Hypertext Induced Topic Search Research Articles

Content and Location Based Point-of-Interest Recommendation System Using HITS Algorithm

A study of geographic information has become a significant field of concentrate in software engineering because of the expansion of much information created by electronic gadgets fit together geographic data from people, like advanced mobile phones and GPS gadgets. Area facts allow a deeper understanding of users’ options and actions by bridging the gap between the physical and digital worlds. This expansion of wide geo-spatial datasets has inspired studies into novel recommender systems that aim to make users’ travels and social interactions easier. This huge amount of data generated by these devices has prompted an increase in the number of research activities and procedures aimed at breaking down and recovering useful data based on these large datasets. The aim of this challenge is to study GPS directions from a variety of people, as well as examine and apply computational strategies to recover useful data from those directions, useful data from GPS directions, including areas of interest and people’s proximity, and then create an instrument for information representation. This paper demonstrates how data mining techniques is used to recover valuable information from spatial data. As well as how such data can be useful in understanding people and areas within a district. Depending on the outcomes, we recommend a HITS (Hypertext Induced Topic Search) based POI recommendation calculation that can take into account the effect of social connections when recommending POIs to individual users.

A normalized score-based weighted PageRank algorithm on ranking prediction of basketball games

Ranking of sports teams has always been significant to sponsors, coaches, as well as audiences. Prevailing prediction methods investigate probabilities by taking into account of different kinds of attributes (e.g. field goals, fields goal attempts) in order to establish a detail-based mechanism for analyzing the capability among competing teams. The different types of activation and inhibition actions between athletes provide a considerable challenge in the framework of network analysis. Moreover, these attributes interactions might add up the substantial redundancy to network frame as well. This paper proposes a weighted PageRank algorithm based on the normalized basketball match scores from a macroscopic point of view. Taking Chinese Basketball Association and Chinese University Basketball Association as examples, the developed approach takes into account the win/lose nature of interactions between each pair of competing teams in the framework of PageRank network. We also evolve a weighted network model for the network matrix which highlights the capability difference of teams whose PageRank probabilities are most sensitive with respect to the scores of the two competing teams. The chance of championship of teams can be better demonstrated by the PageRank probabilities. The results show that our method achieves more precise predicting result than that of original PageRank algorithm and Hypertext-Induced Topic Search algorithm.

Identification of Fictitious Messages in Social Network using E-Hits and Newsapi

Social network has become a primary resource for users to send and receive the foremost up-to-date data and trend the present events. Currently, most of the social network contains the fictional content that was created by the influential spreaders wherever the message originality and therefore the spreader identity cannot be found which affects the end users. The proposed models to discover fictitious messages are verifying the contextual integrity with the trained classifier using large datasets. But the problem lies in updating of datasets with the recent or trending events from trusted sources in a regular interval. In the existing model, Hypertext-Induced Topic Search (HITS) method has been used for rating posts based on hub score and authority score. The hub score is calculated based on how many posts are posted or liked or tagged by the user and authority score is calculated based on how many users liked or tagged a post. If the user who ranks high in hub score tries to trend the low ranked post in authority score, the user will be marked as spreader. But the problem lies in the identification and verification of the posts that ranks in authority score. In our proposed system, we have enhanced the HITS algorithm by adding a third mechanism called top score which assigns weightage for every post based on the time they have posted. The time and content of the post has been verified by the integrated new model NewsAPI. Based on the three scores, the posts are filtered and matched with the news collected from NewsAPI. The news or posts that have not been matched either with the context or with the time will be marked as fictitious.

A collective entity linking algorithm with parallel computing on large-scale knowledge base

Entity linking is a central concern of automatic knowledge question answering and knowledge base population. Traditional collective entity linking approaches only consider one of the entity contexts or semantic relations between entities. Thus, these approaches always have poor performance on Web documents. The efficiency of collective entity linking needs to be improved as well. This paper proposes a collective entity linking algorithm based on topic model and graph. Constructing the topic model can represent mentions and candidate entities by using topic distributions. It makes full use of context in documents. Entity semantic relations are represented by document similarities which are computed through the topic model. Parallel computing is used to reduce long running time which is caused by topic model construction. Entity graph is constructed according to the relations between entities in the knowledge graph. Hypertext-Induced Topic Search exploits the entity graph to compute hub value and authority value of candidate entities. And the authority value is the basis for entity linking. Experimental results on open-domain corpus (NLPCC2014) demonstrate the validity of the proposed method. Experimental results show that the proposed approach has 5.2% improvement in $$F_{1}$$-measure than AGDISTIS on corp NLPCC2014 .

