17,485 research outputs found
From Group Recommendations to Group Formation
There has been significant recent interest in the area of group
recommendations, where, given groups of users of a recommender system, one
wants to recommend top-k items to a group that maximize the satisfaction of the
group members, according to a chosen semantics of group satisfaction. Examples
semantics of satisfaction of a recommended itemset to a group include the
so-called least misery (LM) and aggregate voting (AV). We consider the
complementary problem of how to form groups such that the users in the formed
groups are most satisfied with the suggested top-k recommendations. We assume
that the recommendations will be generated according to one of the two group
recommendation semantics - LM or AV. Rather than assuming groups are given, or
rely on ad hoc group formation dynamics, our framework allows a strategic
approach for forming groups of users in order to maximize satisfaction. We show
that the problem is NP-hard to solve optimally under both semantics.
Furthermore, we develop two efficient algorithms for group formation under LM
and show that they achieve bounded absolute error. We develop efficient
heuristic algorithms for group formation under AV. We validate our results and
demonstrate the scalability and effectiveness of our group formation algorithms
on two large real data sets.Comment: 14 pages, 22 figure
Comparison of group recommendation algorithms
In recent years recommender systems have become the common tool to handle the information overload problem of educational and informative web sites, content delivery systems, and online shops. Although most recommender systems make suggestions for individual users, in many circumstances the selected items (e.g., movies) are not intended for personal usage but rather for consumption in groups. This paper investigates how effective group recommendations for movies can be generated by combining the group members' preferences (as expressed by ratings) or by combining the group members' recommendations. These two grouping strategies, which convert traditional recommendation algorithms into group recommendation algorithms, are combined with five commonly used recommendation algorithms to calculate group recommendations for different group compositions. The group recommendations are not only assessed in terms of accuracy, but also in terms of other qualitative aspects that are important for users such as diversity, coverage, and serendipity. In addition, the paper discusses the influence of the size and composition of the group on the quality of the recommendations. The results show that the grouping strategy which produces the most accurate results depends on the algorithm that is used for generating individual recommendations. Therefore, the paper proposes a combination of grouping strategies which outperforms each individual strategy in terms of accuracy. Besides, the results show that the accuracy of the group recommendations increases as the similarity between members of the group increases. Also the diversity, coverage, and serendipity of the group recommendations are to a large extent dependent on the used grouping strategy and recommendation algorithm. Consequently for (commercial) group recommender systems, the grouping strategy and algorithm have to be chosen carefully in order to optimize the desired quality metrics of the group recommendations. The conclusions of this paper can be used as guidelines for this selection process
Advanced recommendations in a mobile tourist information system
An advanced tourist information provider system delivers information regarding sights and events on their users' travel route. In order to give sophisticated personalized information about tourist attractions to their users, the system is required to consider base data which are user preferences defined in their user profiles, user context, sights context, user travel history as well as their feedback given to the sighs they have visited. In
addition to sights information, recommendation on sights to the user could also be provided. This project concentrates on combinations of knowledge on recommendation systems and base information given by the users to build a recommendation component in the Tourist Information Provider or TIP system. To accomplish our goal, we not only examine several tourist information systems but also conduct the investigation on recommendation systems. We propose a number of approaches for advanced recommendation models in a tourist information system and select a subset of these for implementation to prove the concept
Graph-RAT: Combining data sources in music recommendation systems
The complexity of music recommendation systems has increased rapidly in recent years, drawing upon different sources of information: content analysis, web-mining, social tagging, etc. Unfortunately, the tools to scientifically evaluate such integrated systems are not readily available; nor are the base algorithms available. This article describes Graph-RAT (Graph-based Relational Analysis Toolkit), an open source toolkit that provides a framework for developing and evaluating novel hybrid systems. While this toolkit is designed for music recommendation, it has applications outside its discipline as well. An experimentâindicative of the sort of procedure that can be configured using the toolkitâis provided to illustrate its usefulness
Learning to rank music tracks using triplet loss
Most music streaming services rely on automatic recommendation algorithms to
exploit their large music catalogs. These algorithms aim at retrieving a ranked
list of music tracks based on their similarity with a target music track. In
this work, we propose a method for direct recommendation based on the audio
content without explicitly tagging the music tracks. To that aim, we propose
several strategies to perform triplet mining from ranked lists. We train a
Convolutional Neural Network to learn the similarity via triplet loss. These
different strategies are compared and validated on a large-scale experiment
against an auto-tagging based approach. The results obtained highlight the
efficiency of our system, especially when associated with an Auto-pooling
layer
Enhancing Web-Based Configuration with Recommendations and Cluster-Based Help
In a collaborative project with Tacton AB, we have investigated new ways of assisting the user in the process of on-line product configuration. A web-based prototype, RIND, was built for ephemeral users in the domain of PC configuration
The contribution of data mining to information science
The information explosion is a serious challenge for current information institutions. On the other hand, data mining, which is the search for valuable information in large volumes of data, is one of the solutions to face this challenge. In the past several years, data mining has made a significant contribution to the field of information science. This paper examines the impact of data mining by reviewing existing applications, including personalized environments, electronic commerce, and search engines. For these three types of application, how data mining can enhance their functions is discussed. The reader of this paper is expected to get an overview of the state of the art research associated with these applications. Furthermore, we identify the limitations of current work and raise several directions for future research
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