Comparison of group recommendation algorithms

Abstract

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.

Enhancing Recommender Systems for TV by Face Recognition

“…Different solutions to aggregate the preferences or recommendations of individuals and find the best items have been proposed (De Pessemier et al, 2014a). In our implementation, the average without misery strategy is used.…”

A user-centric evaluation of context-aware recommendations for a mobile news service

“…'Item' is the general term used to denote what the system recommends to users such as videos [14], songs [22], or events [12]. The suggestions provided are aimed at supporting their users in various decision-making processes, such as what movies to watch, what music to listen, which events to attend, what products to buy, or what news to read.…”

Development of a group recommender application in a Social Network

“…These systems commonly aggregate real or predicted ratings for group members [2,3,4,5]. The aggregation functions typically used are inspired by the social welfare functions developed Email addresses: lara.quijano@fdi.ucm.es (Lara Quijano-Sánchez), belend@sip.ucm.es (Belén Díaz-Agudo), jareciog@fdi.ucm.es (Juan A.…”

“…This evaluation will allow us to determine if HappyMovie is viable not only for giving good recommendations but also as a research tool that allows us to extract group related knowledge 5 .…”

“…The remainder of this paper is structured as follows: In the next section we introduce some of the state-ofthe-art knowledge regarding recommender systems and study other recommendation approaches whose main 5 As a future goal we would like to obtain a big dataset with all the knowledge that this application is capable of acquiring, in terms of trust levels, users' personality, users' preferences and final group decisions. With this dataset we could be able to provide to the recommenders community a public group dataset (we are not aware of any similar public dataset), run Big Data experiments or study group similarity composition and behaviour among others.…”