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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. |
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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. |
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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. |
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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. |
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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. |
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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. |
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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. |
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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.
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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%. |
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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. |
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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. |
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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. |
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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. |
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