An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Recommender systems in e-commerce
Proceedings of the 1st ACM conference on Electronic commerce
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Incorporating contextual information in recommender systems using a multidimensional approach
ACM Transactions on Information Systems (TOIS)
Unifying user-based and item-based collaborative filtering approaches by similarity fusion
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Structure and evolution of online social networks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
The effect of correlation coefficients on communities of recommenders
Proceedings of the 2008 ACM symposium on Applied computing
Identifying user behavior in online social networks
Proceedings of the 1st Workshop on Social Network Systems
Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Content-based recommendation systems
The adaptive web
Movie recommendations based in explicit and implicit features extracted from the Filmtipset dataset
Proceedings of the Workshop on Context-Aware Movie Recommendation
Dependable filtering: Philosophy and realizations
ACM Transactions on Information Systems (TOIS)
A recommender system based on tag and time information for social tagging systems
Expert Systems with Applications: An International Journal
Precision-oriented evaluation of recommender systems: an algorithmic comparison
Proceedings of the fifth ACM conference on Recommender systems
TOAST: a topic-oriented tag-based recommender system
WISE'11 Proceedings of the 12th international conference on Web information system engineering
When power users attack: assessing impacts in collaborative recommender systems
Proceedings of the 7th ACM conference on Recommender systems
Accuracy and robustness impacts of power user attacks on collaborative recommender systems
Proceedings of the 7th ACM conference on Recommender systems
Hi-index | 0.00 |
Recommender systems, based on collaborative filtering, draw their strength from techniques that manipulate a set of user-rating profiles in order to compute predicted ratings of unrated items. There are a wide range of techniques that can be applied to this problem; however, the k-nearest neighbour (kNN) algorithm has become the dominant method used in this context. Much research to date has focused on improving the performance of this algorithm, without considering the properties that emerge from manipulating the user data in this way. In order to understand the effect of kNN on a user-rating dataset, the algorithm can be viewed as a process that generates a graph, where nodes are users and edges connect similar users: the algorithm generates an implicit social network amongst the system subscribers. Temporal updates of the recommender system will impose changes on the graph. In this work we analyse user-user kNN graphs from a temporal perspective, retrieving characteristics such as dataset growth, the evolution of similarity between pairs of users, the volatility of user neighbourhoods over time, and emergent properties of the entire graph as the algorithm parameters change. These insights explain why certain kNN parameters and similarity measures outperform others, and show that there is a surprising degree of structural similarity between these graphs and explicit user social networks.