Query personalization using social network information and collaborative filtering techniques

Margaris, Dionisis; Vassilakis, Costas; Georgiadis, Panagiotis

doi:10.1016/j.future.2017.03.015

Cited by 53 publications

(12 citation statements)

References 27 publications

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“…However, their usage has span multiple other domains in the past few years, from digital collections (e.g. news and research articles [31], to database queries [32,33] or even web services [34,35]).…”

Section: Beyond Simple Item Recommendationsmentioning

confidence: 99%

Recommender Systems for Large-Scale Social Networks: A review of challenges and solutions

Eirinaki

Gao

Varlamis

et al. 2018

Future Generation Computer Systems

163

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Social networks have become very important for networking, communications, and content sharing. Social networking applications generate a huge amount of data on a daily basis and social networks constitute a growing field of research, because of the heterogeneity of data and structures formed in them, and their size and dynamics. When this wealth of data is leveraged by recommender systems, the resulting coupling can help address interesting problems related to social engagement, member recruitment, and friend recommendations. In this work we review the various facets of large-scale social recommender systems, summarizing the challenges and interesting problems and discussing some of the solutions.

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Section: Beyond Simple Item Recommendationsmentioning

confidence: 99%

Recommender Systems for Large-Scale Social Networks: A review of challenges and solutions

Eirinaki

Gao

Varlamis

et al. 2018

Future Generation Computer Systems

163

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“…Still, these preliminary approaches have yet to integrate standards in QAC, e.g. query personalization (Koutrika and Ioannidis, 2005;Margaris et al, 2018) and time sensitivity (Cai et al, 2014). This integration has to differ from traditional approaches by taking full advantage of neural language modeling.…”

Section: Context Informationmentioning

confidence: 99%

Personalized neural language models for real-world query auto completion

Fiorini¹,

Lu²

2018

Proceedings of the 2018 Conference of the North American Chapter Of the Association for Computational Linguistics: Hu

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Query auto completion (QAC) systems are a standard part of search engines in industry, helping users formulate their query. Such systems update their suggestions after the user types each character, predicting the user's intent using various signals -one of the most common being popularity. Recently, deep learning approaches have been proposed for the QAC task, to specifically address the main limitation of previous popularity-based methods: the inability to predict unseen queries. In this work we improve previous methods based on neural language modeling, with the goal of building an end-to-end system. We particularly focus on using real-world data by integrating user information for personalized suggestions when possible. We also make use of time information and study how to increase diversity in the suggestions while studying the impact on scalability. Our empirical results demonstrate a marked improvement on two separate datasets over previous best methods in both accuracy and scalability, making a step towards neural query auto-completion in production search engines.

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“…Concern. MEC aims to offer computing resources and information technology services at the network edge, where mobile users face a bewildering array of options in an MEC edge server, such as which movie to watch [13], which news to read [14], and which people to add as friends [15]. Personalization services have been applied to help users find the items that best fit their interests by learning the past item-user relationships and performing information-filtering methods.…”

Section: Personalization Service Versus Privacymentioning

confidence: 99%

CEPTM: A Cross‐Edge Model for Diverse Personalization Service and Topic Migration in MEC

Zhang

Cui

et al. 2018

Wireless Communications and Mobile Computing

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For several reasons, the cloud computing paradigm, e.g., mobile edge computing (MEC), is suffering from the problem of privacy issues. MEC servers provide personalization services to mobile users for better QoE qualities, but the ongoing migrated data from the source edge server to the destination edge server cause users to have privacy concerns and unwillingness of self-disclosure, which further leads to a sparsity problem. As a result, personalization services ignore valuable user profiles across edges where users have accounts in and tend to predict users’ potential purchases with insufficient sources, thereby limiting further improvement of QoE through personalization of the contents. This paper proposes a novel model, called CEPTM, which (1) collects mobile user data across multiple MEC edge servers, (2) improves the users’ experience in personalization services by loading collected diverse data, and (3) lowers their privacy concern with the improved personalization. This model also reveals that famous topics in one edge server can migrate into several other edge servers with users’ favorite content tags and that the diverse types of items could increase the possibility of users accepting the personalization service. In the experiment section, we use exploratory factor analysis to mathematically evaluate the correlations among those factors that influence users’ information disclosure in the MEC network, and the results indicate that CEPTM (1) achieves a high rate of personalization acceptance due to the availability of more data as input and highly diverse personalization as output and (2) gains the users’ trust because it collects user data while respecting individual privacy concerns and providing better personalization. It outperforms a traditional personalization service that runs on a single-edge server. This paper provides new insights into MEC diverse personalization services and privacy problems, and researchers and personalization providers can apply this model to merge popular users’ like trends throughout the MEC edge servers and generate better data management strategies.

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Query personalization using social network information and collaborative filtering techniques

Cited by 53 publications

References 27 publications

Recommender Systems for Large-Scale Social Networks: A review of challenges and solutions

Recommender Systems for Large-Scale Social Networks: A review of challenges and solutions

Personalized neural language models for real-world query auto completion

CEPTM: A Cross‐Edge Model for Diverse Personalization Service and Topic Migration in MEC

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