Giuseppe Sansonetti

An approach to social recommendation for context-aware mobile services

Nowadays, several location-based services (LBSs) allow their users to take advantage of information from the Web about points of interest (POIs) such as cultural events or restaurants. To the best of our knowledge, however, none of these provides information taking into account user preferences, or other elements, in addition to location, that contribute to define the context of use. The provided suggestions do not consider, for example, time, day of week, weather, user activity or means of transport. This article describes a social recommender system able to identify user preferences and information needs, thus suggesting personalized recommendations related to POIs in the surroundings of the users current location. The proposed approach achieves the following goals: (i) to supply, unlike the current LBSs, a methodology for identifying user preferences and needs to be used in the information filtering process; (ii) to exploit the ever-growing amount of information from social networking, user reviews, and local search Web sites; (iii) to establish procedures for defining the context of use to be employed in the recommendation of POIs with low effort. The flexibility of the architecture is such that our approach can be easily extended to any category of POI. Experimental tests carried out on real users enabled us to quantify the benefits of the proposed approach in terms of performance improvement.

Social semantic query expansion

Weak semantic techniques rely on the integration of Semantic Web techniques with social annotations and aim to embrace the strengths of both. In this article, we propose a novel weak semantic technique for query expansion. Traditional query expansion techniques are based on the computation of two-dimensional co-occurrence matrices. Our approach proposes the use of three-dimensional matrices, where the added dimension is represented by semantic classes (i.e., categories comprising all the terms that share a semantic property) related to the folksonomy extracted from social bookmarking services, such as delicious and StumbleUpon. The results of an indepth experimental evaluation performed on both artificial datasets and real users show that our approach outperforms traditional techniques, such as relevance feedback and personalized PageRank, so confirming the validity and usefulness of the categorization of the user needs and preferences in semantic classes. We also present the results of a questionnaire aimed to know the users opinion regarding the system. As one drawback of several query expansion techniques is their high computational costs, we also provide a complexity analysis of our system, in order to show its capability of operating in real time.

Context-aware movie recommendation based on signal processing and machine learning

Most of the existing recommendation engines do not take into consideration contextual information for suggesting interesting items to users. Features such as time, location, or weather, may affect the user preferences for a particular item. In this paper, we propose two different context-aware approaches for the movie recommendation task. The first is an hybrid recommender that assesses available contextual factors related to time in order to increase the performance of traditional CF approaches. The second approach aims at identifying users in a household that submitted a given rating. This latter approach is based on machine learning techniques, namely, neural networks and majority voting classifiers. The effectiveness of both the approaches has been experimentally validated using several evaluation metrics and a large dataset.

Signal-based user recommendation on twitter

In recent years, social networks have become one of the best ways to access information. The ease with which users connect to each other and the opportunity provided by Twitter and other social tools in order to follow person activities are increasing the use of such platforms for gathering information. The amount of available digital data is the core of the new challenges we now face. Social recommender systems can suggest both relevant content and users with common social interests. Our approach relies on a signal-based model, which explicitly includes a time dimension in the representation of the user interests. Specifically, this model takes advantage of a signal processing technique, namely, the wavelet transform, for defining an efficient pattern-based similarity function among users. Experimental comparisons with other approaches show the benefits of the proposed approach.

Enhancing traditional local search recommendations with context-awareness

Traditional desktop search paradigm often does not fit mobile contexts. Common mobile devices provide impoverished mechanisms for text entry and small screens are able to offer only a limited set of options, therefore the users are not usually able to specify their needs. On a different note, mobile technologies have become part of the everyday life as shown by the estimate of one billion of mobile broadband subscriptions in 2011. This paper describes an approach to make context-aware mobile interaction available in scenarios where users might be looking for categories of points of interest (POIs), such as cultural events and restaurants, through remote location-based services. Empirical evaluations shows how rich representations of user contexts has the chance to increase the relevance of the retrieved POIs.

iSCUR: Interest and Sentiment-Based Community Detection for User Recommendation on Twitter

The increasing popularity of social networks has encouraged a large number of significant research works on community detection and user recommendation. The idea behind this work is that taking into account peculiar users’ attitudes (i.e., sentiments, opinions or ways of thinking) toward their own interests can bring benefits in performing such tasks. In this paper we describe (i) a novel method to infer sentiment-based communities without the requirement of obtaining the whole social structure, and (ii) a community-based approach to user recommendation. We take advantage of the SVO (sentiment-volume-objectivity) user profiling and the Tanimoto similarity to evaluate user similarity for each topic. Afterwards we employ a clustering algorithm based on modularity optimization to find densely connected users and the Adamic-Adar tie strength to finally suggest the most relevant users to follow. Preliminary experimental results on Twitter reveal the benefits of our approach compared to some state-of-the-art user recommendation techniques.

Exploiting Web Browsing Activities for User Needs Identification

Browsing sessions are rich in elements useful to build profiles of user interests, but at the same time HTML pages include noise data, such as ads and navigation menus. Moreover, pages might cover several different topics. For these reasons they are often ignored in personalized approaches. We propose a novel approach for implicitly recognizing valuable text descriptions of current user needs based on the implicit feedback revealed through web browsing interactions.

Temporal people-to-people recommendation on social networks with sentiment-based matrix factorization

Abstract Nowadays, the exponential advancement of social networks is creating new application areas for recommender systems (RSs). People-to-people RSs aim to exploit user’s interests for suggesting relevant people to follow. However, traditional recommenders do not consider that people may share similar interests, but might have different feelings or opinions about them. In this paper, we propose a novel recommendation engine which relies on the identification of semantic attitudes, that is, sentiment, volume, and objectivity, extracted from user-generated content. In order to do this at large-scale on traditional social networks, we devise a three-dimensional matrix factorization, one for each attitude. Potential temporal alterations of users’ attitudes are also taken into consideration in the factorization model. Extensive offline experiments on different real world datasets, reveal the benefits of the proposed approach compared with some state-of-the-art techniques.

A Case-Based Approach to Indoor Navigation Using Sonar Maps

Abstract The aim of this paper is to propose an alternative to the traditional approaches that are applied to address the complex problems generated by indoor navigation using sonar maps. Essentially, we present an architecture based on a reasoning method that is known as Case-Based Reasoning in the Artificial Intelligence domain. The system we have developed is capable of analyzing the maps obtained from a robot’s ultrasonic sensors, of recognizing the represented object and consequently of making this information available for subsequent experiences. In this way, the robot acquires knowledge on a progressive basis and is therefore able to navigate autonomously in an environment of which it initially has no prior information.

A Social Context-Aware Recommender of Itineraries Between Relevant Points of Interest

In this paper, we present a personalized recommender system able to suggest to the target user itineraries that both meet her preferences and needs, and are sensitive to her physical and social contexts. The recommendation process takes into account different aspects: in addition to the popularity of the points of interest (POIs), inferred by considering, for instance, the number of check-ins on social networking services such as Foursquare, it also includes the user’s profile, the current context of use, and the user’s network of social ties. The system, therefore, consists of four main modules that accomplish the following tasks: (1) the construction of the user’s profile according to her interests and tastes; (2) the creation of the path graph in the user’s proximity; (3) the routing to locate the first k itineraries that match the query; (4) their ranking through a scoring function that considers the POI popularity, the user’s profile, and her physical and social context. The proposed system was evaluated on a sample of 40 real users. Experimental results showed the effectiveness of the proposed recommender.