for reducing the dimensionality space by extracting latent contexts from data collected by mobile device sensors. Latent contexts are hidden context patterns, modeled as numeric vectors that are learned for each user automatically, by utilizing unsupervised deep learning techniques on the collected data. We also describe a novel latent context recommendation technique that uses latent contexts and improves the accuracy of state-of-the-art CARS. A preliminary analysis reveals encouraging insights regarding the feasibility of latent contexts and their utilization for context-aware recommendation systems. <s> BIB004 </s> A Survey on Session-based Recommender Systems <s> Session-based Recommendations Considering More Contextual Factors <s> A critical change has occurred in the status of context in recommender systems. In the past, context has been considered 'additional evidence'. This past picture is at odds with many present application domains, where user and item information is scarce. Such domains face continuous cold start conditions and must exploit session rather than user information. In this paper, we describe the `Contextual Turn?: the move towards context-driven recommendation algorithms for which context is critical, rather than additional. We cover application domains, algorithms that promise to address the challenges of context-driven recommendation, and the steps that the community has taken to tackle context-driven problems. Our goal is to point out the commonalities of context-driven problems, and urge the community to address the overarching challenges that context-driven recommendation poses. <s> BIB005 </s> A Survey on Session-based Recommender Systems <s> Session-based Recommendations Considering More Contextual Factors <s> Preparing recommendations for unknown users or such that correctly respond to the short-term needs of a particular user is one of the fundamental problems for e-commerce. Most of the common Recommender Systems assume that user identification must be explicit. In this paper a Session-Aware Recommender System approach is presented where no straightforward user information is required. The recommendation process is based only on user activity within a single session, defined as a sequence of events. This information is incorporated in the recommendation process by explicit context modeling with factorization methods and a novel approach with Recurrent Neural Network (RNN). Compared to the session modeling approach, RNN directly models the dependency of user observed sequential behavior throughout its recurrent structure. The evaluation discusses the results based on sessions from real-life system with ephemeral items (identified only by the set of their attributes) for the task of top-n best recommendations. <s> BIB006 </s> A Survey on Session-based Recommender Systems <s> Session-based Recommendations Considering More Contextual Factors <s> Most research in recommender systems is focused on the problem of identifying and ranking items that are relevant for the individual users but unknown to them. The potential value of such systems is to help users discover new items, e.g., in e-commerce settings. Many real-world systems however also utilize recommendation lists for a different goal, namely to remind users of items that they have viewed or consumed in the past. In this work, we aim to quantify the value of such reminders in recommendation lists ("recominders"), which has to our knowledge not been done in the past. We first report the results of a live experiment in which we applied a naive reminding strategy on an online platform and compare them with results obtained through different offline analyses. We then propose more elaborate reminding techniques, which aim to avoid reminders of too obvious or of already outdated items. Overall, our results show that although reminders do not lead to new item discoveries, they can be valuable both for users and service providers. <s> BIB007 </s> A Survey on Session-based Recommender Systems <s> Session-based Recommendations Considering More Contextual Factors <s> Recommendations can greatly benefit from good representations of the user state at recommendation time. Recent approaches that leverage Recurrent Neural Networks (RNNs) for session-based recommendations have shown that Deep Learning models can provide useful user representations for recommendation. However, current RNN modeling approaches summarize the user state by only taking into account the sequence of items that the user has interacted with in the past, without taking into account other essential types of context information such as the associated types of user-item interactions, the time gaps between events and the time of day for each interaction. To address this, we propose a new class of Contextual Recurrent Neural Networks for Recommendation (CRNNs) that can take into account the contextual information both in the input and output layers and modifying the behavior of the RNN by combining the context embedding with the item embedding and more explicitly, in the model dynamics, by parametrizing the hidden unit transitions as a function of context information. We compare our CRNNs approach with RNNs and non-sequential baselines and show good improvements on the next event prediction task. <s> BIB008 </s> A Survey on Session-based Recommender Systems <s> Session-based Recommendations Considering More Contextual Factors <s> Academic research in recommender systems largely focuses on the problem of predicting the relevance of (long-tail) items that the individual user presumably does not know yet. Many real-world systems however also recommend items that users have inspected in the past, items that are popular at the moment, and items currently on sale. In this work we investigate the value of including such items in recommendation lists based on an analysis of the web logs of a large online retailer. An examination of the features of successful item suggestions reveals that the chances of a recommendation leading to a purchase increase when the item is recently trending, on sale, or was recently viewed by the user. Offline simulation experiments furthermore show that considering those success factors that were identified from log data in the ranking algorithms can help to increase the prediction accuracy of recommender systems. <s> BIB009
Siдni f icance. Recommendation context refers to the practical and specific situation when a user is making choices on items. Accordingly, contextual factors refer to different context-related aspects that may affect a user's choice, such as weather, season, location, time and recent popularity trend. Taking these contextual factors into account may make a huge difference on recommendation performance. The significance of context in RS is also emphasized by other researchers like Gediminas el al. BIB001 BIB002 , Shi el al. BIB003 and Pagano el al. BIB005 . Actually, an SBRS can be seen as a simplified context-aware RS whose context is simplified as a session context BIB006 . Open issues. Although contextual information has been incorporated into other RS or even context-aware RS have been proposed as a new paradigm BIB002 BIB004 , context is rarely exploited in SBRS. -How to incorporate more contextual factors into SBRS? A Contextual RNN for Recommendation (CRNN) is proposed to incorporate contextual factors including the type of user-item interactions, the time gap between different events in a session and the time of a day into an RNN-based SBRS BIB008 . In BIB009 BIB007 , the recent popularity trend, user's recently viewed items, and items with discount in shopping mall are taken as contextual factors for session-based recommendations. However, these methods are just a starting point, more explorations are still necessary on how to collect more contextual information and how to develop models to more effectively incorporate such information into SBRS.