Manos Papagelis

Alleviating the sparsity problem of collaborative filtering using trust inferences

Collaborative Filtering (CF), the prevalent recommendation approach, has been successfully used to identify users that can be characterized as “similar” according to their logged history of prior transactions. However, the applicability of CF is limited due to the sparsity problem, which refers to a situation that transactional data are lacking or are insufficient. In an attempt to provide high-quality recommendations even when data are sparse, we propose a method for alleviating sparsity using trust inferences. Trust inferences are transitive associations between users in the context of an underlying social network and are valuable sources of additional information that help dealing with the sparsity and the cold-start problems. A trust computational model has been developed that permits to define the subjective notion of trust by applying confidence and uncertainty properties to network associations. We compare our method with the classic CF that does not consider any transitive associations. Our experimental results indicate that our method of trust inferences significantly improves the quality performance of the classic CF method.

Sampling Online Social Networks

As online social networking emerges, there has been increased interest to utilize the underlying network structure as well as the available information on social peers to improve the information needs of a user. In this paper, we focus on improving the performance of information collection from the neighborhood of a user in a dynamic social network. We introduce sampling-based algorithms to efficiently explore a users social network respecting its structure and to quickly approximate quantities of interest. We introduce and analyze variants of the basic sampling scheme exploring correlations across our samples. Models of centralized and distributed social networks are considered. We show that our algorithms can be utilized to rank items in the neighborhood of a user, assuming that information for each user in the network is available. Using real and synthetic data sets, we validate the results of our analysis and demonstrate the efficiency of our algorithms in approximating quantities of interest. The methods we describe are general and can probably be easily adopted in a variety of strategies aiming to efficiently collect information from a social graph.

Suggesting ghost edges for a smaller world

Small changes in the network topology can have dramatic effects on its capacity to disseminate information. In this paper, we consider the problem of adding a small number of ghost edges in the network in order to minimize the average shortest-path distance between nodes, towards a smaller-world network. We formalize the problem of suggesting ghost edges and we propose a novel method for quickly evaluating the importance of ghost edges in sparse graphs. Through experiments on real and synthetic data sets, we demonstrate that our approach performs very well, for a varying range of conditions, and it outperforms sensible baselines.

CONFIOUS: managing the electronic submission and reviewing process of scientific conferences

Most scientific communities have recently established policies and mechanisms to put into practice electronic conference management, mainly by exploiting the Internet as the communication and cooperation infrastructure. Their foremost objective is to reduce the operational and communication costs but to maintain high quality reviewing and the fairness of the evaluation process. Interestingly, we report on experience gained by an implemented system named Confious. Confious [8] is a state-of-the-art management system that combines modern design, sophisticated algorithms and a powerful engine to help the program committee (PC) Chair to effortlessly accomplish a number of complicated tasks and carry out the necessary activities to produce the proceedings of a scientific conference. We are principally interested in (a) describing the workflow dynamics of a real-world scientific process, (b) identifying the main concerns of the person in charge of the conference organization, (c) providing mechanisms that enable the efficient management and monitoring of the overall coordination process.

Refining Social Graph Connectivity via Shortcut Edge Addition

Small changes on the structure of a graph can have a dramatic effect on its connectivity. While in the traditional graph theory, the focus is on well-defined properties of graph connectivity, such as biconnectivity, in the context of a social graph, connectivity is typically manifested by its ability to carry on social processes. In this paper, we consider the problem of adding a small set of nonexisting edges (shortcuts) in a social graph with the main objective of minimizing its characteristic path length. This property determines the average distance between pairs of vertices and essentially controls how broadly information can propagate through a network. We formally define the problem of interest, characterize its hardness and propose a novel method, path screening, which quickly identifies important shortcuts to guide the augmentation of the graph. We devise a sampling-based variant of our method that can scale up the computation in larger graphs. The claims of our methods are formally validated. Through experiments on real and synthetic data, we demonstrate that our methods are a multitude of times faster than standard approaches, their accuracy outperforms sensible baselines and they can ease the spread of information in a network, for a varying range of conditions.

Efficient sampling of information in social networks

As online social networking emerges, there has been increased interest to utilize the underlying social structure as well as the available social information to improve search. In this paper, we focus on improving the performance of information collection from the neighborhood of a user in a dynamic social network. To this end, we introduce sampling based algorithms to quickly approximate quantities of interest from the vicinity of a users social graph. We then introduce and analyze variants of this basic scheme exploring correlations across our samples. Models of centralized and distributed social networks are considered. We show that our algorithms can be utilized to rank items in the neighborhood of a user, assuming that information for each user in the network is available. Using real and synthetic data sets, we validate the results of our analysis and demonstrate the efficiency of our algorithms in approximating quantities of interest. The methods we describe are general and can probably be easily adopted in a variety of strategies aiming to efficiently collect information from a social graph.

A Method for Estimating the Precision of Placename Matching

Information in digital libraries and information systems frequently refers to locations or objects in geographic space. Digital gazetteers are commonly employed to match the referred placenames with actual locations in information integration and data cleaning procedures. This process may fail due to missing information in the gazetteer, multiple matches, or false positive matches. We have analyzed the cases of success and reasons for failure of the mapping process to a gazetteer. Based on these, we present a statistical model that permits estimating 1) the completeness of a gazetteer with respect to the specific target area and application, 2) the expected precision and recall of one-to-one mappings of source placenames to the gazetteer, 3) the semantic inconsistency that remains in one-to-one mappings, and 4) the degree to which the precision and recall are improved under knowledge of the identity of higher levels in a hierarchy of places. The presented model is based on statistical analysis of the mapping process of a large set of placenames itself and does not require any other background data. The statistical model assumes that a gazetteer is populated by a stochastic process. The paper discusses how future work could take deviations from this assumption into account. The method has been applied to a real case.

Qualitative Analysis of User-Based and Item-Based Prediction Algorithms for Recommendation Agents

Recommendation agents employ prediction algorithms to provide users with items that match their interests. In this paper, we describe and evaluate several prediction algorithms, some of which are novel in that they combine user-based and item-based similarity measures derived from either explicit or implicit ratings. We compare both statistical and decision-support accuracy metrics of the algorithms against different levels of data sparsity and different operational thresholds. The first metric evaluates the accuracy in terms of average absolute deviation, while the second evaluates how effectively predictions help users to select high-quality items. Our experimental results indicate better performance of item-based predictions derived from explicit ratings in relation to both metrics. Category-boosted predictions can lead to slightly better predictions when combined with explicit ratings, while implicit ratings (in the sense that we have defined them here) perform much worse than explicit ratings.

Enabling Social Navigation on the Web

For a place that gathers millions of people the Web seems pretty lonely at times. This is mainly due to the current predominant browsing scenario; that of an individual participating in an autonomous surfing session. We believe that people should be seen as an integral part of the browsing and searching activity towards a concept known as social navigation. In this work, we extend the typical Web browserpsilas functionality so as to raise awareness of other people having similar Web surfing goals at the current moment. We further present features and algorithms that facilitate online communication and collaboration towards common searching targets. The utility of our system is established by experimental studies. The extentions we present can be easily adopted in a typical Web browser.

Iclone: towards online social navigation

For a place that gathers millions of people the Web seems pretty lonely at times. This is mainly due to the current predominant browsing scenario; that of an individual participating in an autonomous surfing session. We believe that people should be seen as an integral part of the browsing and searching activity towards a concept known as social navigation. Based on this observation we present iClone (www.iclone.com), a social web browser that is able to raise awareness of other people surfing similar websites at the same time by utilizing temporal correlations of their web history logs and to facilitate online communication and collaboration.