Alexis Papadimitriou

A generalized taxonomy of explanations styles for traditional and social recommender systems

Recommender systems usually provide explanations of their recommendations to better help users to choose products, activities or even friends. Up until now, the type of an explanation style was considered in accordance to the recommender system that employed it. This relation was one-to-one, meaning that for each different recommender systems category, there was a different explanation style category. However, this kind of one-to-one correspondence can be considered as over-simplistic and non generalizable. In contrast, we consider three fundamental resources that can be used in an explanation: users, items and features and any combination of them. In this survey, we define (i) the Human style of explanation, which provides explanations based on similar users, (ii) the Item style of explanation, which is based on choices made by a user on similar items and (iii) the Feature style of explanation, which explains the recommendation based on item features rated by the user beforehand. By using any combination of the aforementioned styles we can also define the Hybrid style of explanation. We demonstrate how these styles are put into practice, by presenting recommender systems that employ them. Moreover, since there is inadequate research in the impact of social web in contemporary recommender systems and their explanation styles, we study new emerged social recommender systems i.e. Facebook Connect explanations (HuffPo, Netflix, etc.) and geo-social explanations that combine geographical with social data (Gowalla, Facebook Places, etc.). Finally, we summarize the results of three different user studies, to support that Hybrid is the most effective explanation style, since it incorporates all other styles.

Topology control algorithms for wireless sensor networks: a critical survey

In a densely deployed wireless sensor network, a single node has many neighbouring nodes with which direct communication would be possible when using sufficiently large transmission power. This is, however, not beneficial; high transmission power requires lots of energy, many neighbours are a burden for a MAC protocol, and routing protocols suffer from volatility in the network when nodes move around. To overcome these problem topology control can be applied. The idea is to deliberately restrict the set of nodes that are considered neighbours of a given node. This article surveys the most popular and efficient topology control algorithms for wireless ad hoc sensor networks.

Friendlink: Link prediction in social networks via bounded local path traversal

Online social networks (OSNs) like Facebook, Myspace, and Hi5 have become popular, because they allow users to easily share content or expand their social circle. OSNs recommend new friends to registered users based on local graph features (i.e. based on the number of common friends that two users share). However, OSNs do not exploit all different length paths of the network. Instead, they consider only pathways of maximum length 2 between a user and his candidate friends. On the other hand, there are global approaches, which detect the overall path structure in a network, being computationally prohibitive for huge-size social networks. In this paper, we provide friend recommendations, also known as the link prediction problem, by traversing all paths of a bounded length, based on the “algorithmic small world hypothesis”. As a result, we are able to provide more accurate and faster friend recommendations. We perform an extensive experimental comparison of the proposed method against existing link prediction algorithms, using two real data sets (Hi5 and Epinions). Our experimental results show that our FriendLink algorithm outperforms other approaches in terms of effectiveness and efficiency in both real data sets.

EBC: A Topology Control Algorithm for Achieving High QoS in Sensor Networks

A novel approach for achieving high Quality of Service (QoS) in sensor networks via topology control is introduced and experimentally assessed in this paper. Our approach falls in the broader discipline of graph structural mining, and exploits a leading concept initially studied in the context of Social Network Analysis (SNA), namely betweenness. Particularly, in our research betweenness is applied in terms of a graph structural mining measure embedded in the core layer of our proposed topology control algorithm, called Edge Betweenness Centrality (EBC). EBC allows us to evaluate relationships between entities of the network (e.g., nodes, edges), and hence identify different roles among them (e.g., brokers, outliers). In turn, deriving knowledge is further exploited to define raking operators that look at structural properties of the graph modeling the target sensor network. Based on these amenities, our topology control algorithm is able of providing an “insight” of the graph structure of the network on top which control over information flow, message delivery, latency and energy dissipation among nodes can be easily deployed.

Query Sensitive Storage for Wireless Sensor Networks

Storage management in wireless sensor networks is an area that has started to attract significant attention, and several methods have been proposed, such as Local Storage (LS), Data-Centric Storage (DCS) and more recently Location-Centric Storage (LCS). Several modern applications, like context-dependent information dissemination for pervasive computing, on-demand warning in surveillance sensor networks and roadway safety warning, require that each originating event is stored around its point of origin. LCS is a suitable approach for such applications. Though, LCS does not take into consideration the origin of the queries,which is equally important to the storage method, because it has immediate influence on the experienced latency. This paper proposes a simple yet effective way of reducing the network latency, namely the Query Sensitive Storage (QSS) protocol. QSS makes certain that not only will the queries be answered, but all subsequent queries that originated in the same area will be answered faster. The experimental evaluation using the J-Sim simulator attests that with the proposed QSS protocol we can achieve smaller network latency at a minimum storage cost as compared to its state-of-the-art competitor, namely LCS.