A recommender system aims to provide users with personalized online product or service recommendations to handle the increasing online information overload problem and improve customer relationship management. Various recommender system techniques have been proposed since the mid-1990s, and many sorts of recommender system software have been developed recently for a variety of applications. Researchers and managers recognize that recommender systems offer great opportunities and challenges for business, government, education, and other domains, with more recent successful developments of recommender systems for real-world applications becoming apparent. It is thus vital that a high quality, instructive review of current trends should be conducted, not only of the theoretical research results but more importantly of the practical developments in recommender systems. This paper therefore reviews up-to-date application developments of recommender systems, clusters their applications into eight main categories: e-government, e-business, e-commerce/e-shopping, e-library, e-learning, e-tourism, e-resource services and e-group activities, and summarizes the related recommendation techniques used in each category. It systematically examines the reported recommender systems through four dimensions: recommendation methods (such as CF), recommender systems software (such as BizSeeker), real-world application domains (such as e-business) and application platforms (such as mobile-based platforms). Some significant new topics are identified and listed as new directions. By providing a state-of-the-art knowledge, this survey will directly support researchers and practical professionals in their understanding of developments in recommender system applications. Research papers on various recommender system applications are summarized.The recommender systems are examined systematically through four dimensions.The recommender system applications are classified into eight categories.Related recommendation techniques in each category are identified.Several new recommendation techniques and application areas are uncovered.

Recommender system application developments

Novel group hybrid method combining collaborative and content-based recommendation.Proposed method improves the quality of recommended items ordering.Proposed method increases the recommendation precision for very Top-N results.Applicable for single user as well as group recommendation. Nowadays, the increasing demand for group recommendations can be observed. In this paper we address the problem of recommendation performance for groups of users (group recommendation). We focus on the performance of very Top-N recommendations, which are important when recommending the long lasting items (only a few such items are consumed per session, e.g. movie). To improve existing group recommenders we propose a mixed hybrid recommender for groups combining content-based and collaborative strategies. The principle of proposed group recommender is to generate content and collaborative recommendations for each user, apply an aggregation strategy to solve the group conflict preferences for the content and collaborative sets separately, and finally reorder the collaborative candidates based on the content-based ones. It is based on an idea that candidates recommended by both recommendation strategies at the same time are presumably more appropriate for the group than the candidates recommended by individual strategies. The evaluation is performed by several experiments in the multimedia domain (as typical representative for group recommendations). Both, online and offline experiments were performed in order to compare real users' satisfaction to the standard group recommenders and also, to compare performance of proposed approach to the state-of-the-art recommenders based on the MovieLens dataset. Finally, we experimented with the proposed hybrid recommender to generate the recommendation for a group of size one (i.e. single user recommendation). Obtained results, support our hypothesis that proposed mixed hybrid approach improves the precision of the recommendation for groups of users and for the single-user recommendation respectively on very Top-N recommended items.

Personalized hybrid recommendation for group of users

Enhancing memory-based collaborative filtering techniques for group recommender systems by resolving the data sparsity problem.Comparing the proposed method's accuracy with basic memory-based techniques and latent factor model.Makeing accurate predictions for unknown ratings in sparse matrices based on the proposed method.More users are satisfied of the group recommender system's performance. Memory-based collaborating filtering techniques are widely used in recommender systems. They are based on full initial ratings in a user-item matrix. However, most of the time in group recommender systems, this matrix is sparse and users' preferences are unknown. This deficiency may make memory-based collaborative filtering unsuitable for group recommender systems. This paper, improves memory-based techniques for group recommendation systems by resolving the data sparsity problem. The core of the proposed method is based on a support vector machine learning model that computes similarities between items. This method employs calculated similarities and enhances basic memory-based techniques. Experiments demonstrate that the proposed method overcomes the memory-based techniques. It also indicates that the presented work outperforms the latent factor approach, which is very efficient in sparse conditions. Finally, it is indicated that the proposed method gives a better performance than existing approaches on generating group recommendations.

