A Re-Ranking Method of Search Results Based on Keyword and User Interest

2010 ◽  
pp. 108-121
Author(s):  
Ming Xu ◽  
Hong-Rong Yang ◽  
Ning Zheng
Keyword(s):  
Experimental Results ◽  
Search Process ◽  
Improved Method ◽  
User Interest ◽  
High Recall ◽  
String Match ◽  
Electronic Evidence ◽  
Search Results ◽  
Text String ◽  
Digital Forensic

It is a pivotal task for a forensic investigator to search a hard disk to find interesting evidences. Currently, most search tools in digital forensic field, which utilize text string match and index technology, produce high recall (100%) and low precision. Therefore, the investigators often waste vast time on huge irrelevant search hits. In this chapter, an improved method for ranking of search results was proposed to reduce human efforts on locating interesting hits. The K-UIH (the keyword and user interest hierarchies) was constructed by both investigator-defined keywords and user interest learnt from electronic evidence adaptive, and then the K-UIH was used to re-rank the search results. The experimental results indicated that the proposed method is feasible and valuable in digital forensic search process.

A Collaborative Filtering Recommendation Algorithm Incorporated with Life Cycle

2013 ◽  
Vol 765-767 ◽  
pp. 630-633 ◽  
Author(s):  
Chong Lin Zheng ◽  
Kuang Rong Hao ◽  
Yong Sheng Ding
Keyword(s):  
Life Cycle ◽  
Collaborative Filtering ◽  
Recommendation Systems ◽  
Experimental Results ◽  
User Interest ◽  
Recommendation Algorithm ◽  
Time Information ◽  
Successful Technology

Collaborative filtering recommendation algorithm is the most successful technology for recommendation systems. However, traditional collaborative filtering recommendation algorithm does not consider the change of time information. For this problem,this paper improve the algorithm with two new methods:Predict score incorporated with time information in order to reflect the user interest change; Recommend according to scores by adding the weight information determined by the item life cycle. Experimental results show that the proposed algorithm outperforms the traditional item in accuracy.

Multi-Target Search on Semantic Associations in Linked Data

2018 ◽  
Vol 14 (1) ◽  
pp. 71-97 ◽  
Author(s):  
Xiang Zhang ◽  
Erjing Lin ◽  
Yulian Lv
Keyword(s):  
Linked Data ◽  
Large Scale ◽  
Search Process ◽  
Search Model ◽  
Linguistic Features ◽  
Target Search ◽  
Search Results ◽  
Semantic Associations ◽  
Effectiveness And Efficiency ◽  
Virtual Document

In this article, the authors propose a novel search model: Multi-Target Search (MT search in brief). MT search is a keyword-based search model on Semantic Associations in Linked Data. Each search contains multiple sub-queries, in which each sub-query represents a certain user need for a certain object in a group relationship. They first formularize the problem of association search, and then introduce their approach to discover Semantic Associations in large-scale Linked Data. Next, they elaborate their novel search model, the notion of Virtual Document they use to extract linguistic features, and the details of search process. The authors then discuss the way search results are organized and summarized. Quantitative experiments are conducted on DBpedia to validate the effectiveness and efficiency of their approach.

A Webpage Ranking Algorithm Based on Collaborative Recommendation

2013 ◽  
Vol 765-767 ◽  
pp. 998-1002
Author(s):  
Shao Xuan Zhang ◽  
Tian Liu
Keyword(s):  
Sorting Algorithm ◽  
User Interest ◽  
Recommendation Algorithm ◽  
Search Results ◽  
Collaborative Recommendation ◽  
Interest Model ◽  
Sorting Efficiency ◽  
User Interest Model ◽  
Relevant Calculation ◽  
Personalized Ranking

In view of the present personalized ranking of search results user interest model construction difficult, relevant calculation imprecise problems, proposes a combination of user interest model and collaborative recommendation algorithm for personalized ranking method. The method from the user search history, including the submit query, click the relevant webpage information to train users interest model, then using collaborative recommendation algorithm to obtain with common interests and neighbor users, on the basis of these neighbors on the webpage and webpage recommendation level associated with the users to sort the search results. Experimental results show that: the algorithm the average minimum precision than general sorting algorithm was increased by about 0.1, with an increase in the number of neighbors of the user, minimum accuracy increased. Compared with other ranking algorithms, using collaborative recommendation algorithm is helpful for improving webpage with the user interest relevance precision, thereby improving the sorting efficiency, help to improve the search experience of the user.

scholarly journals Recommending High Utility Queries via Query-Reformulation Graph

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
JianGuo Wang ◽  
Joshua Zhexue Huang ◽  
Dingming Wu
Keyword(s):  
Random Walk ◽  
Search Engine ◽  
State Of The Art ◽  
Transition Probability ◽  
Experimental Results ◽  
Query Reformulation ◽  
Search Results ◽  
Query Recommendation ◽  
Query Logs ◽  
High Utility