Human-Centric Cyber Social Computing Model for Hot-Event Detection and Propagation

Microblogging networks have gained popularity in recent years as a platform enabling expressions of human emotions, through which users can conveniently produce contents on public events, breaking news, and/or products. Subsequently, microblogging networks generate massive amounts of data that carry opinions and mass sentiment on various topics. Herein, microblogging is regarded as a useful platform for detecting and propagating new hot events. It is also a useful channel for identifying high-quality posts, popular topics, key interests, and high-influence users. The existence of noisy data in the traditional social media data streams enforces to focus on human-centric computing. This paper proposes a human-centric social computing (HCSC) model for hot-event detection and propagation in microblogging networks. In the proposed HCSC model, all posts and users are preprocessed through hypertext induced topic search (HITS) for determining high-quality subsets of the users, topics, and posts. Then, a latent Dirichlet allocation (LDA)-based multiprototype user topic detection method is used for identifying users with high influence in the network. Furthermore, an influence maximization is used for final determination of influential users based on the user subsets. Finally, the users mined by influence maximization process are generated as the influential user sets for specific topics. Experimental results prove the superiority of our HCSC model against similar models of hot-event detection and information propagation.

Event detection and identification of influential spreaders in social media data streams

Microblogging, a popular social media service platform, has become a new information channel for users to receive and exchange the most up-to-date information on current events. Consequently, it is a crucial platform for detecting newly emerging events and for identifying influential spreaders who have the potential to actively disseminate knowledge about events through microblogs. However, traditional event detection models require human intervention to detect the number of topics to be explored, which significantly reduces the efficiency and accuracy of event detection. In addition, most existing methods focus only on event detection and are unable to identify either influential spreaders or key event-related posts, thus making it challenging to track momentous events in a timely manner. To address these problems, we propose a Hypertext-Induced Topic Search (HITS) based Topic-Decision method (TD-HITS), and a Latent Dirichlet Allocation (LDA) based Three-Step model (TS-LDA). TDHITS can automatically detect the number of topics as well as identify associated key posts in a large number of posts. TS-LDA can identify influential spreaders of hot event topics based on both post and user information. The experimental results, using a Twitter dataset, demonstrate the effectiveness of our proposed methods for both detecting events and identifying influential spreaders.

A Technique to Improved Page Rank Algorithm in perspective to Optimized Normalization Technique

Page Ranking is an essential segment for information retrieval system. It is utilized to gauge the significance and conduct of website pages. We audit two methodologies for ranking: HITS idea and Page Rank technique. Both methodologies concentrate on the link structure of the Web to discover the significance of the Web pages. The Page Rank algorithm figures the rank of individual website page and Hypertext Induced Topic Search (HITS) relies on the hubs and authority framework. A quick and efficient page ranking component for web retrieval stays as a test. This paper proposed another page rank algorithm which utilizes a normalization technique in view of mean value of page ranks. The proposed scheme reduces the time complexity of the traditional Page Rank algorithm by diminishing the number of iterations to reach a convergence point . Keywords: Ranking, Page Rank, HITS, Hyperlink, Normalization

A temporal-aware POI recommendation system using context-aware tensor decomposition and weighted HITS

The popularity of location-based social networks (LBSN) provides us with a new perspective for understanding people's travel behaviours and enables a lot of location-based services, such as point of interest (POI) recommendation. However, personalized POI recommendation is very challenging, as the user-location matrix is very sparse for traditional collaborative filtering (CF)-based POI recommendation approaches. The problem becomes even more challenging when people travel to a new city. In addition, temporal influence plays an important role in POI recommendation, for most users tend to visit different kinds of POIs at different time in a day, e.g., visiting a food-related POI at noon and visiting a nightlife spot at night. To the end, we propose a novel POI recommendation system, which consists of two components: context-aware tensor decomposition (CTD) for user preferences modelling and weighted HITS (Hypertext Induced Topic Search)-based POI rating (WHBPR). We model user preferences with a three-dimension tensor (user-category-time). Supplementing the missing entries of the tensor through CTD with the aid of other three matrices, we recover user preferences of different time slots. WHBPR incorporates the impacts of user preferences and social opinions on POI rating. We evaluated our method using the real Foursquare datasets, verifying the advantages of our method beyond other baselines.

New use of the HITS algorithm for fast web page classification

The immense number of documents published on the web requires the utilization of automatic classifiers that allow organizing and obtaining information from these large resources. Typically, automatic web pages classifiers handle millions of web pages, tens of thousands of features, and hundreds of categories. Most of the classifiers use the vector space model to represent the dataset of web pages. The components of each vector are computed using the term frequency inversed document frequency (TFIDF) scheme. Unfortunately, TFIDF-based classifiers face the problem of the large-scale size of input data that leads to a long processing time and an increase in resource requests. Therefore, there is an increasing demand to alleviate these problems by reducing the size of the input data without influencing the classification results. In this paper, we propose a novel approach that improves web page classifiers by reducing the size of the input data (i.e. web pages and feature reduction) by using the hypertext induced topic search (HITS) algorithm. We employ HITS results for weighting remaining features. We evaluate the performance of the proposed approach by comparing it with the TFIDF-based classifier. We demonstrate that our approach significantly reduces the time needed for classification.

