Abstract
Group Recommender System is one of the categories of recommender system, where the recommendation of things is for a group of users rather than for any individual. These system combines the preferences of each user present in the group and then predicts things which are suitable for the users of the group. Various grouping strategies are available, which are used to generate to group recommendations, but most of them are suitable when used for specific purpose only. In this paper we have proposed a novel approach to group recommender system using collaborative filtering technique, which can be applicable to all the real world scenarios where the data set uses rating system to distinguish among users’ preferences. We have made use of nearest neighbor algorithm to create a group of users with similar likeness. We have also applied the priority among users of the group as there are some members whose preferences might affect the whole group. We have validated our results with the movie lens data set which is the standard data set for recommender system testing.
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Pujahari, A., Padmanabhan, V., Patel, S. (2016). Nearest Neighbour with Priority Based Recommendation Approach to Group Recommender System. In: Behera, H., Mohapatra, D. (eds) Computational Intelligence in Data Mining—Volume 2. Advances in Intelligent Systems and Computing, vol 411. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2731-1_32
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DOI: https://doi.org/10.1007/978-81-322-2731-1_32
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