Alfonso Miola

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.

Supporting Teachers to Retrieve and Select Learning Objects for Personalized Courses in the Moodle_LS Environment

In this paper we present a comprehensive framework supporting the tasks of defining, retrieving, and importing Learning Objects (LOs) for personalized courses. It is partially implemented in a Moodle-based personalization system, where the instructional designer is guided through: 1) a theoretical specification of the needed LOs; 2) a retrieval function of actual LOs, by automatically querying standard-compliant repositories; 3) an analysis of such items, to import those selected by him, also adding metadata relevant to the personalization system, at hand. This work overcomes some well known shortcomings of the Moodle system in supporting retrieval of learning material in a personalization context.

Advances in the Design of Symbolic Computation Systems

State of the art and motivations.- Mathematica: doing mathematics by computer?.- An overview of the TASSO project.- Mathematical objects.- The uniform representation of mathematical objects by truncated power series.- p-adic arithmetic: a tool for error-free computations.- Exact solution of computational problems via parallel truncated p-adic arithmetic.- A canonical form guide to symbolic summation.- Indexes in sums and series: from formal definition to object-oriented implementation.- Programming methodologies.- Equational specifications: design, implementation, and reasoning.- On the algebraic specification of classes and inheritance in object-oriented programming.- On subtyping in languages for symbolic computation systems.- Enhanced strict inheritance in TASSO-L.- Reasoning capabilities.- Deduction and abduction using a sequent calculus.- A sequent calculus machine for symbolic computation systems.- Automated deduction by connection method in an object-oriented environment.- A general reasoning apparatus for intelligent tutoring systems in mathematics.

The conversion of Hensel codes to their rational equivalents: or how to solve the Gregory's open problem

The problem of rational arithmetic has been studied recently by many authors. For instance we can mention the work of Horn [1978] and of Matula and Kornerup [1979].

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-

Deduction and abduction using a sequent calculus

A sequent calculus for automated reasoning is a particular sequent calculus that constitutes a single uniform method to perform different types of logical inferences in first order theories.

Constructive lifting in graded structures: A unified view of Buchberger and Hensel methods

This paper discusses a general lifting technique for solving polynomial equations in gradedstructures A, where the solution is understood to lie in the completion A^@^ of A. It shows that the classical Hensel lifting and the main constructions related to the Buchberger algorithm for Grobner bases are both instances of this technique. So, while the setting of it is too general to allow for an effective solution of equations, this technique stresses a theoretical relation between two basic algorithms in computer algebra and could be used as a theoretical model to attack computation problems under the same viewpoint.

A normalization algorithm for truncated p-adic arithmetic

This paper presents a new algorithmic approach to cope with the problems related to the generation and the manipulation of the pseudo-Hensel-codes in the p-adic arithmetic. After reviewing some classical properties and the results of the Hensel code arithmetic, a new algorithm to manipulate pseudo-Hensel-codes is presented, discussed and compared with two existing methods. The lower cost of the proposed new algorithm will result from the comparison.

p-adic arithmetic: a tool for error-free computations

In this paper we propose the use of the p-adic arithmetic as a basic computational tool for a symbolic computation system in the framework of the TASSO project. This arithmetic has been chosen for two main reasons.

A sequent calculus for automated reasoning in symbolic computation systems

Abstract In this paper the problem of reasoning on properties of mathematical objects is considered in the context of symbolic computation. Automated reasoning mechanisms are proposed as a new basic computing tool in a symbolic computation system. These mechanisms are aimed to support the semantical correctness of a computation by allowing for verification of properties of mathematical objects introduced in the system and for generation and abduction of new properties of mathematical objects resulting from computations. The main objective of this paper is to define an extended sequent calculus to deal with generative and abductive logic problems, as well as with verificative problems, within a single methodological and computational environment. The implementation aspects of the proposed automated reasoning apparatus are also discussed. Examples of execution are presented and possible further applications are hinted.