Neural recommender models for sparse and skewed behavioral data

Modern online platforms offer recommendations and personalized search and services to a large and diverse user base while still aiming to acquaint users with the broader community on the platform. Prior work backed by large volumes of user data has shown that user retention is reliant on catering to their specific eccentric tastes, in addition to providing them popular services or content on the platform. Long-tailed distributions are a fundamental characteristic of human activity, owing to the bursty nature of human attention. As a result, we often observe skew in data facets that involve human interaction. While there are superficial similarities to Zipf's law in textual data and other domains, the challenges with user data extend further. Individual words may have skewed frequencies in the corpus, but the long-tail words by themselves do not significantly impact downstream text-mining tasks. On the contrary, while sparse users (a majority on most online platforms) contribute little to the training data, they are equally crucial at inference time. Perhaps more so, since they are likely to churn. In this thesis, we study platforms and applications that elicit user participation in rich social settings incorporating user-generated content, user-user interaction, and other modalities of user participation and data generation. For instance, users on the Yelp review platform participate in a follower-followee network and also create and interact with review text (two modalities of user data). Similarly, community question-answer (CQA) platforms incorporate user interaction and collaboratively authored content over diverse domains and discussion threads. Since user participation is multimodal, we develop generalizable abstractions beyond any single data modality. Specifically, we aim to address the distributional mismatch that occurs with user data independent of dataset specifics; While a minority of the users generates most training samples, it is insufficient only to learn the preferences of this subset of users. As a result, the data's overall skew and individual users' sparsity are closely interlinked: sparse users with uncommon preferences are under-represented. Thus, we propose to treat these problems jointly with a skew-aware grouping mechanism that iteratively sharpens the identification of preference groups within the user population. As a result, we improve user characterization; content recommendation and activity prediction (+6-22% AUC, +6-43% AUC, +12-25% RMSE over state-of-the-art baselines), primarily for users with sparse activity. The size of the item or content inventories compounds the skew problem. Recommendation models can achieve very high aggregate performance while recommending only a tiny proportion of the inventory (as little as 5%) to users. We propose a data-driven solution guided by the aggregate co-occurrence information across items in the dataset. We specifically note that different co-occurrences are not equally significant; For example, some co-occurring items are easily substituted while others are not. We develop a self-supervised learning framework where the aggregate co-occurrences guide the recommendation problem while providing room to learn these variations among the item associations. As a result, we improve coverage to ~100% (up from 5%) of the inventory and increase long-tail item recall up to 25%. We also note that the skew and sparsity problems repeat across data modalities. For instance, social interactions and review content both exhibit aggregate skew, although individual users who actively generate reviews may not participate socially and vice-versa. It is necessary to differentially weight and merge different data sources for each user towards inference tasks in such cases. We show that the problem is inherently adversarial since the user participation modalities compete to describe a user accurately. We develop a framework to unify these representations while algorithmically tackling mode collapse, a well-known pitfall with adversarial models. A more challenging but important instantiation of sparsity is the few-shot setting or cross-domain setting. We may only have a single or a few interactions for users or items in the sparse domains or partitions. We show that contextualizing user-item interactions helps us infer behavioral invariants in the dense domain, allowing us to correlate sparse participants to their active counterparts (resulting in 3x faster training, ~19% recall gains in multi-domain settings). Finally, we consider the multi-task setting, where the platform incorporates multiple distinct recommendations and prediction tasks for each user. A single-user representation is insufficient for users who exhibit different preferences along each dimension. At the same time, it is counter-productive to handle correlated prediction or inference tasks in isolation. We develop a multi-faceted representation approach grounded on residual learning with heterogeneous knowledge graph representations, which provides us an expressive data representation for specialized domains and applications with multimodal user data. We achieve knowledge sharing by unifying task-independent and task-specific representations of each entity with a unified knowledge graph framework. In each chapter, we also discuss and demonstrate how the proposed frameworks directly incorporate a wide range of gradient-optimizable recommendation and behavior models, maximizing their applicability and pertinence to user-centered inference tasks and platforms

Intelligent techniques for recommender systems

This thesis focuses on the data sparsity issue and the temporal dynamic issue in the context of collaborative filtering, and addresses them with imputation techniques, low-rank subspace techniques and optimizations techniques from the machine learning perspective. A comprehensive survey on the development of collaborative filtering techniques is also included

