Title: AVATAR: A Flexible Approach to Improve the Personalized TV by Semantic Inference
Authors: Yolanda Blanco Fernández, Jose J. Pazos Arias, Alberto Gil Solla and Manuel Ramos Cabrer
Abstract: Both the TV recommender systems and search engines developed in the Internet are intended to lighten the user burden, by offering them automatically the required information, personalized according to their preferences or needs. In last years, with the goal of improving these search engines, an important research line has been developed in the context of the WWW, known as the Semantic Web. The Semantic Web describes the resources by metadata and reasons on them by discovering new knowledge. Taking the advantage of the Semantic Web in the field of the personalized TV, we propose an intelligent assistant named AVATAR, which uses the semantic inference as a novel recommendation strategy. This approach allows to overcome an important limitation identified in the personalization strategies adopted in other systems: an excessive similarity between the programs known by the user and those suggested by the recommender. In this regard, our approach diversifies and personalizes the elaborated recommendations, by inferring semantic associations of different nature between the user preferences and the suggested TV contents. This inference process requires a formal representation both the knowledge of our application domain, and the user preferences. In this regard, we resort to an OWL ontology to identify resources and relations typical in the TV field, and to reason about them.
Title: Identifying User and Group Information from Collaborative Filtering Data Sets
Authors: Josephine Griffith, Colm O'Riordan and Humphrey Sorensen
Abstract: This paper considers the information that can be captured about users and groups from a collaborative filtering data set with a view to creating user models and group models. The approach outlined defines a number of user and group features which are represented using a graph model where links exist between users and items, between users and users, and between items and items. The main focus of this paper is to extract implicit information about users and groups that exists in a collaborative filtering data set.
Title: Incorporating Context into Recommender Systems Using Multidimensional Rating Estimation Methods
Authors: Gediminas Adomavicius and Alexander Tuzhilin
Abstract: Traditionally recommendation technologies have been focusing on recommending items to users (or users to items) and typically do not consider additional contextual information, such as time or location. In this paper we discuss a multidimensional approach to recommender systems that supports ad-ditional dimensions capturing the context in which recommendations are made. One of the most important questions in recommender systems research is how to estimate unknown ratings, and in the paper we address this issue for the mul-tidimensional recommendation space. We present the classification of multidi-mensional rating estimation methods, discuss how to extend traditional two-dimensional recommendation approaches to the multidimensional space, and identify research directions for the multidimensional rating estimation problem.
Title: Personalizing the Search for Persons: A Recommender-based Approach
Authors: Tobias Keim, Jochen Malinowski, Gregor Heinrich, and Oliver Wendt
Abstract: Recommendation systems are widely used on the Internet to assist customers in finding the products or services that best fit their individual preferences. While current implementations successfully reduce information overload by generating personalized suggestions when searching for objects such as books or movies, recommendation systems so far cannot be found in another potential field of application: the personalized search for subjects such as business partners or employees. This is astonishing as (1) the number of CV-, assessment- and social network-data available on the Internet is growing and (2) the complexity and scope of selecting the right partner is much higher than when buying a book. We argue that recommendation systems personalizing the search for people need to be grounded on two pillars: unary attributes on the one hand and relational attributes on the other. We present a framework meeting these requirements together with an outline of a first prototypical implementation.
Title: Comparative evaluation of personalization algorithms for content recommendation
Authors: Carlos R. C. Alves and Lúcia V. L. Filgueiras
Abstract: Personalization techniques that combine user characteristics, user behavior, and content organization can be used to help users on finding objectively content on the web. The main contribution of this text is the multidisciplinary study that was conducted integrating different areas on human knowledge in order to find the best way to direct content, including some wide research on personalization concepts and applications. This study also presents the development of the Argo software which is formed by a web site, a component that captures and stores information about the user navigation, and three different personalization algorithms. Using navigation data it is possible to generate user profile, which is used to recommend content. Tests were conducted to check efficiency of the personalization algorithms.
Title: SMS Communication and Announcement Classification in Managed Learning Environments
Authors: Ross Clement, Mark Baldwin, Clive Vassell and Nadia Amin
Abstract: A prototype system for sending SMS text messages to students telling them about announcements has been designed and partially implemented. Experiments have been performed to test whether automatic text classification can be used to decide which announcements posted by tutors are urgent and that a SMS text message should be sent informing students. The accuracy of a naive Bayes classifier is not sufficient in itself to decide this, but a flexible classifier and the ability of tutors to override its decisions has promise. How the system would be used would depend on management policies concerning the effects of classification errors.
Title: User Profile Generation Based on a Memory Retrieval Theory
Authors: Fabio Gasparetti and Alessandro Micarelli
Abstract: Several statistical approaches for user profiling have been proposed in order to recognize users' information needs during their interaction with information sources. Human memory processes in terms of learning and retrieval are with no doubt some of the fundamental elements that take part during this interaction, but actually only a few models for user profiling have been devised considering explicitly these processes. For this reason, a new approach for user modeling is proposed and evaluated. The grounds in the well-studied Search of Associative Memory (SAM) model provides a clear definition of the structure to store information and the processes of learning and retrieval. These assets are missing in other works based for example on simplified versions of semantic memory models and co-occurrence data.
