A supply chain is the network of entities and processes involved in the production and distribution of a commodity. Supply chains are a critical backbone across industries like retail, manufacturing, healthcare, and automotive, driving everything from product availability to operational efficiency and customer satisfaction. Modern supply chains are 1) non-cooperative, functioning as fragmented systems where isolated technologies solve individual problems without integration, and 2) unadaptable, failing to adjust to real-time data and uncertainties, like demand fluctuations and regulatory changes. As a result, frequent manual overrides are required, as even small errors can lead to significant financial losses, strained customer relationships, or reputational damage. Modern Artificial Intelligence (AI) advancements offer great potential to unify fragmented supply chains into a seamless, adaptive system while enhancing automation, decision-making, and transparency. In this workshop, we will examine critical supply chain challenges and demonstrate how AI can provide accurate, efficient, and scalable solutions.

Recommender systems are an integral part of eCommerce services, helping to optimize revenue and user satisfaction. Bundle recommendation has recently gained attention by the research community since behavioral data supports that users often buy more than one product in a single transaction. In most cases, bundle recommendations are of the form "users who bought product A also bought products B, C, and D". Although such recommendations can be useful, there is no guarantee that products A, B, C, and D may actually be related to each other. In this paper, we address the problem of collection recommendation, i.e., recommending a collection of products that share a common theme and can potentially be purchased together in a single transaction. We extend on traditional approaches that use mostly transactional data by incorporating both domain knowledge from product suppliers in the form of hierarchies, as well as textual attributes from the products. Our approach starts by combining product hierarchies together with transactional data or domain knowledge to identify candidate sets of product collections. Then, it generates the product collection recommendations from these candidate sets by learning a deep similarity model that leverages textual attributes. Experimental evaluation on real data from the Home Depot online retailer shows that the proposed solution can recommend collections of products with increased accuracy when compared to expert-crafted collections.

Given an author-conference graph, how do we answer proximity queries (e.g., what are the most related conferences for John Smith?); how can we tailor the search result if the user provides additional yes/no type of feedback (e.g., what are the most related conferences for John Smith given that he does not like ICML?)? Given the potential computational complexity, we mainly devote ourselves to addressing the computational issues in this paper by proposing an efficient solution (referred to as iPoG-B) for bipartite graphs. Our experimental results show that the proposed fast solution (iPoGB) achieves significant speedup, while leading to the same ranking result.

This paper studies how to incorporate side information (such as users' feedback) in measuring node proximity on large graphs. Our method (ProSIN) is motivated by the well-studied random walk with restart (RWR). The basic idea behind ProSIN is to leverage side information to refine the graph structure so that the random walk is biased towards/away from some specific zones on the graph. Our case studies demonstrate that ProSIN is well-suited in a variety of applications, including neighborhood search, center-piece subgraphs, and image caption. Given the potential computational complexity of ProSIN, we also propose a fast algorithm (Fast-ProSIN) that exploits the smoothness of the graph structures with/without side information. Our experimental evaluation shows that fast-ProSIN achieves significant speedups (up to 49x) over straightforward implementations.

Typical Web-database systems receive read-only queries, that generate dynamic Web pages as a response, and write-only updates, that keep information up-to-date. Users expect short response times and low staleness. However, it may be extremely hard to apply all updates on time, i.e., keep zero staleness, and also get fast response times, especially in periods of bursty traffic. In this paper, we present the concept of quality contracts (QCs) which combines the two incomparable performance metrics: response time or quality of service (QoS), and staleness or quality of data (QoD). QCs allows individual users to express their preferences for the expected QoS and QoD of their queries by assigning "profit" values. To maximize the total profit from submitted QCs, we propose an adaptive algorithm, called QUTS. QUTS addresses the problem of prioritizing the scheduling of updates over queries using a two-level scheduling scheme that dynamically allocates CPU resources to updates and queries according to user preferences. We present the results of an extensive experimental study using real data (taken from a stock information Web site), where we show that QUTS performs better than baseline algorithms under the entire spectrum of QCs; QUTS also adapts fast to changing workloads.

The proliferation of database-driven web sites (or web-databases) has brought upon a plethora of applications where both Quality of Service (QoS) and Quality of Data (QoD) are of paramount importance to the end users. In our previous work, we have proposed Quality Contracts, a comprehensive framework for specifying multiple dimensions of QoS/QoD; we have also developed user-centric admission control and scheduling algorithms in web databases, whose goal is to maximize overall system performance. In this work, we turn our attention to the user side of the equation. Specifically, we propose to demonstrate how the adaptation of Quality Contracts (QCs) by the users can lead to vastly different performance results, both from the user point of view (i.e., user satisfaction) and also from the system point of view. Towards this, we propose to structure our demo in the form of an interactive game, where participants will be playing the role of users continuously adapting their QCs over time, while "playing" against system-generated users, who follow predetermined QC adaptation policies. Finally, we also propose to illustrate the effect of different admission control and scheduling policies.

Web-database systems are nowadays an integral part of everybody’s life, with applications ranging from monitoring/ trading stock portfolios, to personalized blog aggregation and news services, to personalized weather tracking services. For most of these services to be successful (and their users to be kept satisfied), two criteria need to be met: user requests must be answered in a timely fashion and using fresh data. This paper presents a framework to balance both requirements from the users’ perspective. Toward this, we propose a user satisfaction metric to measure the overall effectiveness of the Web-database system. We also provide a set of algorithms to dynamically optimize this metric, through query admission control and update frequency modulation. Finally, we present extensive experimental results which compare our proposed algorithms to the current state of the art and show that we outperform competitors under various workloads (generated based on real traces) and user requirements.

Many real applications can be modeled using bipartite graphs, such as users vs. files in a P2P system, traders vs. stocks in a financial trading system, conferences vs. authors in a scientific publication network, and so on. We introduce two operations on bipartite graphs: 1) identifying similar nodes (Neighborhood formation), and 2) finding abnormal nodes (Anomaly detection). And we propose algorithms to compute the neighborhood for each node using random walk with restarts and graph partitioning; we also propose algorithms to identify abnormal nodes, using neighborhood information. We evaluate the quality of neighborhoods based on semantics of the datasets, and we also measure the performance of the anomaly detection algorithm with manually injected anomalies. Both effectiveness and efficiency of the methods are confirmed by experiments on several real datasets.