A Survey on Session-based Recommender Systems <s> INTRODUCTION <s> In many e-commerce applications, ranging from dynamic Web content presentation, to personalized ad targeting, to individual recommendations to the customers, it is important to build personalized profiles of individual users from their transactional histories. These profiles constitute models of individual user behavior and can be specified with sets of rules learned from user transactional histories using various data mining techniques. Since many discovered rules can be spurious, irrelevant, or trivial, one of the main problems is how to perform post-analysis of the discovered rules, i.e., how to validate user profiles by separating “good” rules from the “bad.” This validation process should be done with an explicit participation of the human expert. However, complications may arise because there can be very large numbers of rules discovered in the applications that deal with many users, and the expert cannot perform the validation on a rule-by-rule basis in a reasonable period of time. This paper presents a framework for building behavioral profiles of individual users. It also introduces a new approach to expert-driven validation of a very large number of rules pertaining to these users. In particular, it presents several types of validation operators, including rule grouping, filtering, browsing, and redundant rule elimination operators, that allow a human expert validate many individual rules at a time. By iteratively applying such operators, the human expert can validate a significant part of all the initially discovered rules in an acceptable time period. These validation operators were implemented as a part of a one-to-one profiling system. The paper also presents a case study of using this system for validating individual user rules discovered in a marketing application. <s> BIB001 </s> A Survey on Session-based Recommender Systems <s> INTRODUCTION <s> This chapter discusses content-based recommendation systems, i.e., systems that recommend an item to a user based upon a description of the item and a profile of the user's interests. Content-based recommendation systems may be used in a variety of domains ranging from recommending web pages, news articles, restaurants, television programs, and items for sale. Although the details of various systems differ, content-based recommendation systems share in common a means for describing the items that may be recommended, a means for creating a profile of the user that describes the types of items the user likes, and a means of comparing items to the user profile to determine what to recommend. The profile is often created and updated automatically in response to feedback on the desirability of items that have been presented to the user. <s> BIB002 </s> A Survey on Session-based Recommender Systems <s> INTRODUCTION <s> One of the potent personalization technologies powering the adaptive web is collaborative filtering. Collaborative filtering (CF) is the process of filtering or evaluating items through the opinions of other people. CF technology brings together the opinions of large interconnected communities on the web, supporting filtering of substantial quantities of data. In this chapter we introduce the core concepts of collaborative filtering, its primary uses for users of the adaptive web, the theory and practice of CF algorithms, and design decisions regarding rating systems and acquisition of ratings. We also discuss how to evaluate CF systems, and the evolution of rich interaction interfaces. We close the chapter with discussions of the challenges of privacy particular to a CF recommendation service and important open research questions in the field. <s> BIB003 </s> A Survey on Session-based Recommender Systems <s> INTRODUCTION <s> Recommender systems are an important part of the information and e-commerce ecosystem. They represent a powerful method for enabling users to filter through large information and product spaces. Nearly two decades of research on collaborative filtering have led to a varied set of algorithms and a rich collection of tools for evaluating their performance. Research in the field is moving in the direction of a richer understanding of how recommender technology may be embedded in specific domains. The differing personalities exhibited by different recommender algorithms show that recommendation is not a one-size-fits-all problem. Specific tasks, information needs, and item domains represent unique problems for recommenders, and design and evaluation of recommenders needs to be done based on the user tasks to be supported. Effective deployments must begin with careful analysis of prospective users and their goals. Based on this analysis, system designers have a host of options for the choice of algorithm and for its embedding in the surrounding user experience. This paper discusses a~wide variety of the choices available and their implications, aiming to provide both practicioners and researchers with an introduction to the important issues underlying recommenders and current best practices for addressing these issues. <s> BIB004 </s> A Survey on Session-based Recommender Systems <s> INTRODUCTION <s> The collaborative systems discussed in the previous chapters use the correlations in the ratings patterns across users to make recommendations. On the other hand, these methods do not use item attributes for computing predictions. This would seem rather wasteful; after all, if John likes the futuristic science fiction movie Terminator, then there is a very good chance that he might like a movie from a similar genre, such as Aliens. In such cases, the ratings of other users may not be required to make meaningful recommendations. <s> BIB005 </s> A Survey on Session-based Recommender Systems <s> INTRODUCTION <s> This paper contributes improvements on both the effectiveness and efficiency of Matrix Factorization (MF) methods for implicit feedback. We highlight two critical issues of existing works. First, due to the large space of unobserved feedback, most existing works resort to assign a uniform weight to the missing data to reduce computational complexity. However, such a uniform assumption is invalid in real-world settings. Second, most methods are also designed in an offline setting and fail to keep up with the dynamic nature of online data. We address the above two issues in learning MF models from implicit feedback. We first propose to weight the missing data based on item popularity, which is more effective and flexible than the uniform-weight assumption. However, such a non-uniform weighting poses efficiency challenge in learning the model. To address this, we specifically design a new learning algorithm based on the element-wise Alternating Least Squares (eALS) technique, for efficiently optimizing a MF model with variably-weighted missing data. We exploit this efficiency to then seamlessly devise an incremental update strategy that instantly refreshes a MF model given new feedback. Through comprehensive experiments on two public datasets in both offline and online protocols, we show that our implemented, open-source (https://github.com/hexiangnan/sigir16-eals) eALS consistently outperforms state-of-the-art implicit MF methods. <s> BIB006
