Trust based collaborative filtering

k-nearest neighbour (kNN) collaborative filtering (CF), the widely successful algorithm supporting recommender systems, attempts to relieve the problem of information overload by generating predicted ratings for items users have not expressed their opinions about; to do so, each predicted rating is computed based on ratings given by like-minded individuals. Like-mindedness, or similarity-based recommendation, is the cause of a variety of problems that plague recommender systems. An alternative view of the problem, based on trust, offers the potential to address many of the previous limiations in CF. In this work we present a varation of kNN, the trusted k-nearest recommenders (or kNR) algorithm, which allows users to learn who and how much to trust one another by evaluating the utility of the rating information they receive. This method redefines the way CF is performed, and while avoiding some of the pitfalls that similarity-based CF is prone to, outperforms the basic similarity-based methods in terms of prediction accuracy

Graph Convolutional Matrix Completion

We consider matrix completion for recommender systems from the point of view of link prediction on graphs. Interaction data such as movie ratings can be represented by a bipartite user-item graph with labeled edges denoting observed ratings. Building on recent progress in deep learning on graph-structured data, we propose a graph auto-encoder framework based on differentiable message passing on the bipartite interaction graph. Our model shows competitive performance on standard collaborative filtering benchmarks. In settings where complimentary feature information or structured data such as a social network is available, our framework outperforms recent state-of-the-art methods.Comment: 9 pages, 3 figures, updated with additional experimental evaluatio

Assessing the Quality and Stability of Recommender Systems

Recommender systems help users to find products they may like when lacking personal experience or facing an overwhelmingly large set of items. However, assessing the quality and stability of recommender systems can present challenges for developers. First, traditional accuracy metrics, such as precision and recall, for validating the quality of recommendations, offer only a coarse, one-dimensional view of the system performance. Second, assessing the stability of a recommender systems requires generating new data and retraining a system, which is expensive. In this work, we present two new approaches for assessing the quality and stability of recommender systems to address these challenges. We first present a general and extensible approach for assessing the quality of the behavior of a recommender system using logical property templates. The approach is general in that it defines recommendation systems in terms of sets of rankings, ratings, users, and items on which property templates are defined. It is extensible in that these property templates define a space of properties that can be instantiated and parameterized to characterize a recommendation system. We study the application of the approach to several recommendation systems. Our findings demonstrate the potential of these properties, illustrating the insights they can provide about the different algorithms and evolving datasets. We also present an approach for influence-guided fuzz testing of recommender system stability. We infer influence models for aspects of a dataset, such as users or items, from the recommendations produced by a recommender system and its training data. We define dataset fuzzing heuristics that use these influence models for generating modifications to an original dataset and we present a test oracle based on a threshold of acceptable instability. We implement our approach and evaluate it on several recommender algorithms using the MovieLens dataset and we find that influence-guided fuzzing can effectively find small sets of modifications that cause significantly more instability than random approaches. Adviser: Sebastian Elbau

Towards Collaborative Travel Recommender Systems

Collaborative filtering (CF) based recommender systems have been proven to be a promising solution to the problem of information overload. Such systems provide personalized recommendations to users based on their previously expressed preferences and that of other similar users. In the past decade, they have been successfully applied in various domains, such as the recommendation of books and movies, where items are simple, independent and single units. When applied in the tourism domain, however, CF falls short due to the simplicity of existing techniques and complexity of tourism products. In view of this, a study was carried out to review the research problems and opportunities. This paper details the results of the study, which includes a review on the recent developments in CF as well as recommender systems in tourism, and suggests future research directions for personalized recommendation of tourist destinations and products

Synthetic sequence generator for recommender systems - memory biased random walk on sequence multilayer network

Personalized recommender systems rely on each user's personal usage data in the system, in order to assist in decision making. However, privacy policies protecting users' rights prevent these highly personal data from being publicly available to a wider researcher audience. In this work, we propose a memory biased random walk model on multilayer sequence network, as a generator of synthetic sequential data for recommender systems. We demonstrate the applicability of the synthetic data in training recommender system models for cases when privacy policies restrict clickstream publishing.Comment: The new updated version of the pape