Blackspot Location and Recommendation to Reduce Number and Severity of Accidents on Purbaleunyi Toll Road

Toll roads, as land transportation infrastructure, have an important role in Indonesia. With a high number of road crashes in Indonesia, with about 40,000 people die on the road each year, the determination of blackspot locations is crucial. The aim of this study is to analyze blackspot location on a toll road in Indonesia and, furthermore, to provide recommendations in order to reduce number and severity of accident. A case study is carried out on a toll road, named Purbaleunyi Toll Road, in West Java. Accident rate value and UCL method are used in this study to determine blackspot locations. The results indicated that there are many blackspot locations along the toll road and recommended solutions provided are adherence to traffic regulation, adherence to vehicle worthiness,dissemination of road safety importance to road users, and the implementation of blackspot treatments continuously

Signed Distance-based Deep Memory Recommender

Personalized recommendation algorithms learn a user's preference for an item by measuring a distance/similarity between them. However, some of the existing recommendation models (e.g., matrix factorization) assume a linear relationship between the user and item. This approach limits the capacity of recommender systems, since the interactions between users and items in real-world applications are much more complex than the linear relationship. To overcome this limitation, in this paper, we design and propose a deep learning framework called Signed Distance-based Deep Memory Recommender, which captures non-linear relationships between users and items explicitly and implicitly, and work well in both general recommendation task and shopping basket-based recommendation task. Through an extensive empirical study on six real-world datasets in the two recommendation tasks, our proposed approach achieved significant improvement over ten state-of-the-art recommendation models

Improving Reachability and Navigability in Recommender Systems

In this paper, we investigate recommender systems from a network perspective and investigate recommendation networks, where nodes are items (e.g., movies) and edges are constructed from top-N recommendations (e.g., related movies). In particular, we focus on evaluating the reachability and navigability of recommendation networks and investigate the following questions: (i) How well do recommendation networks support navigation and exploratory search? (ii) What is the influence of parameters, in particular different recommendation algorithms and the number of recommendations shown, on reachability and navigability? and (iii) How can reachability and navigability be improved in these networks? We tackle these questions by first evaluating the reachability of recommendation networks by investigating their structural properties. Second, we evaluate navigability by simulating three different models of information seeking scenarios. We find that with standard algorithms, recommender systems are not well suited to navigation and exploration and propose methods to modify recommendations to improve this. Our work extends from one-click-based evaluations of recommender systems towards multi-click analysis (i.e., sequences of dependent clicks) and presents a general, comprehensive approach to evaluating navigability of arbitrary recommendation networks

The Topology of Music Recommendation Networks

We study the topology of several music recommendation networks, which rise from relationships between artist, co-occurrence of songs in playlists or experts' recommendation. The analysis uncovers the emergence of complex network phenomena in this kind of recommendation networks, built considering artists as nodes and their resemblance as links. We observe structural properties that provide some hints on navigation and possible optimizations on the design of music recommendation systems. Finally, the analysis derived from existing music knowledge sources provides a deeper understanding of the human music similarity perceptions.Comment: 15 pages, 3 figure