Magdalini Eirinaki

San Jose State University | SJSU · Department of Computer Engineering

Recommender systems have been widely used in different application domains including energy-preservation, e-commerce, healthcare, social media, etc. Such applications require the analysis and mining of massive amounts of various types of user data, including demographics, preferences, social interactions, etc. in order to develop accurate and preci...

Recommender systems have been widely used in different application domains including energy-preservation, e-commerce, healthcare, social media, etc. Such applications require the analysis and mining of massive amounts of various types of user data, including demographics, preferences, social interactions, etc. in order to develop accurate and preci...

Visual impairment refers to the partial or complete loss of one’s ability to see. It is estimated that there are 1.3 billion people in the world with some form of vision loss. In this work, we present Viva, an Android-based virtual assistant aiming to help people with visual impairment. The application provides haptic and voice navigation assistanc...

In an organization as big as a university that has many distinct departments and administrative bodies, it becomes almost impossible to easily obtain information online or by other means. Assistance over the phone or in-person is often limited to office hours and the information online is scattered through numerous (often nested) web pages, often i...

Crime has been prevalent in our society for a very long time and it continues to be so even today. Currently, many cities have released crime-related data as part of an open data initiative. Using this as input, we can apply analytics to be able to predict and hopefully prevent crime in the future. In this work, we applied big data analytics to the...

Social recommendations have been a very intriguing domain for researchers in the past decade. The main premise is that the social network of a user can be leveraged to enhance the rating-based recommendation process. This has been achieved in various ways, and under different assumptions about the network characteristics, structure, and availabilit...

Wearable technology allows users to monitor their activity and pursue a healthy lifestyle through the use of embedded sensors. Such wearables usually connect to a mobile application that allows them to set their profile and keep track of their goals. However, due to the relatively “high maintenance” of such applications, where a significant amount...

In this paper we propose a novel, cloud-based framework to support citizens and city officials in the building permit process. The proposed framework is efficient, user-friendly, and transparent with a quick turn-around time for homeowners. Compared to existing permit systems, the proposed smart city permit framework provides a pre-permitting decis...

Social media is rapidly becoming the main source of news consumption for users, raising significant challenges to news aggregation and recommendation tasks. One of these challenges concerns the recommendation of very recent news. To tackle this problem, approaches to the prediction of news popularity have been proposed. In this paper we study the t...

Thousands of news are published everyday reporting worldwide events. Most of these news obtain a low level of popularity and only a small set of events become highly popular in social media platforms. Predicting rare cases of highly popular news is not a trivial task due to shortcomings of standard learning approaches and evaluation metrics. So far...

The process of decision making in humans involves a combination of the genuine information held by the individual , and the external influence from their social network connections. This helps individuals to make decisions or adopt behaviors, opinions or products. In this work, we seek to investigate under which conditions and with what cost we can...

In this paper we propose a novel cloud-based platform for building permit system that is efficient, userfriendly, transparent, and has quick turn-around time for homeowners. Compared to the existing permit systems, the proposed smart city permit framework provides a prepermitting decision workflow, and incorporates a data analytics and mining modul...

This paper proposes an unconventional carpool-matching system concept that is different from existing systems with four innovative operational features: (F1) The proposed matching system will be used by members of an association and sponsored by the association, e.g., the employees of a company, members of a homeowner association, employees of a sh...

Interactive database exploration is a key task in information mining. Relational databases have been long used as a critical infrastructure component to access and analyze large volumes of data in a variety of applications, including ad-hoc analytics over big data, large-scale data warehouses that support business-intelligence tools, and services f...

As the amount of recorded digital information increases, there is a growing need for flexible recommender systems which can incorporate richly structured data sources to improve recommendations. In this paper, we show how a recently introduced statistical relational learning framework can be used to develop a generic and extensible hybrid rec-ommen...

Online advertisements are a major source of profit and customer attraction for web-based businesses. In a successful advertisement campaign, both users and businesses can benefit, as users are expected to respond positively to special offers and recommendations of their liking and businesses are able to reach the most promising potential customers....

