Recommending social activities, such as watching movies or having dinner is a typical function found in social networks or e-commerce sites. Besides certain websites which manage activity-related locations (e.g., foursquare.com), many items on product sale platforms (e.g, groupon.com) can naturally be mapped to social activities. For example, movie tickets can be thought of as activity items, which map to social activity "watch a movie". Traditional recommenders estimate the degree of interest by the target user to candidate activity items and, accordingly, promote the top-k activity items to the user. However, these systems ignore an important characteristic of social activities: people like to participate in them with their folks. This paper is the first one to consider this fact and improves the effectiveness of recommendation in two directions. First, we show that people, more often than not, prefer to find partners before participation in social activities. This means that if a system recommends an activity item alone, the user may give up the item, if she cannot think of a partner to attend the activity together. Therefore, we study the problem of activity-partner recommendation; i.e., for each recommended activity item, find a suitable partner for the user. This (i) saves the users time for finding activity partners, (ii) increases the likelihood that the activity item will be selected by the user, (iii) improves the effectiveness of recommender systems to users overall and enkindles their social enthusiasm. Our partner recommender is built upon the users historical attendance preferences, their social context, and geographic information. Second, we explore the impact of finding suitable partners to improve the effectiveness of recommending activities to users. Assuming that users tend to select the activities for which they can find suitable partners, we propose a partner-aware activity recommendation model, which integrates this hypothesis into conventional recommendation approaches. Our method first estimates the probability that the target user can find suitable partners for each candidate activity item, and then considers this probability when ranking them. Finally, the recommended items not only match the users' interests, but also have high chances to be selected by the users, because the users can find suitable partners to attend the corresponding activities together. We conduct experiments on real data that evaluate the effectiveness of activity-partner recommendation and partner-aware activity recommendation. The results verify that (i) suggesting partners greatly improves the likelihood that a recommended activity item is selected by the target user and (ii) considering the existence of suitable partners in the ranking of recommended items improves the accuracy of recommendation significantly.
Internet and Web technologies have changed our lives in ways we are not even fully aware. In the near future, Internet will interconnect more than 50 billion of things of the real world, nodes will sense billions of features of interest and properties, and things will be represented with Web-based bi-directional services with high-dynamic content and real-time data. This is the new era of the Internet and the Web of Things. The emergence of such paradigms implies the evolution and integration of the systems which they interact with. Thereby, it is essential to develop abstract models for representing, and simulating the Web of Things in order to establish new approaches. A model of the Web of Things based on a structured XML representation is described in this paper. We also present a simulator whose ultimate goal is to encapsulate the expected dynamics of the Web of Things, for the future development of Information Retrieval (IR) systems. The sim- ulator generates a real-time collection of XML documents, which contain spatio-temporal contexts, textual and sensed information with highly dynamic dimensions. The simulator is characterized among others for its flexibility and versatility to represent real-world scenarios and a unique perspective from information retrieval, we tested the simulator in terms of fundamentals variables.
A Knowledge graph is a graph with entities of different types as nodes and various relations among them as edges. The constructions of knowledge graphs in the past decades facilitate many applications, such as link prediction, web search analysis, question answering, etc. Knowledge graph embedding aims to represent entities and relations in a large-scale knowledge graph as elements in a continuous vector space. Existing methods, e.g., TransE, TransH and TransR, learn the embedding representation by defining a global margin-based loss function over the data. However, the optimal loss function is determined during experiments whose parameters are examined among a closed set of candidates. Moreover, embeddings over two knowledge graphs with different entities and relations share the same set of candidate loss functions, ignoring the locality of both graphs. This leads to the limited performance of embedding related applications. In this paper, a locally adaptive translation method for knowledge graph embedding, called TransA, is proposed to find the optimal loss function by adaptively determining its margin over different knowledge graphs. Then the convergence of TransA is verified from the aspect of its uniform stability. To make the embedding methods up-to-date when new vertices and edges are added into the knowledge graph, the incremental algorithm for TransA, called iTransA, is proposed by adaptively adjusting the optimal margin over time. Experiments on two benchmark data sets demonstrate the superiority of the proposed method, as compared to the-state-of-the-art ones.
Mobile applications consume device energy for their operations, and the fast rate of battery depletion on mobile devices poses a major usability hurdle. After the display, data communication is the second-biggest consumer of mobile device energy. At the same time, software applications that run on mobile devices represent a fast-growing product segment. Typically, these applications serve as front-end display mechanisms, which fetch data from remote servers and display the information to the user in an appropriate format -- incurring significant data communication overheads in the process. In this work, we propose methods to reduce energy overheads in mobile devices due to data communication by leveraging data caching technology. A review of existing caching mechanisms revealed that they are primarily designed for optimizing performance and cannot be easily ported to mobile devices for energy savings. Further, architectural differences between traditional client-server and mobile communications infrastructures make the use of existing caching technologies unsuitable in mobile devices. In this paper, we propose a set of two new caching approaches specifically designed with the constraints of mobile devices in mind: (a) a response caching approach, and (b) an object caching approach. Our experiments show that, even for a small cache size of 250 MB, object caching can reduce energy consumption on average by 45%, compared to the no-cache case, and response caching can reduce energy consumption by 20% compared to the no-cache case. The benefits increase with larger cache sizes. These results demonstrate the efficacy of our proposed method, and raise the possibility of significantly extending mobile device battery life.
In recent times, automated business processes and web services have become ubiquitous in diverse application spaces. Efficient composition of web services in real time while providing necessary Quality of Service (QoS) guarantees is a computationally complex problem and several heuristic based approaches have been proposed to compose services optimally. In this paper, we present the design of a scalable QoS-aware service composition mechanism which balances the computational complexity of service composition with the QoS guarantees of the composed service and achieves scalability. On one hand, we handle the case of a single QoS parameter using an intelligent search and pruning mechanism in the composed service space and show that our methodology yields near optimal solution on real benchmarks. On the other hand, we handle the case of multiple QoS parameters using aggregation techniques. As a final contribution, we explore search time versus solution quality trade-off using parameterized search algorithms that produce better quality solutions at the cost of time. We present experimental results to show the efficiency of our proposed mechanism.