Abstract
Modern online web-based systems continuously generate data at very fast rates. This continuous flow of data encompasses web content - e.g. posts, news, products, comments -, but also user feedback - e.g. ratings, views, reads, clicks, thumbs up -, as well as context information - device used, geographic info, social network, current user activity, weather. This is potentially overwhelming for systems and algorithms design to train in offline batches, given the continuous and potentially fast change of content, context and user preferences. Therefore it is important to investigate online methods to be able to transparently adapt to the inherent dynamics of online systems. Incremental models that learn from data streams are gaining attention in the recommender systems community, given their natural ability to deal with data generated in dynamic, complex environments. User modeling and personalization can particularly benefit from algorithms capable of maintaining models incrementally and online, as data is generated. The objective of this workshop is to foster contributions and bring together a growing community of researchers and practitioners interested in online, adaptive approaches to user modeling, recommendation and personalization, as well as other related tasks, such as evaluation, reproducibility, privacy and explainability.

Abstract
Electronic commerce is becoming a significant hub for sourcing products/services which helps organizations to connect with potential customers and gain competitive advantages, though little empirical work focuses on small businesses operating in developing countries to date. Increasingly, companies are looking to utilize social media to connect with stakeholders and pursue several benefits. This study aims to investigate the technological, organizational, and environmental (TOE) factors that influence small- and medium-sized enterprises' (SMEs) social media (SM) adoption in developing countries. This study used a closed-ended questionnaire to collect data from randomly-selected respondents (owners, executives, and managers) from SMEs in Pakistan. SMART PLS version 3.2.8 was used for path analysis of 316 responses and for structural equation modeling. The research findings include the direct influence of TOE factors (relative advantage, interactivity, visibility, top management support, and institutional pressure) on SMEs' SM adoption, and in turn SM adoption also has a positive influence on SMEs performance. Moreover, the coefficient of determination of the study showed that 77.7% of the variation in SM adoption occurs because of TOE factors and 29.8% variation in SMEs occurred because of SM adoption. This paper has implications for practitioners and scholars interested in exploring the SM adoption and usage by SMEs.

Abstract
This article aims at identifying the effect of executive compensation, mediated by the nature of the control, on the sustained performance of the company in a defined ownership context, in which a shareholder or group holds more than half of the voting shares. Despite the wide literature on the relationship between executive compensation and performance, there is little evidence of the lagged effect of compensation on future performance and sustainability. This is particularly significant in countries where concentrated ownership structures dominate, where the controller has incentives to control the behavior of executives and may use the negotiation of such remuneration in order to guide management for their interests or as an instrument of expropriation of minority shareholders. The present study advances along this path by including in the analysis the nature of the control and by considering the lagged effects of the vesting period on performance. Based on executive compensation data from a group of listed Brazilian companies, the effect of this remuneration on change in shareholders' wealth in the three-year period is analyzed by means of multiple regression. The results pointed out that the impact of the variable remuneration on future performance depend on the nature of the remuneration, and on the type of ownership control. Effectively, the weight of the bonus in the total remuneration does not show significant effect. However, the weight of the stock-based remuneration is negatively related with the future performance. Moreover, this result applies both to companies with a controller shareholder and to companies with control shared, but not to companies with dispersed ownership, for which there is evidence of a positive relationship between the importance of the stock-based remuneration and the future performance.

Abstract
Bin picking is a challenging problem common to many industries, whose automation will lead to great economic benefits. This paper presents a method for estimating the pose of a set of randomly arranged bent tubes, highly subject to occlusions and entanglement. The approach involves using a depth sensor to obtain a point cloud of the bin. The algorithm begins by filtering the point cloud to remove noise and segmenting it using the surface normals. Tube sections are then modeled as cylinders that are fitted into each segment using RANSAC. Finally, the sections are combined into complete tubes by adopting a greedy heuristic based on the distance between their endpoints. Experimental results with a dataset created with a Zivid sensor show that this method is able to provide estimates with high accuracy for bins with up to ten tubes. Therefore, this solution has the potential of being integrated into fully automated bin picking systems.

Abstract
Physical properties of materials are seldom studied in the context of packing problems. In this work we study the behavior of semifluids: materials with particular characteristics that share properties both with solids and with fluids. We describe the importance of some specific semifluids in an industrial context, and propose methods for tackling the problem of packing them, taking into account several practical requirements and physical constraints. The problem dealt with here can be reduced to a variant of two-dimensional knapsack problem with guillotine cuts, where items are splittable in one of the dimensions and the number of cuts is not limited. Although the focus of this paper is on the computation of practical solutions, it also uncovers interesting mathematical properties of this problem, which differentiate it from other packing problems. A thorough computational experiment is used to assess the quality of the approaches proposed, which is analyzed and compared to relevant methods from the literature.

Abstract
The resource-constrained project scheduling problem (RCPSP) is one of the most studied problems in the project scheduling literature, and aims at constructing a project schedule with a minimum makespan that satisfies both the precedence relations of the network and the limited availability of the renewable resources. The problem has attracted attention due to its NP hardness status, and different algorithms have been proposed that solve a wide variety of RCPSP instances to optimality or near-optimality. In this paper, we analyse the hardness of this problem from an experimental point-of-view by testing different algorithms on a huge set of existing instances and detect which ones are difficult to solve. To that purpose, we propose a three-phased approach that makes use of five elementary blocks, well-performing algorithms and a huge amount of computational power to transform easy RCPSP instances into very hard ones. The purpose of this study is to create insight and understanding into what makes an RCPSP instance hard, and propose a new dataset that consists of a small set of instances that are impossible to solve with the algorithms currently existing in the literature. These instances should be as small as possible in terms of number of activities and resources, and should be as diverse as possible in terms of network structure and resource strictness. Such a dataset should enable researchers to focus their attention on the development of radically new algorithms to solve the RCPSP rather than gradually improving current algorithms that can solve the existing RCPSP instances only slightly better.