Query recommendation is an essential part of modern search engine which aims at helping users find useful information. Existing query recommendation methods all focus on recommending similar queries to the users. However, the main problem of these similarity-based approaches is that even some very similar queries may return few or even no useful search results, while other less similar queries may return more useful search results, especially when the initial query does not reflect user’s search intent correctly. Therefore, we propose recommending high utility queries, that is, useful queries with more relevant documents, rather than similar ones. In this paper, we first construct a query-reformulation graph that consists of query nodes, satisfactory document nodes, and interruption node. Then, we apply an absorbing random walk on the query-reformulation graph and model the document utility with the transition probability from initial query to the satisfactory document. At last, we propagate the document utilities back to queries and rank candidate queries with their utilities for recommendation. Extensive experiments were conducted on real query logs, and the experimental results have shown that our method significantly outperformed the state-of-the-art methods in recommending high utility queries.

scholarly journals Personalised Information Recommender Using Framework for Ontology Alignment Among Digital Libraries

2016 ◽  
Vol 36 (4) ◽  
Author(s):  
Anirban Chakrabarty ◽  
Sudipta Roy
Keyword(s):  
Digital Libraries ◽  
User Profile ◽  
Context Aware ◽  
Human Intervention ◽  
Ontology Alignment ◽  
User Interest ◽  
Search Results ◽  
Efficient Information ◽  
Alignment Model ◽  
Multiple Ontologies

In the digital erantology is considered as one of the powerful tools for knowledge representation and efficient information retrieval. Ontology alignment is a process that discovers mapping between source and target ontologies, where each mapping is a relationship based on some similarity measure. This paper, has presented a new context aware alignment approach that needs little human intervention and it can map multiple ontologies to generate user interest dynamically. The objective is to design and develop an ontology alignment model that provides more benefits to its stakeholders in sharing resources and searching across digital libraries based on priorities of users. The experimental results evidently indicate significant improvement in search results when user profile and navigational pattern ontologies are aligned with digital library ontology.

scholarly journals Peringkasan Dokumen Berdasarkan Metode Semantic Sebaran Kalimat

2017 ◽  
Vol 8 (1) ◽  
Author(s):  
Aminul Wahib ◽  
Dita Lupita Sari
Keyword(s):  
Semantic Relation ◽  
Experimental Results ◽  
Semantic Meaning ◽  
Distribution Method ◽  
Average Performance ◽  
Search Results ◽  
Abstract Sentence ◽  
New Strategies

Abstract. Sentence distribution method performs weighting based on the sentence distribution without taking the semantic meaning of the sentence spread into account. In fact, the semantic relation between sentences is believed to increase the relevance of the search results document. This study proposes new strategies to summarize documents using the semantic sentence distribution method in an effort to improve the quality of the summary. The experimental results show that the proposed method has better performance with the average performance ROUGE-1 0.412, an increase of 1,9% compared to "Sentence distribution method" and ROUGE-2 by 4,7% compared to 0.127 "sentence distribution method".Keywords: Semantic Sentence Distribution, Summarizing Document, ROUGE. Abstrak. Peringkasan dokumen menggunakan metode sebaran kalimat terbukti memiliki hasil yang lebih baik jika dibanding dengan penelitian-penelitian sebelumnya. Metode tersebut melakukan pembobotan kalimat berdasarkan sebaran kalimat tanpa memperhitungkan makna semantic kalimat yang tersebar. Faktanya hubungan semantic antar kalimat telah terbukti mampu meningkatkan relevansi hasil dalam pencarian dokumen. Penelitian ini mengajukan strategi baru dalam peringkasan dokumen yaitu menggunakan metode semantic sebaran kalimat sebagai upaya untuk meningkatkan kualitas hasil ringkasan. Hasil eksperimen didapatkan bahwa metode yang diusulkan memiliki performa lebih baik dengan capaian rata-rata ROUGE-1 0,412, meningkat 1,9% dibanding metode sebaran kalimat dan ROUGE-2 0,127 meningkat 4,7% dibanding metode sebaran kalimat.Kata Kunci: Semantic Sebaran Kalimat, Peringkasan Dokumen, ROUGE.

scholarly journals Towards crowd-aware indoor path planning

2021 ◽  
Vol 14 (8) ◽  
pp. 1365-1377
Author(s):  
Tiantian Liu ◽  
Huan Li ◽  
Hua Lu ◽  
Muhammad Aamir Cheema ◽  
Lidan Shou
Keyword(s):  
Path Planning ◽  
Query Processing ◽  
Travel Time ◽  
Real Data ◽  
Experimental Results ◽  
Search Process ◽  
Unified Framework ◽  
Approximate Query Processing ◽  
Approximate Query ◽  
Processing Algorithms

Indoor venues accommodate many people who collectively form crowds. Such crowds in turn influence people's routing choices, e.g., people may prefer to avoid crowded rooms when walking from A to B. This paper studies two types of crowd-aware indoor path planning queries. The Indoor Crowd-Aware Fastest Path Query (FPQ) finds a path with the shortest travel time in the presence of crowds, whereas the Indoor Least Crowded Path Query (LCPQ) finds a path encountering the least objects en route. To process the queries, we design a unified framework with three major components. First, an indoor crowd model organizes indoor topology and captures object flows between rooms. Second, a time-evolving population estimator derives room populations for a future timestamp to support crowd-aware routing cost computations in query processing. Third, two exact and two approximate query processing algorithms process each type of query. All algorithms are based on graph traversal over the indoor crowd model and use the same search framework with different strategies of updating the populations during the search process. All proposals are evaluated experimentally on synthetic and real data. The experimental results demonstrate the efficiency and scalability of our framework and query processing algorithms.

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