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A3CRank: An Adaptive Ranking method based on Connectivity, Content and Click-through data Ali Mohammad Zareh Bidoki, Pedram Ghodsnia, Nasser Yazdani, Farhad Oroumchian Abstract – Due to the proliferation and abundance of information on the web, ranking algorithms play an important role in web search. Currently, there are some ranking algorithms based on content and connectivity such as PageRank and BM25. Unfortunately, these algorithms have low precision and are not always satisfying for users. In this paper, we propose an adaptive method based on the content, connectivity and click-through data triple, called A3CRank. The aggregation idea of meta search engines has been used to aggregate ranking algorithms such as PageRank, BM25, TF-IDF. We have used reinforcement learning to incorporate user behavior and find a measure of user satisfaction for each ranking algorithm. Furthermore, OWA, an aggregation operator is used for merging the results of the various ranking algorithms. A3CRank adapts itself with user needs and makes use of user clicks to aggregate the results of ranking algorithms. A3Crank is designed to overcome some of the shortcomings of existing ranking algorithms by combining them together and producing an overall better ranking criterion. Experimental results indicate that A3CRank outperforms all other single ranking algorithms in P@n and NDCG measures. We have used 130 queries on University of California at Berkeley’s web to train and evaluate our method. Information Processing and Management, Volume 46, Issue 2, March 2010, Pages 159-169. |
FICA: A Novel Crawling algorithm based on reinforcement learning Ali Mohammad Zareh Bidoki, Nasser Yazdani, Pedram Ghodsnia Abstract – Web is a huge and highly dynamic environment which is growing exponentially in the content and structure. Also no search engine can cover whole of the web, thus it has to focus on the most valuable pages for crawling. So an efficient crawling algorithm for retrieving the most important pages has remained still as a challenging issue. Several algorithms like PageRank and OPIC have been proposed. Unfortunately, they have high time complexity. In this paper, an intelligent crawling algorithm based on reinforcement learning, called FICA is proposed that models a real surfing user. The priority for crawling pages is based on a concept which we name it as logarithmic distance. FICA is easy to implement and its time complexity is O(E*logV) where V and E are the number of nodes and edges in the web graph respectively. Comparison of the FICA with other proposed algorithms shows that FICA outperforms them in discovering highly important pages. Furthermore, FICA computes the importance (ranking) of each page during the crawling process. Thus, we can also use FICA as a ranking method for computation of page importance. An adaptive version of FICA is also proposed that adjusts dynamically with changes in the web graph. We have used UK’s web graph for our experiments. International Journal of Web Intelligence and Agent Systems, Vol 7, No. 4, pp. 363-373, September 2009. |
WPR: A Weighted Approach to PageRank Pedram Ghodsnia, Ali Mohammad Zareh Bidoki, Naser Yazdani Abstract – The PageRank algorithm which is used by google as a successful ranking algorithm for ranking its results can be interpreted as leveraging the recommendations of all the other page creators on the web, about how important a page is. But it does not take advantage of the recommendations of page visitors. In PageRank, every page creator propagates the importance score of her/his page to its outgoing links uniformly. In this paper a revised version of PageRank called Weighted PageRank (WPR) is proposed in which this uniform propagation is transformed into a weighted one. These weights are assigned to outgoing links based on the average opinion of page visitors about the importance of pages. This opinion is recorded from search engine logs indicating which search results were clicked most. It is demonstrated that our approach is simply applicable without any significant extra time and storage costs compared to PageRank. International review on Computer and Software (IRECOS), Vol. 3, No. 1, pp. 99-109, January 2008. |
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Statistical Evaluation of effect of Some Persian Language Characteristics in Recall Factor of Search Engine Results Pedram Ghodsnia, Ali Mohammad Zareh Bidoki, Naser Yazdani Abstract – With growth of Farsi web content in recent years, Farsi language has been turned into the primar language of Iranians in communication, learning and interchange of ideas and thoughts in Internet. In spite of recent studies about challenges and restrictions of Farsi language in quality of search results in search engines, there is no exact statistical analysis about the role of each problem in decreasing the quality of search results. In this paper, using a real collection of Farsi web pages, we present some experimental results about the role of some of the most important problems and restrictions of Farsi language in decreasing the recall factor of search results in search engines. In Proceeding of the 13th National CSI Computer Conference, March 9-11, 2008, Kish Island, Persian Gulf, Iran. |
FICA: A Fast Intelligent Crawling Algorithm
Ali Mohammad Zareh Bidoki, Nasser Yazdani, Pedram Ghodsnia
Abstract – Due to the proliferation and highly dynamic nature of the web, an efficient crawling and ranking algorithm for retrieving the most important pages has remained as a challenging issue. Several algorithms like PageRank and OPIC have been proposed. Unfortunately, they have high time complexity. In this paper, an intelligent crawling algorithm based on reinforcement learning, called FICA is proposed that models a real surfing user. The priority for crawling pages is based on a concept which we name as logarithmic distance. FICA is easy to implement and its time complexity is O(E*logV) where V and E are the number of nodes and edges in the web graph respectively. Comparison of the FICA with other proposed algorithms shows that FICA outperforms them in discovering highly important pages. Furthermore, FICA computes the importance (ranking) of each page during the crawling process. Thus, we can also use FICA as a ranking method for computation of page importance. We have used UK’s web graph for our experiments.