The success of a product/service in e-commerce largely depends on the user reviews. A product/service that has a higher average review or rating usually gets picked against a similar product/service with less favorable reviews. Reviews usually have an overall rating, but most of the times there are sub-texts in the review body that describe certain...

The success of a product/service in e-commerce largely depends on the user reviews. A product/service that has a higher average review or rating usually gets picked against a similar product/service with less favorable reviews. Reviews usually have an overall rating, but most of the times there are sub-texts in the review body that describe certain...

Interactive database exploration is a key task in information mining. However, users who lack SQL expertise or familiarity with the database schema face great difficulties in performing this task. To aid these users, we developed the QueRIE system for personalized query recommendations. QueRIE continuously monitors the user's querying behavior and...

Smartphones are becoming a powerful platform for event recognition due to the number of sensors they are equipped with. This provides an opportunity to apply data mining techniques on movement data in order to recognize people's daily activities without changing their routine. In this paper, we present a methodology for collecting and analysing use...

Almost all users look at online ratings and reviews before buying a product, visiting a business, or using a service. These reviews are independent, authored by other users, and thus may convey useful information to the end user. Reviews usually have an overall rating, but most of the times there are sub-texts in the review body that describe certa...

Social network analysis has recently gained a lot of interest because of the advent and the increasing popularity of social media, such as blogs, social networking applications, micro-blogging, or customer review sites. In this environment, trust is becoming an essential quality among user interactions and the recommendation for useful content and...

Social network analysis has emerged as a key technique in modern sociology, but has recently gained a lot of interest in Web mining research, because of the advent and the increasing popularity of social media, such as blogs, social networks, micro-blogging, customer review sites etc. Such media often serve as platforms for information disseminatio...

The proliferation of blogs and social networks presents a new set of challenges and opportunities in the way information is searched and retrieved. Even though facts still play a very important role when information is sought on a topic, opinions have become increasingly important as well. Opinions expressed in blogs and social networks are playing...

Online social networking is deeply interleaved in today's lifestyle. People come together and build communities to share thoughts, offer suggestions, exchange information, ideas, and opinions. Moreover, social networks often serve as platforms for information dissemination and product placement or promotion through viral marketing. The success rate...

The Web is a continuously evolving environment, since its content is updated on a regular basis. As a result, the traditional usage-based approach to generate recommendations that takes as input the navigation paths recorded on the Web page level, is not as effective. Moreover, most of the content available online is either explicitly or implicitly...

The proliferation of blogs and social networks presents a new set of challenges and opportunities in the way information is searched and retrieved. Opinions expressed in blogs and social networks are playing an important role influencing everything from the products people buy to the presidential candidate they support. This demonstration presents...

Data Security is a major issue in any web-based application. There have been approaches to handle intruders in any system, however, these approaches are not fully trustable; evidently data is not totally protected. Real world databases have information that needs to be securely stored. The approach of generating negative database could help solve s...

Web2.0 has resulted in an increasing popularity of personalized recommender systems, especially in the context of social networking applications. Although there exist design approaches available for such systems, most of them make very explicit assumptions on the application domain as well as on the availability and data types to be used as input....

This demonstration presents QueRIE, a recommender system that supports interactive database exploration. This system aims at assisting non-expert users of scientific databases by generating personalized query recommendations. Drawing inspiration from Web recommender systems, QueRIE tracks the querying behavior of each user and identifies potentiall...

This demonstration presents QueRIE, a recommender system that supports interactive database exploration. This system aims at assisting non-expert users of scientific databases by tracking their querying behavior and generating personalized query recommendations. The system is supported by two recommendation engines and the underlying recommendation...

Social network analysis has recently gained a lot of interest because of the advent and the increasing popularity of social media, such as blogs, social networks, micro logging, or customer review sites. Such media often serve as platforms for information dissemination and product placement or promotion. In this environment, influence and trust are...

In this paper we present FGP, an algorithm that combines the powers of an association rule mining algorithm (FP-Growth) and a generalized pattern mining algorithm (GP-Close) in order to efficiently generate rules from transaction data. Our Frequent Generalized Pattern (FGP) algorithm considers that all items that appear in a set of transactions, be...

