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An Improved hits algorithm Based on Page-query Similarity and Page Popularity

JOURNAL OF COMPUTERS 2012年第1期7卷 130-134页

作者： Liu, Xinyue Lin, Hongfei Zhang, Cong Dalian Univ Technol Sch Comp Sci & Technol Dalian Peoples R China Dalian Univ Technol Sch Software Dalian Peoples R China

The hits algorithm is a very popular and effective algorithm to rank web documents based on the link information among a set of web pages. However, it assigns every link with the same weight. This assumption results in topic drift. In this paper, we firstly define the generalized similarity between a query and a page, and the popularity of a web page. Then we propose a weighted hits algorithm which differentiates the importance of links with the query-page similarities and the popularity of web pages. Experimental results indicate that the improved hits algorithm can find more relevant pages than hits and improve the relevance by 30%-50%. Furthermore, it can avoid the problem of topic drift and enhance the quality of web search effectively.

关键词： hits algorithm Link Analysis Similarity Popularity

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Combining hyperlink-induced topic search and Bayesian approach for personalised manufacturing service recommendation

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INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING 2017年第11期30卷 1152-1163页

作者： Zhang, W. Y. Guo, S. S. Zhang, S. Zhejiang Univ Finance & Econ Sch Informat Hangzhou Zhejiang Peoples R China Zhejiang Univ Finance & Econ Hangzhou Zhejiang Peoples R China

The explosive growth of Web service technologies for integrating virtual enterprises has resulted in the serious information overload. Thus the users are facing increasing difficulty in selecting the correct manufacturing services from the vast amount provided or recommended by collaborative partners for service-oriented supply chain deployment. Therefore, in this paper, a novel approach is presented for recommending personalised manufacturing services by combining a Hyperlink-Induced Topic Search (hits) algorithm and the Bayesian approach. The personalised service recommendation problem is modelled to determine the optimal manufacturing services that are most probably the best selections to user preferences for some known manufacturing services. Further, the Bayesian approach decomposes such a problem of posterior probability into two sub-problems: the prior probability of a manufacturing service hypothesis before considering any user preferences, and the conditional probability of considering the aggregated preferences for some known manufacturing services as evidences when the user actually wants an unknown most preferable manufacturing service. Next, the personalised hits algorithm is adapted to the network of service-oriented supply chain to rank the authority scores of manufacturing services that determine the relative probabilities of service execution through personalised trust propagation. The experimental results show that the proposed method can produce more accurate manufacturing service recommendation results than the existing approaches.

关键词： Bayesian approach collaborative manufacturing hits algorithm manufacturing service service discovery service-oriented architecture service recommendation

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A Time-Aware Personalized Point-of-Interest Recommendation via High-Order Tensor Factorization

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ACM TRANSACTIONS ON INFORMATION SYSTEMS 2017年第4期35卷 31-31页

作者： Li, Xin Jiang, Mingming Hong, Huiting Liao, Lejian Beijing Inst Technol Sch Comp Sci BJ ER Ctr HVLIP Beijing 100081 Peoples R China

Recently, location-based services (LBSs) have been increasingly popular for people to experience new possibilities, for example, personalized point-of-interest (POI) recommendations that leverage on the overlapping of user trajectories to recommend POI collaboratively. POI recommendation is yet challenging as it suffers from the problems known for the conventional recommendation tasks such as data sparsity and cold start, and to a much greater extent. In the literature, most of the related works apply collaborate filtering to POI recommendation while overlooking the personalized time-variant human behavioral tendency. In this article, we put forward a fourth-order tensor factorization-based ranking methodology to recommend users their interested locations by considering their time-varying behavioral trends while capturing their long-term preferences and short-term preferences simultaneously. We also propose to categorize the locations to alleviate data sparsity and cold-start issues, and accordingly new POIs that users have not visited can thus be bubbled up during the category ranking process. The tensor factorization is carefully studied to prune the irrelevant factors to the ranking results to achieve efficient POI recommendations. The experimental results validate the efficacy of our proposed mechanism, which outperforms the state-of-the-art approaches significantly.

