Claudio Biancalana

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.

Social Tagging in Query Expansion: A New Way for Personalized Web Search

Social networks and collaborative tagging systems are rapidly gaining popularity as primary means for sorting and sharing data: users tag their bookmarks in order to simplify information dissemination and later lookup. Social Bookmarking services are useful in two important respects: first, they can allow an individual to remember the visited URLs, and second, tags can be made by the community to guide users towards valuable content. In this paper we focus on the latter use: we present a novel approach for personalized web search using query expansion. We further extend the family of well-known co-occurence matrix technique models by using a new way of exploring social tagging services. Our approach shows its strength particularly in the case of disambiguation of word contexts. We show how to design and implement such a system in practice and conduct several experiments on a real web-dataset collected from Regione Lazio Portal1. To the best of our knowledge this is the first study centered on using social bookmarking and tagging techniques for personalization

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.

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.

Nereau : a social approach to query expansion

Classical query expansion techniques can be roughly divided into two groups: the statistical approach, which consists of the selection of top-ranked terms from relevant sources based on co-occurrence values, and the semantic approach, where candidate terms are chosen based on their meaning. In this paper we present a novel approach, in which the classical cooccurrence matrix is enhanced with metadata retrieved from social bookmarking services in order to overcome its lack of semantic attributes. The implemented system, named Nereau, combines methods from the areas of Information Retrieval and Social Network Analysis: given the original query, our system performs multiple expansions and presents results divided into categories. We use a new approach to web personalization based on user collaboration sharing of information about web documents. Our evaluation results are encouraging and suggest that personalization based on social bookmarking and tagging is a useful addition to web toolset and that the subject is worth further research, in particular with regard to increasing popularity of social and collaborative services in the WWW today.

Intelligent search on the internet

The Web has grown from a simple hypertext system for research labs to an ubiquitous information system including virtually all human knowledge, e.g., movies, images, music, documents, etc. The traditional browsing activity seems to be often inadequate to locate information satisfying the user needs. Even search engines, based on the Information Retrieval approach, with their huge indexes show many drawbacks, which force users to sift through long lists of results or reformulate queries several times. Recently, an important research activity effort has been focusing on this vast amount of machine-accessible knowledge and on how it can be exploited in order to match the user needs. The personalization and adaptation of the human-computer interaction in information seeking by means of machine learning techniques and in AI-based representations of the information help users to address the overload problem. This chapter illustrates the most important approaches proposed to personalize the access to information, in terms of gathering resources related to given topics of interest and ranking them as a function of the current user needs and activities, as well as examples of prototypes and Web systems.

Personalized Web Search Using Correlation Matrix for Query Expansion

In this work we present a comparing analysis of four Query Expansion (QE) techniques. Sharing the concept of term co-occurrence, we start from a simple system based on bigrams, then we moved onto a system based on term proximity through an approach known in the literature as Hyperspace Analogue to Language (HAL), and eventually developing a solution based on co-occurrence at page level.We have implemented the methods in a system prototype, which has been used to conduct several experiments that have produced interesting results.

Social Tagging for Personalized Web Search

Social networks and collaborative tagging systems are rapidly gaining popularity as primary means for sorting and sharing data: users tag their bookmarks in order to simplify information dissemination and later lookup. Social Bookmarking services are useful in two important respects: first, they can allow an individual to remember the visited URLs, and second, tags can be made by the community to guide users towards valuable content. In this paper we focus on the latter use: we present a novel approach for personalized web search using query expansion. We further extend the family of well-known co-occurence matrix technique models by using a new way of exploring social tagging services. Our approach shows its strength particularly in the case of disambiguation of word contexts. We show how to design and implement such a system in practice and conduct several experiments. To the best of our knowledge this is the first study centered on using social bookmarking and tagging techniques for personalization of web search and its evaluation in a real-world scenario.

Wavelet-based Music Recommendation

Recommender Systems provide suggestions for items (e.g., movies or songs) to be of use to a user. They must take into account information to deliver more useful (perceived) recommendations. Current music recommender takes an initial input of a song and plays music with similar characteristics, or music that other users have listened to along with the input song. Listening behaviors in terms of temporal information associated to ratings or playbacks are usually ignored. We propose a recommender that predicts the most rated songs that a given user is likely to play in the future analyzing and comparing user listening habits by means of signal processing techniques. Recommender systems provide suggestions based on user preferences in order to recommend items likely to be of interest to a user. It is obvious that user preferences are influenced by the current context, such as the current time of the day, mood, or current activities. Nevertheless, a few recommender systems explicitly include this information in the preference models. A special group of recommender systems are the ones based on the collaborative approach (Resnick et al., 1994; Shardanand and Maes, 1995; Breese et al., 1998). The system generates recommendations using only information about rating profiles for different users. Collaborative systems locate peer users with a rating history similar to the current user and generate recommendations using this neighborhood. Collaborative filtering (CF) systems have been successful in several recommender systems. The availability of large datasets and additional information that is easy collectable from the web, makes this task interesting. There are several issues that do not allow us to directly apply the traditional CF approach for music recommendation. The space of possible items (i.e., tracks) can be very large and, similarly, the user space can also be enormous. Often user ratings are not available or they cover only a small subset of the user library of songs. Moreover, when new users enter to the system or new songs are added to the global library, it is not possible to provide any recommendation to them due to the lack of any preference information (the so known cold-start problem). There is no chance to use taxonomies or ontologies to represent the new items and facilitate the clustering as happens in different domains (e.g., (Acampora et al., 2010a; Micarelli et al., 2009)) Content-based approaches collect information describing the items and then, based on the user preferences, they predict which tracks the user might enjoy (see for example the Pandora service1). The key component of this approach is the similarity function among the songs. Nevertheless, there is a strong limitation of the highlevel descriptors that can be automatically extracted from the tracks (Celma, 2010). One more relevant issue that traditional CF approaches do not take into consideration is the listening behavior of the user in terms of temporal information. The timestamp of an item (i.e., when the song song is played) is an important factor for the recommendation algorithm. Usually, the prediction function treats the older items as less relevant than the new ones, but any further reasoning about the temporal information is simply ignored. In this paper, we discuss a recommendation approach based on signal processing. In particular, a traditional CF approach is enhanced considering an improved similarity function between users. The user listening habits are represented by signals. Wavelet theory is used to study the related time-frequency representations of signals and draw similarity between listening behaviors. Signal processing techniques are not employed to extract features from the songs, but for representing and comparing those behaviors in or-