Recommender systems form the backbone of many interactive systems. They incorporate user feedback to personalize the user experience typically via personalized recommendation lists. As users interact with a system, an increasing amount of data about a user's preferences becomes available, which can be leveraged for improving the systems' performance. Incorporating these new data into the underlying recommendation model is, however, not always straightforward. Many models used by recommender systems are computationally expensive and, therefore, have to perform offline computations to compile the recommendation lists. For interactive applications, it is desirable to be able to update the computed values as soon as new user interaction data is available: updating recommendations in interactive time using new feedback data leads to better accuracy and increases the attraction of the system to the users. Additionally, there is a growing consensus that accuracy alone is not enough and user satisfaction is also dependent on diverse recommendations. In this work, we tackle this problem of updating personalized recommendation lists for interactive applications in order to provide both accurate and diverse recommendations. To that end, we explore algorithms that exploit random walks as a sampling technique to obtain diverse recommendations without compromising on efficiency and accuracy. Specifically, we present a novel graph vertex ranking recommendation algorithm called RP3 that reranks items based on three-hop random walk transition probabilities. We show empirically that RP3 provides accurate recommendations with high long-tail item frequency at the top of the recommendation list. We also present approximate versions of RP3 and the two most accurate previously published vertex ranking algorithms based on random walk transition probabilities and show that these approximations converge with an increasing number of samples. To obtain interactively updatable recommendations, we additionally show how our algorithm can be extended for online updates at interactive speeds. The underlying random walk sampling technique makes it possible to perform the updates without having to recompute the values for the entire dataset. In an empirical evaluation with three real-world datasets, we show that RP3 provides highly accurate and diverse recommendations that can easily be updated with newly gathered information at interactive speeds ( 100ms). Recommender systems form the backbone of many interactive systems. They incorporate user feedback to personalize the user experience typically via personalized recommendation lists. As users interact with a system, an increasing amount of data about a user's preferences becomes available, which can be leveraged for improving the systems performance. Incorporating these new data into the underlying recommendation model is, however, not always straightforward. Many models used by recommender systems are computationally expensive and, therefore, have to perform offline computations to c...

User satisfaction is often dependent on providing accurate and diverse recommendations. In this paper, we explore algorithms that exploit random walks as a sampling technique to obtain diverse recommendations without compromising on efficiency and accuracy. Specifically, we present a novel graph vertex ranking recommendation algorithm called RP3 that re-ranks items based on 3-hop random walk transition probabilities. We show empirically, that RP3 provides accurate recommendations with high long-tail item frequency at the top of the recommendation list. We also present approximate versions of RP3 and the two most accurate previously published vertex ranking algorithms based on random walk transition probabilities and show that these approximations converge with increasing number of samples.