Personalized search for social media via dominating verbal context

Xie, Haoran; Li, Xiaodong; Wang, Tao; Chen, Li; Li, Ke; Wang, Fu Lee; Cai, Yi; Li, Qing; Min, Huaqing

doi:10.1016/j.neucom.2014.12.109

Cited by 38 publications

(14 citation statements)

References 27 publications

(38 reference statements)

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“…The most representative ones are past user queries and click-through information measuring the amount of times a page is clicked or the amount of times it's been viewed but not clicked, i.e., the so called impression data. In one of the most recent attempts to model verbal context, authors in [48] propose a verbal context folksonomy graph coupled together with a ranking method; the latter is influenced by issued queries and extracted user profiles. An earlier idea on the subject was developed in [49,50], where the Open Directory Project (DMOZ) categories have been exploited in order to represent contextual information of web pages, focusing on five specific items, namely: Interaction-, collection-, task-, historic-, and social-related sources.…”

Section: Verbal Contextmentioning

confidence: 99%

“…In the verbal context case, research works vary with respect to their focus. Again, context modeling is evident in [48], but there also works focusing solely on the linguistic aspect of verbal context, as well [55]. The interesting topic of efficiently predicting or even expanding user interests and preferences is enabled by respective contextual approaches in [49,50,54].…”

Section: Verbal Contextmentioning

confidence: 99%

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The Evolution of Contextual Information Processing in Informatics

Mylonas

2018

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After many decades of flourishing computer science it is now rather evident that in a world dominated by different kinds of digital information, both applications and people are forced to seek new, innovative structures and forms of data management and organization. Following this blunt observation, researchers in informatics have strived over the recent years to tackle the non-unique and rather evolving notion of context, which aids significantly the data disambiguation process. Motivated by this environment, this work attempts to summarize and organize in a researcher-friendly tabular manner important or pioneer related research works deriving from diverse computational intelligence domains: Initially, we discuss the influence of context with respect to traditional low-level multimedia content analysis and search, and retrieval tasks and then we advance to the fields of overall computational context-awareness and the so-called human-generated contextual elements. In an effort to provide meaningful information to fellow researchers, this brief survey focuses on the impact of context in modern and popular computing undertakings of our era. More specifically, we focus to the presentation of a short review of visual context modeling methods, followed by the depiction of context-awareness in modern computing. Works dealing with the interpretation of context by human-generated interactions are also discussed herein, as the particular domain gains an ever-increasing proportion of related research nowadays. We then conclude the paper by providing a short discussion on (i) the motivation behind the included context type categorization into three main pillars; (ii) the findings and conclusions of the survey for each context category; and (iii) a couple of brief advices derived from the survey for both interested developers and fellow researchers.

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Section: Verbal Contextmentioning

confidence: 99%

Section: Verbal Contextmentioning

confidence: 99%

The Evolution of Contextual Information Processing in Informatics

Mylonas

2018

Information

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“…Prior parameters α, β and λ of EUMG model are set to K/50, 0.01 and 10 respectively, where K denotes the number of latent topics. The number of topics and the number of terms selected from user models are chosen from [5,50]. The dimension of numbers and the number of documents retrieved from an exterior corpus are chosen from [10,100] and [1,10] respectively.…”

Section: Eai Endorsed Transactions Onmentioning

confidence: 99%

“…This behavior makes the information search process an external corpus mean of Log-normal distribution of retrieval scores for topic terms extracted from documents annotated by a user deviation of Log-normal distribution of retrieval scores for topic a user's set of external documents , the number of times that topic sampled from document set of terms extracted from a user's set of external documents , , the number of times , generated by topic a original query relevant document a query containing the concatenated tags of a user The number of independent query words a query extracted from a document that a user tagged a hidden model λ number of top terms extracted from total number of documents number of documents extracted from a particular topic learnt the mean of the normal distribution number of top terms selected from user models even more difficult than normal web search systems. To deal with this problem, personalized search results reranking [3][4][5][6] and personalized query expansion [7][8][9] have been widely adopted.…”

Section: Introductionmentioning

confidence: 99%

An Augmented User Model for Personalized Search in Collaborative Social Tagging Systems

Zhao¹,

Zhou²,

Wu³

et al. 2017

EAI Endorsed Transactions on Collaborative Computing

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Alongside the enormous volume of user-generated content posted to World Wide Web, there exists a thriving demand for search personalization services. To provide personalized services, a user model is usually required. We address the setting adopted by the majority of previous work, where a user model consists solely of the user's past information. We construct an augmented user model from a number of tags and documents. These resources are further processed according to the user's past information by exploring external knowledge base. A novel generative model is proposed for user model generation. This model utilizes recent advances in neural language models such as Word Embeddings with latent semantic models such as Latent Dirichlet Allocation. We further present a new query expansion method to facilitate the desired personalized retrieval. Experiments conducted on two real-world collaborative social tagging datasets show that our proposed methods outperform state-of-the-art methods.

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“…In the literature, some works propose to solve the cold start problem in POI recommendation using social network [31,32,38]. For example, [8] present a solution to simulate the influence of social networks on POI for the better POI recommendation.…”

Section: Introductionmentioning

confidence: 99%

Protecting multi-party privacy in location-aware social point-of-interest recommendation

Wang¹,

Liu²,

Li³

et al. 2018

World Wide Web

Self Cite

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Q, et al. (2018) Protecting multi-party privacy in location-aware social point-ofinterest recommendation. World Wide Web. Available: http:// dx.Abstract Point-of-interest (POI) recommendation has attracted much interest recently because of its significant business potential. Data used in POI recommendation (e.g., user-location check-in matrix) are much more sparse than that used in traditional item (e.g., book and movie) recommendation, which leads to more serious cold start problem. Social POI recommendation has proved to be an effective solution, but most existing works assume that recommenders have access to all required data. This is very rare in practice because these data are generally owned by different entities who are not willing to share their data with others due to privacy and legal concerns. In this paper, we first propose PLAS, a protocol which enables effective POI recommendation without disclosing the sensitive data of every party getting involved in the recommendation. We formally show PLAS is secure in the semi-honest adversary model. To improve its performance. We then adopt the technique of cloaking area by which expensive distance computation over encrypted data is replaced by cheap operation over plaintext. In addition, we utilize the sparsity of check-ins to selectively publish data, thus reducing encryption cost and avoiding unnecessary computation over ciphertext. Experiments on two real datasets show that our protocol is feasible and can scale to large POI recommendation problems in practice.

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Personalized search for social media via dominating verbal context

Cited by 38 publications

References 27 publications

The Evolution of Contextual Information Processing in Informatics

The Evolution of Contextual Information Processing in Informatics

An Augmented User Model for Personalized Search in Collaborative Social Tagging Systems

Protecting multi-party privacy in location-aware social point-of-interest recommendation

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