Sequential recommender systems: Challenges, progress and prospects

© 2019 International Joint Conferences on Artificial Intelligence. All rights reserved. The emerging topic of sequential recommender systems (SRSs) has attracted increasing attention in recent years. Different from the conventional recommender systems (RSs) including collaborative filtering and content-based filtering, SRSs try to understand and model the sequential user behaviors, the interactions between users and items, and the evolution of users' preferences and item popularity over time. SRSs involve the above aspects for more precise characterization of user contexts, intent and goals, and item consumption trend, leading to more accurate, customized and dynamic recommendations. In this paper, we provide a systematic review on SRSs. We first present the characteristics of SRSs, and then summarize and categorize the key challenges in this research area, followed by the corresponding research progress consisting of the most recent and representative developments on this topic. Finally, we discuss the important research directions in this vibrant area

Modeling multi-purpose sessions for next-item recommendations via mixture-channel purpose routing networks

© 2019 International Joint Conferences on Artificial Intelligence. All rights reserved. A session-based recommender system (SBRS) suggests the next item by modeling the dependencies between items in a session. Most of existing SBRSs assume the items inside a session are associated with one (implicit) purpose. However, this may not always be true in reality, and a session may often consist of multiple subsets of items for different purposes (e.g., breakfast and decoration). Specifically, items (e.g., bread and milk) in a subset have strong purpose-specific dependencies whereas items (e.g., bread and vase) from different subsets have much weaker or even no dependencies due to the difference of purposes. Therefore, we propose a mixture-channel model to accommodate the multi-purpose item subsets for more precisely representing a session. To address the shortcomings in existing SBRSs, this model recommends more diverse items to satisfy different purposes. Accordingly, we design effective mixture-channel purpose routing networks (MCPRNs) with a purpose routing network to detect the purposes of each item and assign them into the corresponding channels. Moreover, a purpose-specific recurrent network is devised to model the dependencies between items within each channel for a specific purpose. The experimental results show the superiority of MCPRN over the state-of-the-art methods in terms of both recommendation accuracy and diversity

Intention Nets: Psychology-Inspired User Choice Behavior Modeling for Next-Basket Prediction

Human behaviors are complex, which are often observed as a sequence of heterogeneous actions. In this paper, we take user choices for shopping baskets as a typical case to study the complexity of user behaviors. Most of existing approaches often model user behaviors in a mechanical way, namely treating a user action sequence as homogeneous sequential data, such as hourly temperatures, which fails to consider the complexity in user behaviors. In fact, users' choices are driven by certain underlying intentions (e.g., feeding the baby or relieving pain) according to Psychological theories. Moreover, the durations of intentions to drive user actions are quite different; some of them may be persistent while others may be transient. According to Psychological theories, we develop a hierarchical framework to describe the goal, intentions and action sequences, based on which, we design Intention Nets (IntNet). In IntNet, multiple Action Chain Nets are constructed to model the user actions driven by different intentions, and a specially designed Persistent-Transient Intention Unit models the different intention durations. We apply the IntNet to next-basket prediction, a recent challenging task in recommender systems. Extensive experiments on real-world datasets show the superiority of our Psychology-inspired model IntNet over the state-of-the-art approaches.</jats:p

Intention2Basket: A neural intention-driven approach for dynamic next-basket planning

User purchase behaviours are complex and dynamic, which are usually observed as multiple choice actions across a sequence of shopping baskets. Most of the existing next-basket prediction approaches model user actions as homogeneous sequence data without considering complex and heterogeneous user intentions, impeding deep understanding of user behaviours from the perspective of human inside drivers and thus reducing the prediction performance. Psychological theories have indicated that user actions are essentially driven by certain underlying intentions (e.g., diet and entertainment). Moreover, different intentions may influence each other while different choices usually have different utilities to accomplish an intention. Inspired by such psychological insights, we formalize the next-basket prediction as an Intention Recognition, Modelling and Accomplishing problem and further design the Intention2Basket (Int2Ba in short) model. In Int2Ba, an Intention Recognizer, a Coupled Intention Chain Net, and a Dynamic Basket Planner are specifically designed to respectively recognize, model and accomplish the heterogeneous intentions behind a sequence of baskets to better plan the next-basket. Extensive experiments on real-world datasets show the superiority of Int2Ba over the state-of-the-art approaches

Graph Learning based Recommender Systems: A Review

Recent years have witnessed the fast development of the emerging topic of Graph Learning based Recommender Systems (GLRS). GLRS employ advanced graph learning approaches to model users' preferences and intentions as well as items' characteristics for recommendations. Differently from other RS approaches, including content-based filtering and collaborative filtering, GLRS are built on graphs where the important objects, e.g., users, items, and attributes, are either explicitly or implicitly connected. With the rapid development of graph learning techniques, exploring and exploiting homogeneous or heterogeneous relations in graphs are a promising direction for building more effective RS. In this paper, we provide a systematic review of GLRS, by discussing how they extract important knowledge from graph-based representations to improve the accuracy, reliability and explainability of the recommendations. First, we characterize and formalize GLRS, and then summarize and categorize the key challenges and main progress in this novel research area

Deep mantle roots and continental emergence: implications for whole-Earth elemental cycling, long-term climate, and the Cambrian explosion

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The Shapes of Cooperatively Rearranging Regions in Glass Forming Liquids

The shapes of cooperatively rearranging regions in glassy liquids change from being compact at low temperatures to fractal or ``stringy'' as the dynamical crossover temperature from activated to collisional transport is approached from below. We present a quantitative microscopic treatment of this change of morphology within the framework of the random first order transition theory of glasses. We predict a correlation of the ratio of the dynamical crossover temperature to the laboratory glass transition temperature, and the heat capacity discontinuity at the glass transition, Delta C_p. The predicted correlation agrees with experimental results for the 21 materials compiled by Novikov and Sokolov.Comment: 9 pages, 6 figure