A Survey on Session-based Recommender Systems <s> Fig. 3. An overview of challenges in SBRS <s> Preface Part I. Clustering, Data and Similarity Measures: 1. Data clustering 2. DataTypes 3. Scale conversion 4. Data standardization and transformation 5. Data visualization 6. Similarity and dissimilarity measures Part II. Clustering Algorithms: 7. Hierarchical clustering techniques 8. Fuzzy clustering algorithms 9. Center Based Clustering Algorithms 10. Search based clustering algorithms 11. Graph based clustering algorithms 12. Grid based clustering algorithms 13. Density based clustering algorithms 14. Model based clustering algorithms 15. Subspace clustering 16. Miscellaneous algorithms 17. Evaluation of clustering algorithms Part III. Applications of Clustering: 18. Clustering gene expression data Part IV. Matlab and C++ for Clustering: 19. Data clustering in Matlab 20. Clustering in C/C++ A. Some clustering algorithms B. Thekd-tree data structure C. Matlab Codes D. C++ Codes Subject index Author index. <s> BIB001 </s> A Survey on Session-based Recommender Systems <s> Fig. 3. An overview of challenges in SBRS <s> The similarity between nominal objects is not straightforward, especially in unsupervised learning. This paper proposes coupled similarity metrics for nominal objects, which consider not only intra-coupled similarity within an attribute (i.e., value frequency distribution) but also inter-coupled similarity between attributes (i.e. feature dependency aggregation). Four metrics are designed to calculate the inter-coupled similarity between two categorical values by considering their relationships with other attributes. The theoretical analysis reveals their equivalent accuracy and superior efficiency based on intersection against others, in particular for large-scale data. Substantial experiments on extensive UCI data sets verify the theoretical conclusions. In addition, experiments of clustering based on the derived dissimilarity metrics show a significant performance improvement. <s> BIB002 </s> A Survey on Session-based Recommender Systems <s> Fig. 3. 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> BIB003 </s> A Survey on Session-based Recommender Systems <s> Fig. 3. An overview of challenges in SBRS <s> This paper introduces a novel unsupervised outlier detection method, namely Coupled Biased Random Walks (CBRW), for identifying outliers in categorical data with diversified frequency distributions and many noisy features. Existing pattern-based outlier detection methods are ineffective in handling such complex scenarios, as they misfit such data. CBRW estimates outlier scores of feature values by modelling feature value level couplings, which carry intrinsic data characteristics, via biased random walks to handle this complex data. The outlier scores of feature values can either measure the outlierness of an object or facilitate the existing methods as a feature weighting and selection indicator. Substantial experiments show that CBRW can not only detect outliers in complex data significantly better than the state-of-the-art methods, but also greatly improve the performance of existing methods on data sets with many noisy features. <s> BIB004 </s> A Survey on Session-based Recommender Systems <s> Fig. 3. An overview of challenges in SBRS <s> While it may not be possible to build a data brain identical to a human, data science can still aspire to imaginative machine thinking. <s> BIB005
• Session heterogeneity: similarly, the session heterogeneity mainly refers to that in a sequence of sessions, different contextual sessions relate to the current session with different scales; some are even irrelevant or noisy sessions. • Context heterogeneity: the various types of contextual factors, e.g., time, venue, season, and user, contribute to the session evolution collaboratively but differently; they cannot be modelled with a unified method. (b) The couplings within each level, which are indicated by the yellow lines within each level. Here couplings BIB003 refer to the interactions that the different elements on each level are somehow dependent on each other, and there are diverse interactions between them. • Value-level coupling: it contains two different types of value couplings: intra-feature value coupling and inter-feature value coupling. The former refers to the interactions between different values from the same feature (e.g., values with similar frequencies are regarded similar w.r.t. their importance BIB001 BIB002 ) while the latter means the interactions between values from different features (e.g., the category of an item may have some effect on its price). • Feature-level coupling: it indicates one feature has influence on another, or the combination of several features infers a particular pattern, revealing a special semantic meaning; some examples can be found in BIB004 . • Item-level coupling: this indicates the common interactions over items within a session. For instance, some items (e.g., bread and milk) in a supermarket are usually bought together. • Session-level coupling: it means the interactions between different sessions. In real-world transactions, the recent sessions usually have some impact on the current one. For instance, a user who has bought a car a week ago may have a car insurance purchased in the current transaction. • Domain-level coupling: it refers to the interactions between different domains. Suppose a user likes the movie "Titanic", then he may love the topic song "My heart will go on" in this movie. In this case, the two domains (movie and song) are interactive. (c) Other complexities within each level: except for the typical heterogeneity and coupling issues on each level, there are other complexities BIB005  . For instance, on the item level, the complexities may include the implicit item dependency, the item imbalance, and so on; on the session level, there may be challenges including the long-term dependency over sessions, and the session modeling under certain context factors like venue, time and users. (d) The interactions between different levels: various interactions may exist across different levels. Here we list the two most typical ones. • Feature-item interaction: the features of an item usually influences its occurrences in a transaction. For instance, items belonging to the same category are more likely to occur together. The good modelling of this interaction could benefit the cold-start item issues, in which item occurrence data is quite limited. • Session-item interaction: other sessions may lead to whether an item occur or not in the current session. For instance, a user who has bought a house recently may buy some furniture in the current transaction.