Browser-oriented universal cross-site recommendation and explanation based on user browsing logs

Zhang, Yongfeng

doi:10.1145/2645710.2653367

Cited by 13 publications

(7 citation statements)

References 25 publications

(48 reference statements)

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“…Providing heterogeneous cross-edge personalization could shed light on brand new and promising models, which could tremendously increase the interaction between users and items using both single-edge and cross-edge methods. Users' interests in items could also be discovered in a wider scope, thereby leading to diverse personalization from which users can choose [30]. As a result, the likelihood that users would accept the personalization would increase.…”

Section: Single-edge Versusmentioning

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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Section: Single-edge Versusmentioning

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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“…Output correlations were used to give recommendations to target users based on their interests. Zhang et al [9] tried to make recommendations across web-sites by using browsing information of users. This idea is similar to ours in the sense that we aim to use multiple social networks and they used multiple browsing history.…”

Section: Related Workmentioning

confidence: 99%

“…The work described in [6] stated that identity resolution solutions can be used by various applications, such as security, privacy and recommendation systems. Some research efforts in recommendation systems concentrate on recommendations across domains, e.g., [8][9][10]. However, these cross-domain recommendation systems focus solely on matching items and have not considered users' preferences across available platforms.…”

Section: Introductionmentioning

confidence: 99%

Making recommendations by integrating information from multiple social networks

2016

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It is becoming a common practice to use recommendation systems to serve users of web-based platforms such as social networking platforms, review web-sites, and e-commerce web-sites. Each platform produces recommendations by capturing, maintaining and analyzing data related to its users and their behavior. However, people generally use different web-based platforms for different purposes. Thus, each platform captures its own data which may reflect certain aspects related to its users. Integrating data from multiple platforms may widen the perspective of the analysis and may help in modeling users more effectively. Motivated by this, we developed a recommendation framework which integrates data collected from multiple platforms. For this purpose, we collected and anonymized datasets which contain information from several social networking and social media platforms, namely BlogCatalog, Twitter, Flickr, Facebook, YouTube and LastFm. The collected and integrated data forms a consolidated repository that may become a valuable source for researchers and practitioners. We implemented a number of recommendation methodologies to observe their performance for various cases which involve using single versus multiple features from a single source versus multiple sources. The conducted

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“…Latent factor models based on Matrix Factorization (MF) techniques have long been an important research direction in Collaborative Filtering (CF)-based recommendations [13,26]. Recently, the MF approaches have gained great popularity, as they usually outperform traditional methods, and In the left is the exampled structure of the rating matrix, and in the right is the real structure of the scattered blocks.…”

Section: Related Workmentioning

confidence: 99%

Understanding the Sparsity

Zhang

et al. 2014

Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management

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An important problem of matrix completion/approximation based on Matrix Factorization (MF) algorithms is the existence of multiple global optima; this problem is especially serious when the matrix is sparse, which is common in realworld applications such as personalized recommender systems. In this work, we clarify data sparsity by bounding the solution space of MF algorithms. We present the conditions that an MF algorithm should satisfy for reliable completion of the unobservables, and we further propose to augment current MF algorithms with extra constraints constructed by compressive sampling on the unobserved values, which is well-motivated by the theoretical analysis. Model learning and optimal solution searching is conducted in a properly reduced solution space to achieve more accurate and efficient rating prediction performances. We implemented the proposed algorithms in the Map-Reduce framework, and comprehensive experimental results on Yelp and Dianping datasets verified the effectiveness and efficiency of the augmented matrix factorization algorithms.

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Browser-oriented universal cross-site recommendation and explanation based on user browsing logs

Cited by 13 publications

References 25 publications

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

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

Making recommendations by integrating information from multiple social networks

Understanding the Sparsity

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