Verus Pronk

Incorporating user control into recommender systems based on naive bayesian classification

Recommender systems are increasingly being employed to personalize services, such as on the web, but also in electronics devices, such as personal video recorders. These recommenders learn a user profile, based on rating feedback from the user on, e.g., books, songs, or TV programs, and use machine learning techniques to infer the ratings of new items. The techniques commonly used are collaborative filtering and naive Bayesian classification, and they are known to have several problems, in particular the cold-start problem and its slow adaptivity to changing user preferences. These problems can be mitigated by allowing the user to set up or manipulate his profile. In this paper, we propose an extension to the naive Bayesian classifier that enhances user control. We do this by maintaining and flexibly integrating two profiles for a user, one learned by rating feedback, and one created by the user. We in particular show how the cold-start problem is mitigated.

Enhanced semantic TV-show representation for personalized electronic program guides

Personalized electronic program guides help users overcome information overload in the TV and video domain by exploiting recommender systems that automatically compile lists of novel and diverse video assets, based on implicitly or explicitly defined user preferences. In this context, we assume that user preferences can be specified by program genres (documentary, sports, …) and that an asset can be labeled by one or more program genres, thus allowing an initial and coarse preselection of potentially interesting assets. As these assets may come from various sources, program genre labels may not be consistent among these sources, or not even be given at all, while we assume that each asset has a possibly short textual description. In this paper, we tackle this problem by considering whether those textual descriptions can be effectively used to automatically retrieve the most related TV shows for a specific program genre. More specifically, we compare a statistical approach called logistic regression with an enhanced version of the commonly used vector space model, called random indexing, where the latter is extended by means of a negation operator based on quantum logic. We also apply a new feature generation technique based on explicit semantic analysis for enriching the textual description associated to a TV show with additional features extracted from Wikipedia.

Comparing disk scheduling algorithms for VBR data streams

We compare a number of disk scheduling algorithms that can be used in a multimedia server for sustaining multiple variable-bit-rate (VBR) data streams. A data stream is sustained by repeatedly fetching a block of data from disk and storing it in a corresponding buffer. For each of the disk scheduling algorithms we give necessary and sufficient conditions for avoiding underflow and overflow of the buffers. In addition, the algorithms are compared with respect to buffer requirements as well as average response times.

Disk Scheduling for Variable-Rate Data Streams

We describe three disk scheduling algorithms that can be used in a multimedia server for sustaining a number of heterogeneous variable-rate data streams. A data stream is supported by repeatedly fetching a block of data from the storage device and storing it in a corresponding buffer. For each of the disk scheduling algorithms we give necessary and sufficient conditions for avoiding under- and overflow of the buffers. In addition, the algorithms are compared with respect to buffer requirements as well as average response times.

Object Detection in Flatland

Given a rectangle with emitters and receivers on its perimeter, one can detect objects in it by determining which of the line segments between emitters and receivers are blocked by objects. The problem of object detection can be formulated as the problem of finding all non-empty n-wedge intersections, where a wedge is defined by a consecutive set of blocked line segments from the same emitter. We show that for a given set of wedges, one emanating from each emitter, we can determine the intersection (i.e., the convex polygon) in time linear in the number of wedges, assuming some given ordering of the wedges. We present two algorithms that efficiently determine all non-empty n-wedge intersections, assuming that objects are sufficiently large.

Personalized implicit learning in a music recommender system

Recommender systems typically require feedback from the user to learn the users taste This feedback can come in two forms: explicit and implicit Explicit feedback consists of ratings provided by the user for a number of items, while implicit feedback comes from observing user actions on items These actions have to be interpreted by the recommender system and translated into a rating In this paper we propose a method to learn how to translate user actions on items to ratings on these items by correlating user actions with explicit feedback We do this by associating user actions to rated items and subsequently applying naive Bayesian classification to rate new items with which the user has interacted We apply and evaluate our method on data from a web-based music service and we show its potential as an addition to explicit rating.

Compact disc standards: an introductory overview

The success of the compact disc (CD) as a storage medium for digital audio has, over the last ten years, resulted in a number of initiatives to use the CD for other applications as well, e.g., as read-only memory for computers, as a storage medium for audio-visual material for multimedia applications, and as a storage medium for photographs. Each of these applications poses additional requirements on how the corresponding information is stored and retrieved, resulting in a range of different CD standards. The functional specifications of these standards are each given a specific color for ease of reference: the Red Book for CD-DA, the Yellow Book for CD-ROM, the Green Book for CD-I, etc. This paper aims at giving an overview of the various CD standards by explaining what is specified in each of the colored books and by indicating how they relate to one another.

Incorporating confidence in a naive bayesian classifier

Naive Bayes is a relatively simple classification method to, e.g., rate TV programs as interesting or uninteresting to a user. In case the training set consists of instances, chosen randomly from the instance space, the posterior probability estimates are random variables. Their statistical properties can be used to calculate confidence intervals around them, enabling more refined classification strategies than the usual argmax-operator. This may alleviate the cold-start problem and provide additional feedback to the user. In this paper, we give an explicit expression to estimate the variances of the posterior probability estimates from the training data and investigate the strategy that refrains from classification in case the confidence interval around the largest posterior probability overlaps with any of the other intervals. We show that the classification error rate can be significantly reduced at the cost of a lower coverage, i.e., the fraction of classifiable instances, in a TV-program recommender.

Multimedia Storage and Retrieval: An Algorithmic Approach

Preface.PART I: PRELIMINARIES.1. Introduction.2. Modeling Server and Streams.PART II: DISK SCHEDULING.3. Serving a Single CBR Stream.4. Serving Multiple CBR Streams.5. Serving Multiple VBR Streams.PART III: STORAGE ON A SINGLE DISK.6. File Allocation Strategies.7. Using a Multi-Zone Disk.PART IV: STORAGE ON MULTIPLE DISKS.8. Striping.9. Random Redundant Storage.PART V: DATA TRANSMISSION.10. Bit-Rate Smoothing Algorithms.11. Near Video-on-Demand Strategies.Bibliography.Author Index.Subject Index.

Traffic Models for Telecommunication

Models that describe the traffic on the current (broadband-)integrated-services digital networks are a hot topic in telecommunication. They are relevant for at least the following two reasons: traffic description: It is assumed (at least for some types of networks) that potential users will have to give a traffic description. This will enable the network operator to decide whether the new connection can be admitted to the network without violating the quality of service guarantees of existing connections, i.e. without overloading the network; — network simulation: with the aim to properly dimension future networks; network simulation: with the aim to properly dimension future networks.