Toward the Implementation of Relevance and Reputation Indicators in Edu-AREA

This paper introduces the first implementation of relevance and reputation indicators in Edu-AREA. This Web 2.0 application promotes users creating, viewing, adopting, adapting, and curating elements in an open way, following the principles of the open educational resources movement. A main issue is the management and control of quality. Users can provide elements freely, so some mechanism that can estimate or rank their quality is needed. A solution based on usage by other users inspired in the hypertext-induced topic search algorithm is adopted. Taking into account the usages of the elements, a relevance measure for each element and a reputation measure for each user are calculated.

A Novel Improved Web Structure Mining Algorithm

In this paper, a compact and effective web structure mining algorithm is developed according to the hypertext induced topic search (HITS). Firstly, the environments of traditional web structure mining are explored. Secondly, by analyzing the existing techniques, the existing problem and the lack of previous methods are explored. Finally, amid to overcome the deficiency of the traditional method, the paper introduced the time parameter and proposed a novel web structure mining algorithm. It turns out that the proposed algorithm has better effective and accuracy compared to the traditional methods.

Learning travel recommendations from user-generated GPS traces

The advance of GPS-enabled devices allows people to record their location histories with GPS traces, which imply human behaviors and preferences related to travel. In this article, we perform two types of travel recommendations by mining multiple users' GPS traces. The first is a generic one that recommends a user with top interesting locations and travel sequences in a given geospatial region. The second is a personalized recommendation that provides an individual with locations matching her travel preferences. To achieve the first recommendation, we model multiple users' location histories with a tree-based hierarchical graph ( TBHG ). Based on the TBHG , we propose a HITS (Hypertext Induced Topic Search)-based model to infer the interest level of a location and a user's travel experience (knowledge). In the personalized recommendation, we first understand the correlation between locations, and then incorporate this correlation into a collaborative filtering (CF)-based model, which predicts a user's interests in an unvisited location based on her locations histories and that of others. We evaluated our system based on a real-world GPS trace dataset collected by 107 users over a period of one year. As a result, our HITS-based inference model outperformed baseline approaches like rank-by-count and rank-by-frequency . Meanwhile, we achieved a better performance in recommending travel sequences beyond baselines like rank-by-count . Regarding the personalized recommendation, our approach is more effective than the weighted Slope One algorithm with a slightly additional computation, and is more efficient than the Pearson correlation-based CF model with the similar effectiveness.

On the relationship between trading network and WWW network: a preferential attachment perspective

This paper describes the relationship between trading network and WWW network from preferential attachment mechanism perspective. This mechanism is known to be the underlying principle in the network evolution and has been incorporated to formulate two famous web pages ranking algorithms, PageRank and Hypertext Induced Topic Search (HITS). We point out the differences between trading network and WWW network from preferential attachment perspective, derive the formulation of HITS-based ranking algorithm for trading network as a direct consequence of the differences, and apply the same framework when deriving the formulation back to the HITS formulation that turns to become a technique to accelerate its convergences.

A new generalized similarity-based topic distillation algorithm

The procedure of hypertext induced topic search based on a semantic relation model is analyzed, and the reason for the topic drift of HITS algorithm was found to prove that Web pages are projected to a wrong latent semantic basis. A new concept-generalized similarity is introduced and, based on this, a new topic distillation algorithm GSTDA(generalized similarity based topic distillation algorithm) was presented to improve the quality of topic distillation. GSTDA was applied not only to avoid the topic drift, but also to explore relative topics to user query. The experimental results on 10 queries show that GSTDA reduces topic drift rate by 10% to 58% compared to that of HITS(hypertext induced topic search) algorithm, and discovers several relative topics to queries that have multiple meanings.

하이퍼링크 구조를 이용한 웹 검색의 순위 알고리즘에 관한 연구

본 연구에서는 하이퍼 링크 구조를 이용한 웹 검색 알고리즘에 대해 살펴 본 후 페이지 품질을 측정하기 위해 웹의 하이퍼 구조를 이용하고 있는 알고리즘인 HITS와 PageRank를 분석하였다. 이어서 이들 방법을 이용한 검색 엔진인 Google과 Ask.com을 검색 알고리즘의 특성을 기준으로 분석하였다. 이런 연구는 미래의 웹 문서의 중요도를 평가하는 데 기초자료로 활용할 수 있으며, 웹 정보검색의 검색성능을 향상시키는 시스템 개발에 도움이 될 수 있을 것이라 생각한다. In this paper, after reviewing hyperlink based ranking methods, we saw various other parameters that effect ranking. Then, We analyzed the PageRank and HITS(Hypertext Induced Topic Search) algorithm, which are two popular methods that use eigenvector computations to rank results in terms of their characteristics. Finally, google and Ask.com search engines were examined as examples for applying those methods. The results showed that use of Hyperlink structure can be useful for efficiency of web site search.