关键词： Time-aware POI recommendation tensor factorization hits algorithm

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Application of hits algorithm in Web Mining in Literature Evaluation and Empirical Study

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COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2011年第10期40卷 1576-1586页

作者： Xue, Wei Shen, Peng Renmin Univ China Sch Stat Beijing 100872 Peoples R China

This article has applied the hits algorithm in Web mining in the evaluation of academic literature. Through the empirical analysis of academic articles on the credit risk of the domestic banks, this article proves the feasibility and comprehensiveness of using the hits algorithm in literature evaluation and finds that the generality and authority of corresponding theses have enormous space for further study and development.

关键词： Citation analysis Credit risk hits algorithm Literature evaluation

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A novel page ranking algorithm based on triadic closure and hyperlink-induced topic search

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INTELLIGENT DATA ANALYSIS 2015年第5期19卷 1131-1149页

作者： Du, Yajun Tian, Xiuxia Liu, Wenjun Wang, Min Song, Wen Fan, Yongquan Wang, Xiaoming Xihua Univ Sch Comp & Software Engn Chengdu 610039 Sichuan Peoples R China

The Hyperlink-Induced Topic Search (hits) algorithm developed by Jon Kleinberg made use of the link structure of the web pages on the Web in order to discover and rank web pages being relevant to a particular topic. However it only took account of the hyperlink structure, while completely excluded contents of web pages, and it ignored the fact that degrees of the importance of many hyperlinks on the Web may be different. In this paper, to overcome the topic drifts, we proposed a novel page ranking algorithm combining the hyperlink with the triadic closure theory by considering fully the Vector Space Model (VSM) and the TrustRank algorithm. The method firstly computed the relevance between two randomly arbitrary web pages based on web page topic similarity and common reference degree. Then, by using that model as a point of reference, a new adjacency matrix was constructed to iteratively calculate the authority and hub values of web pages. Next, we calculated the trust-degree for each web page in the basic set by the trust-score algorithm. Finally, the score for each web page is computed by linearly merging the authority and the trust-degree. In our experiments, we used five classic hits-based algorithms to compare with our proposed page ranking algorithm-PCThits (Web Page Topic Similarity, Common Reference Degree, Trust-degree) algorithm. The experimental results demonstrated that our proposed algorithm outperform the other four classic improved algorithms and hits algorithm.

关键词： hits algorithm triadic closure trust-degree topic similarity common reference random walks

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Effective Model And Implementation Of Dynamic Ranking In Web Pages 5

Effective Model And Implementation Of Dynamic Ranking In Web...

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5th International Conference on Communication Systems and Network Technologies (CSNT)

作者： Divjot Singh, Jaiteg Chitkara Univ Chitkara Sch Comp Sci Chandigarh Punjab India

ISBN: (纸本)9781479917976

Web page ranking algorithms are used to score the universal resource locators or simply online links of the web applications. The corporate world strives to develop the web applications in such a way so that it can be visible on the top results in the major search engines and search directories. A number of web page ranking algorithms are developed with different scientific approaches making use of assorted mathematical formulations. Google Page Rank, Yahoo Web Rank, hits algorithm, Tag Rank algorithm, Weighted Links Rank algorithm, Relation Based algorithm, Time Rank algorithm and many other approaches are used in the industry so that the accurate and effective scoring can be done. In this research paper, the effective algorithmic approach based on the enhancement in hits algorithm is performed. This work focuses on the implementation of augmentation in the classical hits algorithm. The results depict the proposed approach providing effective and long term unbiased scoring of the web applications. In this research work, the implementation of deep link validity is performed and the results are obtained in terms of deep scoring and execution time.

关键词： hits algorithm Web Page Ranking Web Mining

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Identification of Microblog Opinion Leader Based on User Feature and Interaction Network 11

Identification of Microblog Opinion Leader Based on User Fea...

