Alda Lopes Gançarski

A contextual-bandit algorithm for mobile context-aware recommender system

Most existing approaches in Mobile Context-Aware Recommender Systems focus on recommending relevant items to users taking into account contextual information, such as time, location, or social aspects. However, none of them has considered the problem of users content evolution. We introduce in this paper an algorithm that tackles this dynamicity. It is based on dynamic exploration/exploitation and can adaptively balance the two aspects by deciding which users situation is most relevant for exploration or exploitation. Within a deliberately designed offline simulation framework we conduct evaluations with real online event log data. The experimental results demonstrate that our algorithm outperforms surveyed algorithms.

Hybrid- ε -greedy for mobile context-aware recommender system

The wide development of mobile applications provides a considerable amount of data of all types. In this sense, Mobile Context-aware Recommender Systems (MCRS) suggest the user suitable information depending on her/his situation and interests. Our work consists in applying machine learning techniques and reasoning process in order to adapt dynamically the MCRS to the evolution of the users interest. To achieve this goal, we propose to combine bandit algorithm and case-based reasoning in order to define a contextual recommendation process based on different context dimensions (social, temporal and location). This paper describes our ongoing work on the implementation of a MCRS based on a hybrid-e -greedy algorithm. It also presents preliminary results by comparing the hybrid-e -greedy and the standard e -greedy algorithm.

Following the User's Interests in Mobile Context-Aware Recommender Systems: The Hybrid-e-greedy Algorithm

The wide development of mobile applications provides a considerable amount of data of all types (images, texts, sounds, videos, etc.). In this sense, Mobile Context-aware Recommender Systems (MCRS) suggest the user suitable information depending on her/his situation and interests. Two key questions have to be considered 1) how to recommend the user information that follows his/her interests evolution? 2)how to model the users situation and its related interests? Tithe best of our knowledge, no existing work proposing a MCRS tries to answer both questions as we do. This paper describes an ongoing work on the implementation of a MCRS based on the hybrid-å-greedy algorithm we propose, which combines the standard å-greedy algorithm and both content-based filtering and case-based reasoning techniques.

Exploration / exploitation trade-off in mobile context-aware recommender systems

The contextual bandit problem has been studied in the recommender system community, but without paying much attention to the contextual aspect of the recommendation. We introduce in this paper an algorithm that tackles this problem by modeling the Mobile Context-Aware Recommender Systems (MCRS) as a contextual bandit algorithm and it is based on dynamic exploration/exploitation. Within a deliberately designed offline simulation framework, we conduct extensive evaluations with real online event log data. The experimental results and detailed analysis demonstrate that our algorithm outperforms surveyed algorithms.

IRIS: A Novel Method of Direct Trust Computation for Generating Trusted Social Networks

Improving trust in social networks appears as the first step toward addressing the existing confidence and privacy concerns related to online social networks. Direct trust is used to develop different trust-based methods such as transitivity and access control, however how to compute direct trust levels is rarely discussed in the literature. To address some of the current limitations, we introduce a novel approach for generating trusted social networks and we compute trust levels between users having direct relationships. Experimental results with data extracted from FOAF files show that our work presents high accuracy.

Contextual Bandits for Context-Based Information Retrieval

Recently, researchers have started to model interactions between users and search engines as an online learning ranking. Such systems obtain feedback only on the few top-ranked documents results. To obtain feedbacks on other documents, the system has to explore the non-top-ranked documents that could lead to a better solution. However, the system also needs to ensure that the quality of result lists is high by exploiting what is already known. Clearly, this results in an exploration/exploitation dilemma. We introduce in this paper an algorithm that tackles this dilemma in Context-Based Information Retrieval CBIR area. It is based on dynamic exploration/exploitation and can adaptively balance the two aspects by deciding which users situation is most relevant for exploration or exploitation. Within a deliberately designed online framework we conduct evaluations with mobile users. The experimental results demonstrate that our algorithm outperforms surveyed algorithms.

Attribute grammar-based interactive system to retrieve information from XML documents

A system to interactively access extensible markup language documents aiming at information retrieval (IR) is described. The system has two main modules: the query editor/processor, where the user specifies his/her needs and the document analyser, which performs operations for query evaluation. The interactive construction of queries is based on the manipulation of intermediate results during query edition and evaluation. Queries are written in IXDIRQL, a query language that extends XPath with selection operations to extract the interesting subset of elements from intermediate results. This helps the user in building queries to retrieve the desired results. Moreover, textual similarity search of traditional IR is also possible in IXDIRQL, leading to a ranked list of elements. To support a syntax-directed edition of queries and its incremental evaluation during the iterative process, IXDIRQL is specified by an attribute grammar (AG). This formalisation enables the use of an automatic generator of the desired working environment. In this system, documents are also represented by AG. This representation uniformly defines structure, content and operations over documents; this allows for a better interoperability between components. The system has been used by real users to check its correct behaviour and the correct specification of queries, using selection operations.

Interactive information retrieval from XML documents represented by attribute grammars

In this paper, we describe a system to interactively accede to XML documents represented by attribute grammars. The system has two main components: (1) the query editor/processor, where the user interactively specifies his needs; (2) the document analyzer, which performs operations for query evaluation that accede directly to the documents. The interactive construction of queries is based on the manipulation of intermediate results during query construction and evaluation. We believe this helps the user to achieve the desired result.

An Agent-Based Service Architecture for User Profiles Dynamic Share

In this paper we describe an agent-based system for user profiles dynamic share. We address the problem of discovery and construction of distributed user profiles for adapted services recommendation in the context of pervasive applications. The system has to be open and proactive in order to dynamically propose adapted services according to users situation and contextual profile, while preserving his/her privacy. The objective of this work is to propose an infrastructure for developing pervasive learning environments to dynamically build the most suitable learner profile for a particular service or interaction in real-time. The implemented prototype is also presented, showing in detail how agents communicate.

Online Social Networks Analysis Visualization Using Socii

Nowadays we face an age of massive Internet usage. With Online Social Networks (OSN) we practically live this parallel reality where everything we do and everyone we met is exposed and shared through these online “worlds”. Today, being able to study and understand how information flows and how relationships are built within these online networks is of paramount importance for various reasons, social, educational, political or economical. That is why social networks analysis has become an important scientific and technological challenge. In this paper, we propose the Socii system for social networks analysis and visualization. Socii aims at helping OSNs users to exploit and understand their own networks through a user friendly interface. The system relies in four main principles, namely simplicity, accessibility, OSNs integration and contextual analysis. Socii’s architecture and implementation technological choices are presented, together with its main functionalities.