Recommender systems (RS) have evolved into a fundamental tool for helping users make informed decisions and choices, especially in the era of big data in which customers have to make choices from a large number of products and services. A lot of RS models and techniques have been proposed and most of them have achieved great success. Among them, the content-based RS BIB005 BIB002 and collaborative filtering RS BIB004 BIB003 are two representative ones. Their efficacy has been demonstrated by both research and industry communities. However, these aforementioned conventional RS still have some drawbacks. A critical one is that they only focus on a user's long-term static preference while ignoring his or her short-term transactional patterns, which results in missing the user's preference shift through the time. In this case, the user's intent at a certain time point may be easily submerged by his or her historical shopping behaviours, which leads to unreliable recommendations. This is because these RS usually break down a basic transaction unit (e.g., a session) into multiple records at smaller granularity (e.g., user-item interaction pairs) and then mix all these records. Such splitting actually has destroyed the intrinsic nature of a transactional behavior, in which a user's preference shift is embedded. For BIB001 BIB001 r' 2,4 r' BIB001 BIB005 Collaborative filtering RS: filling the missing ratings by usually using factorization approaches example, in the matrix factorization BIB006  , which is a representative model for collaborative filtering RS, the items a user bought in all transactions are put into one row of a matrix as shown in the top half of Figure 1 . Another typical drawback is that user IDs are not always available due to privacy issues in some cases, therefore the conventional RS which require user information are not applicable. In such cases, the recommendations can be only made on the transactional data without specific user information. To address the above issues, it is necessary to take the transactional structure into account to capture richer information in recommendation. In other words, it is necessary to learn user transactional behaviour patterns and the user preference shift from one transaction to another. To this end, SBRS were proposed recently. Here, a session can be regarded as a transaction with multiple purchased items in one shopping event. Different from content-based and collaborative filtering RS, SBRS comprehensively consider the information embedded from one session to another and take a session as the basic unit for recommendation, as shown in the bottom half of Figure 1 . In this way, SBRS aim to maximally reduce the information loss caused by ignoring or breaking down session structures as in some of existing approaches. Besides the transactional domain, SBRS are widely applied in other domains like next web page recommendations, next POI recommendations, tourism recommendations, next song recommendations, next movie recommendations, and so on. To cover these various domains, in this work, the concept "session" is not limited to a transaction, instead, it refers to a collection of consumed or visited elements at one time or in a certain period of time. For instance, the web pages visited by a user in one internet surf can be gathered into a session, and the songs listened by a user in an hour or in a day can also be grouped into a session. Except for the difference illustrated in the second and third paragraphs, SBRS also differ from other RS at multiple aspects. To have a substantial understanding of such differences, we present a comprehensive comparison between SBRS and other typical ones in Table 1 . In this survey, we give a comprehensive and systematic overview of the session-based recommendation scenarios, which take a session as the basic unit for recommendation, and the corresponding technique: SBRS. This is actually a relative novel recommendation paradigm investigated in recent years in the relevant communities. The survey provides a basic foundation and a comprehensive view of SBRS with a rich list of relevant resources for the community. The contributions of this work are multifold. • We systematically formalize the issues of SBRS and the corresponding work mechanisms, which provides a in-depth and comprehensive understanding of this new recommendation paradigm. In addition, we thoroughly demonstrate the value and significance of SBRS from both theoretical and practical perspectives. • We introduce a hierarchical framework for the modeling of SBRS, which is believed to cover nearly all the components and their relations associated with a session structure, to the best of our understanding. Further, the special data characteristics, the critical problem complexities and the key challenges in SBRS have been thoroughly revealed and illustrated. To the best of our knowledge, this is the first work to systematically categorize the above aspects of SBRS. • We review the current research progress of SBRS by providing a systematic categorization of the existing work from two dimensions: the research issue perspective and the technical perspective. Following the technical categorization, a detailed introduction to the representative approaches in each category is created. This brings about not only an overview of the progress made so far but also the necessary technical details of solutions. • By comparing the research progress to the SBRS problem complexities and challenges, we offer several prospects of future SBRS research. The rest of this survey is organized as follows. We first define the research scenarios and key concepts, and then formalize the research issues in Section 2. We then present the significance, complexities and key challenges of SBRS in Section 3, followed by an overview of the SBRS research evolution and the attention paid by the related research communities to SBRS in Section 4. Further, in Section 5, we categorize SBRS from the research issue and technical perspectives respectively. Following the technical categorization, we review two main categories of approaches in the subsequent Sections 6 and 7 respectively. Lastly, we offer several future prospects of SBRS in Section 8 and conclude the work in Section 9.