Relational database systems are becoming increasingly popular in the scientific community to support the interactive exploration of large volumes of data. In this scenario, users employ a query inter-face (typically, a web-based client) to issue a series of SQL queries that aim to analyze the data and mine it for interesting information. First-time...

Relational database systems are becoming increasingly pop- ular in the scientific community to support the interactive exploration of large volumes of data. In this scenario, users employ a query interface (typically, a web-based client) to issue a series of SQL queries that aim to analyze the data and mine it for interesting information. First-tim...

As the Web continuously grows, the results returned by search engines are too many to review. Lately, the prob- lem of personalizing the ranked result list based on user feedback has gained a lot of attention. Such approaches usually require a big amount of user feedback on the results, which is used as training data. In this work, we present a met...

In this paper we present FGP, an algorithm that combines the powers of an association rule mining algorithm (FP-Growth) and a generalized pattern mining algorithm (GP-Close) in order to efficiently generate rules from transaction data. Our Frequent Generalized Pattern (FGP) algorithm considers that all items that appear in a set of transactions, be...

The continuous growth in the size and use of the World Wide Web imposes new methods of design and development of on-line information services. The need for predicting the users' needs in order to improve the usability and user retention of a web site is more than evident and can be addressed by personalizing it. Recommendation algorithms aim at pro...

Topic directories are popular means of organizing informa- tion resources in the web. In this work, we introduce a methodology for personalizing topic directories. The key feature of our methodology is that the personalization is based on the mining of navigation patterns extracted from previous user visits. These patterns, expressed in the form of...

Recommendation algorithms aim at proposing "next" pages to a user based on her current visit and the past users' navigational patterns. In the vast majority of related algorithms, only the usage data are used to produce recommendations, whereas the structural properties of the Web graph are ignored. We claim that taking also into account the Web st...

Markov models have been widely used for modelling users' navigational behaviour in the Web graph, using the transitional probabilities between web pages, as recorded in the web logs. The recorded users' navigation is used to extract popular web paths and predict current users' next steps. Such purely usage-based probabilistic models, however, prese...

Web personalization is the process of customizing a web site to the needs of each specific user or set of users. Personalization of a web site may be performed by the provision of recommendations to the users, high- lighting/adding links, creation of index pages, etc. The web personalization sys- tems are mainly based on the exploitation of the nav...

We present SEWeP, a Web Personalization prototype system that integrates usage data with content semantics, expressed in taxonomy terms, in order to produce a broader yet semantically focused set of recommendations.

The amounts of information residing on web sites make users' navigation a hard task. To address this problem, web sites provide recommendations to the end users, based on similar users' navigational patterns mined from past visits. In this paper we introduce a recommendation method, which integrates usage data recorded in web logs, and the conceptu...

Web personalization is the process of customizing a Web site to the needs of specific users, taking advantage of the knowledge acquired from the analysis of the user’s navigational behavior (usage data) in correlation with other information collected in the Web context, namely, structure, content and user profile data. Due to the explosive growth o...

Web personalization is the process of customizing a web site to the needs of each specific user or set of users, taking advantage of the knowledge acquired through the analysis of the user's navigational behavior. The objective of the I-KnowUMine project (IKUM) is to develop an integrated platform (referred to in the paper as the "IKUM system") tha...

Web personalization is the process of customizing a Web site to the needs of each specific user or set of users, taking advantage of the knowledge acquired through the analysis of the user's navigational behavior. Integrating usage data with content, structure or user profile data enhances the results of the personalization process. In this paper,...

Recommendation algorithms aim at proposing "next" pages to a user based on her navigational behavior. In the vast majority of related algorithms, only the usage data are used to produce recommendations. We claim that taking also into account the web structure and using link analysis algorithms ameliorates the quality of recommendations. In this pap...

Web sites have become an increasingly important part of every country's information and cultural heritage. For this reason, Web archiving has become an issue for many national libraries. In this paper, we present a first attempt to archive the Greek Web. This project is divided in two parts; the first part concerns the collection of the majority of...