Towards Scaling Fully Personalized PageRank: Algorithms, Lower Bounds, and Experiments

Fogaras, Dániel; Rácz, Balázs; Csalogány, Károly; Sarlós, Tamás

doi:10.1080/15427951.2005.10129104

Cited by 251 publications

(259 citation statements)

References 32 publications

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“…The node-based personalization problem as well as a corresponding linearity theorem has been studied in [2], [7], [8], [9], [10]. The approach in [8] is based on a linearity theorem that is used to combine multiple personalized PageRank vectors.…”

Section: Approximation Methodsmentioning

confidence: 99%

“…They also presented efficient dynamic programming algorithms. In [7], an algorithm that simulates random walks is used to pre-compute an index database of personalized PageRank vectors (fingerprints). [2], [9] consider using a personalized base set on entity-relationship graphs.…”

Section: Approximation Methodsmentioning

confidence: 99%

“…Authority flow techniques typically require dozens of iteration across the data graph. Previous work [2], [7], [8], [9], [10], [11], [12] has addressed the performance of the node-based personalization approach. There is no work to facilitate efficient computation of edge-based personalization.…”

Section: Introductionmentioning

confidence: 99%

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Efficient Ranking on Entity Graphs with Personalized Relationships

Hristidis

Raschid

2014

IEEE Trans. Knowl. Data Eng.

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Abstract-Authority flow techniques like PageRank and ObjectRank can provide personalized ranking of typed entity-relationship graphs. There are two main ways to personalize authority flow ranking: Node-based personalization, where authority originates from a set of user-specific nodes; Edge-based personalization, where the importance of different edge types is user-specific. We propose the first approach to achieve efficient edge-based personalization using a combination of precomputation and runtime algorithms.In particular, we apply our method to ObjectRank, where a personalized weight assignment vector (WAV) assigns different weights to each edge type or relationship type. Our approach includes a repository of rankings for various WAVs. We consider the following two classes of approximation: (a) SchemaApprox is formulated as a distance minimization problem at the schema level; (b) DataApprox is a distance minimization problem at the data graph level. SchemaApprox is not robust since it does not distinguish between important and trivial edge types based on the edge distribution in the data graph. In contrast, DataApprox has a provable error bound. Both SchemaApprox and DataApprox are expensive so we develop efficient heuristic implementations, ScaleRank and PickOne respectively. Extensive experiments on the DBLP data graph show that ScaleRank provides a fast and accurate personalized authority flow ranking.

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Section: Approximation Methodsmentioning

confidence: 99%

Section: Approximation Methodsmentioning

confidence: 99%

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Efficient Ranking on Entity Graphs with Personalized Relationships

Hristidis

Raschid

2014

IEEE Trans. Knowl. Data Eng.

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“…Decomposition of PPR into Partial Vectors, Hubs Skeleton, and Hubs Vector was described in [Jeh and Widom 2003]. The approximation of similarity scores based on PPR using partially computed values was explored in [Fogaras et al 2005]. Most such work only deal with static graphs.…”

Section: Diversity In Recommendationsmentioning

confidence: 99%

Updatable, Accurate, Diverse, and Scalable Recommendations for Interactive Applications

Paudel

Christoffel

Newell

et al. 2016

ACM Trans. Interact. Intell. Syst.

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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...

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“…Each e ∈ E has a corresponding weight w ∈ W that indicates the similarity between two vertices. Personalized page rank (PPR) [24] starts from an arbitrary vertex. However, unlike standard PPR, node isolation-based algorithms detach the vertex that acquires the highest weight from the graph G at some time point; they then proceed with PPR until the next time point at which the next detachment node is selected.…”

Section: Cost-flow-based Algorithmsmentioning

confidence: 99%

A Survey on Recommendation Methods Beyond Accuracy

Han

Yamana

2017

IEICE Trans. Inf. & Syst.

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SUMMARYIn recommending to another individual an item that one loves, accuracy is important, however in most cases, focusing only on accuracy generates less satisfactory recommendations. Studies have repeatedly pointed out that aspects that go beyond accuracy-such as the diversity and novelty of the recommended items-are as important as accuracy in making a satisfactory recommendation. Despite their importance, there is no global consensus about definitions and evaluations regarding beyondaccuracy aspects, as such aspects closely relate to the subjective sensibility of user satisfaction. In addition, devising algorithms for this purpose is difficult, because algorithms concurrently pursue the aspects in trade-off relation (i.e., accuracy vs. novelty). In the aforementioned situation, for researchers initiating a study in this domain, it is important to obtain a systematically integrated view of the domain. This paper reports the results of a survey of about 70 studies published over the last 15 years, each of which addresses recommendations that consider beyond-accuracy aspects. From this survey, we identify diversity, novelty, and coverage as important aspects in achieving serendipity and popularity unbiasedness-factors that are important to user satisfaction and business profits, respectively. The five major groups of algorithms that tackle the beyond-accuracy aspects are multi-objective, modified collaborative filtering (CF), clustering, graph, and hybrid; we then classify and describe algorithms as per this typology. The off-line evaluation metrics and user studies carried out by the studies are also described. Based on the survey results, we assert that there is a lot of room for research in the domain. Especially, personalization and generalization are considered important issues that should be addressed in future research (e.g., automatic per-user-trade-off among the aspects, and properly establishing beyond-accuracy aspects for various types of applications or algorithms).

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Towards Scaling Fully Personalized PageRank: Algorithms, Lower Bounds, and Experiments

Cited by 251 publications

References 32 publications

Efficient Ranking on Entity Graphs with Personalized Relationships

Efficient Ranking on Entity Graphs with Personalized Relationships

Updatable, Accurate, Diverse, and Scalable Recommendations for Interactive Applications

A Survey on Recommendation Methods Beyond Accuracy

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