Personalized hybrid recommendation for group of users: Top-N multimedia recommender |
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| Institution: | 1. Universidad Autónoma de Madrid, Ciudad Universitaria de Cantoblanco. C/Iván Pavlov, s/n., 28049 Madrid, Spain\n;2. Universidad Nacional de Educación a Distancia, Juan del Rosal, nº 10. 28023, Spain;3. Semantia Lab, Bravo Murillo, 38. 28015, Madrid, Spain;1. College of Education Science and Technology, Zhejiang University of Technology, Hangzhou, 310023, China;2. College of Business and Administration, Zhejiang University of Technology, Hangzhou, 310023, China;3. College of Electrical and Information Engineering, Hunan University, Changsha, Hunan, 410082, China;1. Database Research Group, Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, University of Tehran, Iran\n;2. University of Wollongong, Dubai;1. Department of Computer Science and Software Engineering, International Islamic University, Sector H-10, Islamabad 44000, Pakistan;2. Department of Computer Science, Southern Illinois University, Carbondale, IL 62901, United States;1. CIBER Research Ltd., 1 Westwood Farmhouse, Greenham, Newbury RG14 7RU, United Kingdom;2. Innovation Value Institute, National University of Ireland Maynooth, Ireland\n;3. Center for Information and Communication Studies, University of Tennessee, 230 Communications and University Extension Building, 1345 Circle Park, Knoxville, TN 37996-0341, United States\n;4. School of Information Science, College of Communication and Information, University of Tennessee, 453 Communications Bldg., Knoxville, TN 37996-0341, United States\n;5. School of Communication Studies, College of Communication and Information, University of Tennessee, 293 Communications Bldg., Knoxville, TN 37996-0341, United States\n;6. School of Information Sciences, , College of Communication and Information, University of Tennessee, 1340 Circle Park Drive, 423 Communications Bldg, Knoxville, TN 37996-0341, United States\n |
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| Abstract: | 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. |
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