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11th Web Information System and Application Conference

作者： Luo Jing Xu Lizhen Southeast Univ Sch Comp Sci & Engn Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781479957279

Opinion leaders are core users in online communities, who can guide the direction of the public opinion. With the rapid development of microblog, identification of the microblog opinion leaders has become a significant task. In this paper, we propose a hybrid data mining approach based on user feature and interaction network, which includes three parts: a way to analyze users' authority, activity and influence, a way to consider the orientation of sentiment in interaction network and a combined method based on hits algorithm for identifying micro blog opinion leaders. Comparative results show that this mechanism can provide an effective mining of the user feature and a better rate of recognition.

关键词： opinion leaders user feature interaction network hits algorithm

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Construction of Diachronic Ontologies from People's Daily of Fifty Years 9

Construction of Diachronic Ontologies from <i>People's Daily...

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9th International Conference on Language Resources and Evaluation (LREC)

作者： He, Shaoda Zou, Xiaojun Xiao, Liumingjing Hu, Junfeng Peking Univ Beijing 100871 Peoples R China Peking Univ Minist Educ Key Lab Computat Linguist Beijing 100871 Peoples R China

ISBN: (纸本)9782951740884

This paper presents an Ontology Learning From Text (OLFT) method follows the well-known OLFT cake layer framework. Based on the distributional similarity, the proposed method generates multi-level ontologies from comparatively small corpora with the aid of hits algorithm. Currently, this method covers terms extraction, synonyms recognition, concepts discovery and concepts hierarchical clustering. Among them, both concepts discovery and concepts hierarchical clustering are aided by the hits authority, which is obtained from the hits algorithm by an iteratively recommended way. With this method, a set of diachronic ontologies is constructed for each year based on People's Daily corpora of fifty years (i. e., from 1947 to 1996). Preliminary experiments show that our algorithm outperforms the Google's RNN and K-means based algorithm in both concepts discovery and concepts hierarchical clustering.

关键词： Ontology Learning From Text (OLFT) Diachronic ontologies hits algorithm

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Study on Finding Experts in Community Question-Answering System

Study on Finding Experts in Community Question-Answering Sys...

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International Conference on Advances in Materials Science and Information Technologies in Industry (AMSITI)

作者： Yang, Rongrong Wu, Jianhua Jinan Univ Elect & Informat Coll Zhuhai 519070 Peoples R China

ISBN: (纸本)9783038350125

As one of the new service model of Web 2.0, the emergence of Community Question-Answering system brings a new way for users to obtain information. However, the explosive growth of users and information, it will be hard for users to obtain the information quickly and accurately. Therefore, it is important to find experts in Community Question-Answering system to improve the accuracy and efficiency of information obtaining. This paper firstly analyzed the relationship among users, questions, and answers in Community Question-Answering system, and built the user diagram, and then by means of the Web mining technology, that is the link analysis weighted hits algorithm, to find experts out. Finally, three evaluation indices were used to measure the validity of the experts finding algorithm. Experimental results show the effectiveness of the weighted hits algorithm.

关键词： Community Question-Answering System Expert Finding hits algorithm Link Analysis

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Analysis Using Popularity Awareness Index, Recency Index and Purchase Diversity in Group Buying

Analysis Using Popularity Awareness Index, Recency Index and...

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14th Industrial Conference on Data Mining (ICDM)

作者： Shirai, Yasuyuki Morita, Hiroyuki Nakamoto, Masakazu Oyama, Satoshi Hokkaido Univ JST ERATO Minato Discrete Struct Manipulat Syst P Sapporo Hokkaido Japan Osaka Prefecture Univ Coll Sustainable Syst Sci Osaka Japan Hokkaido Univ Grad Sch Informat Sci & Technol Sapporo Hokkaido Japan

ISBN: (纸本)9783319089768;9783319089751

We propose new metrics for customers' purchasing behaviors in a group buying coupon website, based on hits algorithms and information entropy: that is, popularity awareness index, recency index, and purchase diversity. These indices are used to classify customers and predict future behaviors. This paper includes definitions of these new indices to be used in real group buying websites. In these websites, adequate characteristics for customers are strongly required and are critical for marketing purpose. We will also provide some experimental results on real data set, including customer segmentation used in future marketing planning.

关键词： group buying hits algorithm entropy

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