Enhancing memory-based collaborative filtering for group recommender systems

Media content voting, ranking, and playing system

Because hedonic products consist predominantly of experience attributes, often with many available alternatives, choosing the “right” one is a demanding task for consumers. Decision making becomes even more difficult when a group, instead of an individual consumer, will consume the product, as is regularly the case for hedonic offerings such as movies, opera performances, and wine. Noting the prevalence of automated recommender systems as decision aids, the authors investigate the power of group recommender systems that consider the preferences of all group members. The authors develop a conceptual framework of the effects of group recommenders and empirically examine these effects through two choice experiments. They find that automated group recommenders offer more valuable information than single recommenders when the choice agent must consume the recommended alternative. However, when agents choose freely among alternatives, the group's social relationship quality determines whether group rec...

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Can Automated Group Recommender Systems Help Consumers Make Better Choices

Nowadays, technology allows for a better understanding of user needs through system design (recommender system) methodologies that position the individual at the center of all his actions. In this paper we start by reviewing the state of the art in both individual and group recommender systems technologies. On this ground we cluster the main characteristics of recommender systems with respect to the tasks they perform, the methods they employ and the issues they address. The other theoretical part we rely on is derived from social choice theory and voting. The main objective of this paper is to highlight the role of voting in group recommender systems, more precisely discussing several voting methods together with their characteristics. Our main contributions focus on: reviewing the state of the art literature related to voting in GRS, proposing an innovative and transparent voting mechanism and highlighting the current development of our music recommender system, GroupFun.

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Group recommender systems as a voting problem

Satisfaction and enjoyment are essential in group entertaining domains in which individuals share their preferences and actively participate in group decisions. Group recommender systems (GRS) do not yet employ methods and features allowing users to discover others' interests in an enjoyable fashion. Based on an in-depth user study and a user-centered design approach, we created GroupFun, a collaborative environment that help groups of friends' arrive at a common decision fostering group enjoyment and offering them a unique, fun music experience. We also conducted a user evaluation consisting in: system usage, questionnaires and open interviews to collect user feedback about our algorithms and interaction. Our results present GroupFun as an enjoyable and entertaining group decision platform which highly motivates users.

What's the best music you have?: designing music recommendation for group enjoyment in groupfun

In this paper we present the implementation of a truthful preference elicitation algorithm that favors item diversity in our group music recommender system, GroupFun. First, we discuss our methodology and relate to other group decision mechanisms already implemented in state of the art group recommender systems. Then, we formalize the implementation of the probabilistic weighted sum algorithmic in GroupFun. In addition to this we discuss user strategies and incentives for submitting ratings and show how PWS favors music diversity maximizing the group’s overall satisfaction. Due to space constraints in the following we highlight only the main characteristics of our system presenting its design, interfaces and user actions.

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Designing a Voting Mechanism in the GroupFun Music Recommender System

Nowadays online group activities are emerging, as individuals share their preferences, collaborate, discover and interact with their friends and family. Group recommender systems (GRS) use various social resources to make recommendations of items or activities that users are most likely to consume or agree upon. Thus, aggregating preference and recommending a common set of items for a group has become a challenging topic in online systems providing group suggestions and social websites. This issue is mainly concerned with the following three subjects: eliciting individual users’ preferences, suggesting the maximized overall satisfaction outcome for all users and ensuring that the aggregation mechanism is resistant to individual users’ manipulation. Furthermore, both individual and group preferences change over time. In order to track all of these changes GRS need to benefit from user interaction. This paper aims to present an innovative algorithm, which adapts to individual preference dynamics for group and social recommender systems. Individuals choose their desired items with the purpose of maximizing the entire group’s satisfaction.

George Popescu

Papers

Group recommender systems as a voting problem

What's the best music you have?: designing music recommendation for group enjoyment in groupfun

Designing a Voting Mechanism in the GroupFun Music Recommender System

Adaptive Voting Algorithms for Group and Social Recommender Systems