Title: Koba4MS: Knowledge-based Recommenders for Marketing and Sales
Authors: Alexander Felfernig
Abstract: Due to the increasing size and complexity of products offered by online stores and electronic marketplaces, the identification of solutions fitting to the wishes and needs of a customer is a challenging task. Customers can differ greatly in their expertise and level of knowledge w.r.t. the product domain which requires sales assistance systems allowing personalized dialogs, explanations and repair proposals in the case of inconsistent requirements. In this context, knowledge-based recommenders allow a flexible mapping of product, marketing and sales knowledge to the formal representation of a knowledge base. This paper presents the knowledge-based recommender environment Koba4MS which assists customers and sales representatives in the identification of appropriate solutions. Based on application examples from the domain of financial services, basic Koba4MS technologies are presented which support the effective implementation of customer-oriented sales dialogs.
Title: Electronic Programming Guide Recommender for Viewing on a Portable Device
Authors: Matthew Y. Ma, Jinhong K. Guo, Jingbo Zhu and Guiran Chang
Abstract: With the merge of DTV and the exponential growth of broadcasting network, an overwhelmingly amount of information have become available at views' homes. Therefore, it becomes increasingly challenging how consumers can receive the right amount of information at the right time for their entertainment needs. We proposed an electronic programming guide (EPG) recommender based on natural language processing techniques. Particularly, the recommender has been implemented as a service on a home network that facilitates the browsing and recommendation of TV programs on a portable remote device and such system is found to be feasible. Preliminary experiments have shown a precision of 81%.
Title: Generalizing e-Bay.NET: An Approach to Recommendation Based on Probabilistic Computing
Authors: Luis M. de Campos, Juan M. Fernández-Luna and Juan F. Huete
Abstract: In this paper, we shall present the theoretical developments related to extending existing e-Bay.NET recommendation system in order to improve its expressiveness. In particular, we shall make them more flexible and more general by enabling it to handle evidence items with a finer granularity so that more accurate information may be obtained when user preferences are elicited. The model is based on the formalism of Bayesian networks, and this extension requires the design of new methods to estimate conditional probability distributions and also a new algorithm to compute the posterior probabilities of relevance.
Title: Software Engineering Aspects of an Intelligent User Interface based on Multi Criteria Decision Making
Authors: Katerina Kabassi and Maria Virvou
Abstract: Decision making theories have been proved to be very successful for evaluating the users' interests and preferences in Intelligent User Interfaces (IUIs). However, their application and incorporation in the reasoning of an IUI requires empirical studies throughout the software life-cycle that lead to re-quirements analysis and specification, important design decisions and evalua-tion of the resulting IUI. This paper presents a life-cycle model of how a deci-sion making theory can be applied effectively in an IUI and gives detailed in-formation about the experiments conducted. More specifically, the Simple Ad-ditive Weighting (SAW) model has been used as a theory test bed and has been applied in an IUI that is called MBIFM. MBIFM is a file manipulation system that works in a similar way as Windows/NT Explorer. However, the system constantly reasons about every user's action and provides spontaneous advice, in case this is considered necessary.
Title: Trust Building in Recommender Agents
Authors: Li Chen and Pearl Pu
Abstract: Trust has long been regarded as an important factor influencing users' decision to buy a product in an online shop or to return to the shop for more product information. However, most notions of trust focus on the aspects of benevolence and integrity, and less on competence. Although benefits clearly exist for websites to employ competent recommender agents, the exact nature of these benefits to users' trusting intentions remains unclear. This paper presents some preliminary results of these issues based on a trust model that we have developed for recommender agents. We describe a carefully constructed survey in an attempt to reveal the relationship between users' perception of the agent's trustworthiness based on its competence and consumer trusting intentions, and more importantly, the role of explanation-based recommendation interfaces and their media format on trust promotion.
Title: Data Quality and Sparsity Issues in Collaborative Filtering on Web Logs
Authors: Miha Grcar, Dunja Mladenic and Marko Grobelnik
Abstract: In this paper, we present our experience in applying collaborative filtering to real-life corporate data in the light of data quality and sparsity. The quality of collaborative filtering recommendations is highly dependent on the quality of the data used to identify users' preferences. To understand the influence that highly sparse server-side collected data has on the accuracy of collaborative filtering, we ran a series of experiments in which we used publicly available datasets and, on the other hand, a real-life corporate dataset that does not fit the profile of ideal data for collaborative filtering. We have also experimentally compared two standard distance measures (Pearson correlation and Cosine similarity) used by k-Nearest Neighbor classifier, showing that depending on the dataset one outperforms the other - but no consistent difference can be claimed.