Temporal recommendation on graphs via long- and short-term preference fusion

Xiang, Liang; Yuan, Quan; Zhao, Shiwan; Chen, Li; Zhang, Xiatian; Qing, Yang; Sun, Jimeng

doi:10.1145/1835804.1835896

Cited by 315 publications

(246 citation statements)

References 21 publications

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“…Xiang proposed a session-based temporal graph method that integrates short and long-term temporal variations [4]. Lathia, Hailes, Capra and Amarian compared the results they obtained from item-based CF, the k closest neighbour algorithm, and the SVD methoda matrix factorization technique -in terms of rating accuracy.…”

Section: Related Workmentioning

confidence: 99%

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Collaborative Filtering with Temporal Dynamics with Using Singular Value Decomposition

Bakir

2018

Teh. vjesn.

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Nowadays, Collaborative Filtering (CF) is a widely used recommendation system. However, traditional CF techniques are harder to make fast and accurate suggestions due to changes in user preferences over time, the emergence of new products and the availability of too many users and too many products in the system. Therefore, it becomes more important to make suggestions that are both fast and take the changes in time into consideration. In the presented study, a new method for providing suggestions customized according to the users' preference and taste as they change over time was developed. By combining the time-dependent changes through the SVD (Singular Value Decomposition), a faster suggestion system was developed. Thus, an attempt was made to enhance product prediction success. In the present study all techniques on Netflix data and the results were compared. The results obtained on the accuracy of the predicted ratings were found out to be promising.

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Section: Related Workmentioning

confidence: 99%

“…Also seasonal changes or regional factors [1,4] may change users' perception and perspective over time. For instance, users may develop a new perspective for an actor or movie genre, or may change the types of movies he or she likes over time.…”

Section: Introductionmentioning

confidence: 99%

Collaborative Filtering with Temporal Dynamics with Using Singular Value Decomposition

Bakir

2018

Teh. vjesn.

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“…The use of a graph-based model for recommendations was first introduced in [Aggarwal et al 1999]. To apply a bipartite user-item-feedback graph G was proposed in [Huang et al 2004] and several projects [Baluja et al 2008;Bogers 2010;Cooper et al 2014;Fouss et al 2005;Gori et al 2007;Jamali and Ester 2009;Lee et al 2012;Xiang et al 2010] extended this approach. We classify them as vertex ranking algorithms because their main idea is to rank the vertices in the graph based on their similarities with the target user and use the ranking to generate recommendations.…”

Section: Related Workmentioning

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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“…Recently, another issue has been addressed by a couple of recent published work (Ahmed, Low, Aly, Josifovski, & Smola, 2011;Gueye, Abdessalem, & Naacke, 2012;Li, Yang, Wang, & Kitsuregawa, 2007;Xiang et al, 2010) that is referred to as the dynamicity problem. We define it as the effect of time on user's preferences and how it can be reflected in their profiles.…”

Section: Issues and Problemsmentioning

confidence: 99%

A Survey of User Modelling in Social Media Websites

Abdel-Hafez¹,

Xu²

2013

CIS

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With the widespread of social media websites in the internet, and the huge number of users participating and generating infinite number of contents in these websites, the need for personalisation increases dramatically to become a necessity. One of the major issues in personalisation is building users' profiles, which depend on many elements; such as the used data, the application domain they aim to serve, the representation method and the construction methodology. Recently, this area of research has been a focus for many researchers, and hence, the proposed methods are increasing very quickly. This survey aims to discuss the available user modelling techniques for social media websites, and to highlight the weakness and strength of these methods and to provide a vision for future work in user modelling in social media websites.

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Temporal recommendation on graphs via long- and short-term preference fusion

Cited by 315 publications

References 21 publications

Collaborative Filtering with Temporal Dynamics with Using Singular Value Decomposition

Collaborative Filtering with Temporal Dynamics with Using Singular Value Decomposition

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

A Survey of User Modelling in Social Media Websites

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