In Proceeding of the IEEE/WIC/ACM International Conference on Web Intelligence, November 2-5, 2007, Silicon Valley, USA. |
A Punishment/Reward Based Approach to Ranking Pedram Ghodsnia, Ali Mohammad Zareh Bidoki, Nasser Yazdani Abstract –One of the important challenges in current search engines is dealing with the "rich get richer" problem. In popularity-based ranking algorithms like PageRank, due to considering the structure of the web as the measure for ranking the pages, newly-created but highly-qualified pages are effectively disregarded shoot out, and can take a very long time before becoming popular. In this paper we present a new punishment/reward based approach that adds a new dimension to the PageRank model for reducing the effect of the rich get richer problem using implicit feedback of visitors. In this approach, in addition to considering the structure of links as a page-creator's point of view, we use the page-visitor's view as an important parameter to improve the accuracy of the PageRank algorithm. In Proceeding of the ACM Second International Conference on Scalable Information Systems(INFOSCALE 2007), June 6-8, 2007, Suzhou, China. |
Novel Way of Determining the Optimal Location of a Fragment in a DDBS: BGBR Ashkan Bayati, Pedram Ghodsnia, Masood Rahgozar, Reza Baseda Abstract –This paper addresses the problem of determining the optimal location to place a fragment (object) in a distributed non-replicated database. The algorithm defined takes into consideration a changing environment with changing access patterns. This paper contributes by allocating data fragments to their optimal location, in a distributed network, based on the access patterns for that fragment. The mechanism for achieving this optimality relies on knowing the costs involved in moving data fragments from one site to the other. Embedded into the algorithm is a mechanism to prioritize fragments so that if there is a limited space on a specific node where many fragments are chosen to be allocated the ones with higher priority are placed before the lower priority fragments. In Proceeding of the IEEE ICSNC06, Tahiti, French Polynesia, 2006. |
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| Dynamic Data Allocation in Lazily Replicated Database Systems Shahin Kamali, Pedram Ghodsnia, Khuzaima Daudjee Abstract Fragment allocation is an important problem in modern distributed database systems. The goal of fragment allocation is to allocate data fragments to a network of sites so as to minimize the overall data transmission cost incurred to satisfy queries. We consider the problem of fragment allocation and address both placement and replication issues in an integrated approach. While replication can improve system performance via increased locality, excessive replication can incur extra transmission cost to maintain consistency. A comprehensive model that takes into account network topology, fragment correlation and data access patterns is presented. Based on this model, two algorithms are proposed to find near-optimal dynamic allocation solutions. Experimental results show the efficacy of the proposed solutions. Submitted to the 29th IEEE International Symposium on Reliable Distributed Systems, Dehli, India, (Paper ID: 1569311323) |
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UTUtd 2004 Team Description Mostafa Hadian Dehkordi, Peyman Zarrineh, Pedram Ghodsnia, Farid AmirGhiasvand, Hesamaddin Torabi Dashtii Abstract –In this paper, we briefly introduce the main ideas in UTUtd2004 development. The low-level abilities employed in designing our team are mainly based on Tsinghuaeolus Team 2002. In this paper different parts of humanbrain have been described in terms of Balkenius model of learning which has been presented as an emotional learning model. The model has been used for simulating a soccer player in which player can predict and decide his next move or action. The paper further presents an approach toward GCGA and presents a brief solution. The player in this simulation is a trainable robot for a Robocup team. 8th RoboCup International competitions, 2004, Lisboa, Portugal. |
Converting REO into SMV Pedram Ghodsnia, Sadra Abedinzadeh, Ashkan Bayati, Alireza Vazifehdoost Abstract – In recent years, significant gains have been made in formally verifying systems using symbolic model checking. Reo, an exogenous coordination language, provides the abil ity to formally specify the behavior and requirements of a great range of applications. Although Reo is an extremely powerful tool for modeling, it lacks the ability for verifying the models logic. In this paper we introduce a mechanism to establish properties of hardware or software designs, which have been formally specified via Reo, using logic through the use of a model checker. The model checker we have used is NuSMV.
University of Tehran, May 2006.
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Efficient Packet Forwarding on a Multi-Processor Parallel and Distributed Shared Memory Router Pedram Ghodsnia, Ashkan Bayati Abstract – As link speed continues to grow exponentially, packet forw arding at network switches and routers is becoming a bottleneck. We felt that in order to design a high performance router capable of meeting today's industrial needs would require a close interaction between the design of the architecture and the operating system. Due to low speed capabilities of memory cards and the amount of I/O used in a router we felt the most important aspects of designing an operating system is memory and I/O management. In this paper we have combined an effective memory management and I/O management framework that reaps the benefits of the underlying architecture.
University of Tehran, May 2006. |
TLA and TLA a Survey Pedram Ghodsnia Abstract – TLA is a formal specification language based on set theory, firs-order logic, and TLA (the Temporal Logic of Actions). TLA is a version of temporal logic developed for describing and reasoning about concurrent and reactive systems. TLA includes modules and ways of combining them to form larger specifications. In this paper we are going to provide a brief introduction to TLA and TLA , present the currently available tools for model checking with TLA and review some related works and case studies of the usage of TLA for formal verification of systems.
David Cheriton School of Computer Science, University of Waterloo, July 2009. |
Others:- Pedram Ghodsnia, “Parallel Gaussian Elimination”, project report, David Cheriton School of Computer Science, University of Waterloo, July 2009.
- Pedram Ghodsnia, Performance improvement of Search Engines using analytical techniques of web graph, Master’s thesis, School of Electrical and Computer Engineering, University of Tehran, 2008.
- Pedram Ghodsnia, “A survey on Spatial Methods”, Technical report, Database Research Group, School of Electrical and Computer Engineering, University of Tehran,2006.
- Pedram Ghodsnia, "Learning Linux", Computer Javan Magazine, Volumes: 37, 38, 39 and 40, 2001.
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