Role of emotion in information retrieval

The main objective of Information Retrieval (IR) systems is to satisfy searchers’ needs. A great deal of research has been conducted in the past to attempt to achieve a better insight into searchers’ needs and the factors that can potentially influence the success of an Information Retrieval and Seeking (IR&S) process. One of the factors which has been considered is searchers’ emotion. It has been shown in previous research that emotion plays an important role in the success of an IR&S process, which has the purpose of satisfying an information need. However, these previous studies do not give a sufficiently prominent position to emotion in IR, since they limit the role of emotion to a secondary factor, by assuming that a lack of knowledge (the need for information) is the primary factor (the motivation of the search). In this thesis, we propose to treat emotion as the principal factor in the system of needs of a searcher, and therefore one that ought to be considered by the retrieval algorithms. We present a more realistic view of searchers’ needs by considering not only theories from information retrieval and science, but also from psychology, philosophy, and sociology. We extensively report on the role of emotion in every aspect of human behaviour, both at an individual and social level. This serves not only to modify the current IR views of emotion, but more importantly to uncover social situations where emotion is the primary factor (i.e., source of motivation) in an IR&S process. We also show that the emotion aspect of documents plays an important part in satisfying the searcher’s need, in particular when emotion is indeed a primary factor. Given the above, we define three concepts, called emotion need, emotion object and emotion relevance, and present a conceptual map that utilises these concepts in IR tasks and scenarios. In order to investigate the practical concepts such as emotion object and emotion relevance in a real-life application, we first study the possibility of extracting emotion from text, since this is the first pragmatic challenge to be solved before any IR task can be tackled. For this purpose, we developed a text-based emotion extraction system and demonstrate that it outperforms other available emotion extraction approaches. Using the developed emotion extraction system, the usefulness of the practical concepts mentioned above is studied in two scenarios: movie recommendation and news diversification. In the movie recommendation scenario, two collaborative filtering (CF) models were proposed. CF systems aim to recommend items to a user, based on the information gathered from other users who have similar interests. CF techniques do not handle data sparsity well, especially in the case of the cold start problem, where there is no past rating for an item. In order to predict the rating of an item for a given user, the first and second models rely on an extension of state-of-the-art memory-based and model-based CF systems. The features used by the models are two emotion spaces extracted from the movie plot summary and the reviews made by users, and three semantic spaces, namely, actor, director, and genre. Experiments with two MovieLens datasets show that the inclusion of emotion information significantly improves the accuracy of prediction when compared with the state-of-the-art CF techniques, and also tackles data sparsity issues. In the news retrieval scenario, a novel way of diversifying results, i.e., diversifying based on the emotion aspect of documents, is proposed. For this purpose, two approaches are introduced to consider emotion features for diversification, and they are empirically tested on the TREC 678 Interactive Track collection. The results show that emotion features are capable of enhancing retrieval effectiveness. Overall, this thesis shows that emotion plays a key role in IR and that its importance needs to be considered. At a more detailed level, it illustrates the crucial part that emotion can play in • searchers, both as a primary (emotion need) and secondary factor (influential role) in an IR&S process; • enhancing the representation of a document using emotion features (emotion object); and finally, • improving the effectiveness of IR systems at satisfying searchers’ needs (emotion relevance)

A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects

Recommender systems have significantly developed in recent years in parallel with the witnessed advancements in both internet of things (IoT) and artificial intelligence (AI) technologies. Accordingly, as a consequence of IoT and AI, multiple forms of data are incorporated in these systems, e.g. social, implicit, local and personal information, which can help in improving recommender systems' performance and widen their applicability to traverse different disciplines. On the other side, energy efficiency in the building sector is becoming a hot research topic, in which recommender systems play a major role by promoting energy saving behavior and reducing carbon emissions. However, the deployment of the recommendation frameworks in buildings still needs more investigations to identify the current challenges and issues, where their solutions are the keys to enable the pervasiveness of research findings, and therefore, ensure a large-scale adoption of this technology. Accordingly, this paper presents, to the best of the authors' knowledge, the first timely and comprehensive reference for energy-efficiency recommendation systems through (i) surveying existing recommender systems for energy saving in buildings; (ii) discussing their evolution; (iii) providing an original taxonomy of these systems based on specified criteria, including the nature of the recommender engine, its objective, computing platforms, evaluation metrics and incentive measures; and (iv) conducting an in-depth, critical analysis to identify their limitations and unsolved issues. The derived challenges and areas of future implementation could effectively guide the energy research community to improve the energy-efficiency in buildings and reduce the cost of developed recommender systems-based solutions.Comment: 35 pages, 11 figures, 1 tabl