List of Articles Click

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  1 - Learning to Rank for the Persian Web Using the Layered Genetic Programming
  Amir Hosein Keyhanipour
  Learning to rank (L2R) has emerged as a promising approach in handling the existing challenges of Web search engines. However, there are major drawbacks with the present learning to rank techniques. Current L2R algorithms do not take into account to the search behavio More

  Learning to rank (L2R) has emerged as a promising approach in handling the existing challenges of Web search engines. However, there are major drawbacks with the present learning to rank techniques. Current L2R algorithms do not take into account to the search behavior of the users embedded in their search sessions’ logs. On the other hand, machine-learning as a data-intensive process requires a large volume of data about users’ queries as well as Web documents. This situation has made the usage of L2R techniques questionable in the real-world applications. Recently, by the use of the click-through data model and based on the generation of click-through features, a novel approach is proposed, named as MGP-Rank. Using the layered genetic-programming model, MGP-Rank has achieved noticeable performance on the ranking of the English Web content. In this study, with respect to the specific characteristics of the Persian language, some suitable scenarios are presented for the generation of the click-through features. In this way, a customized version of the MGP-Rank is proposed of the Persian Web retrieval. The evaluation results of this algorithm on the dotIR dataset, indicate its considerable improvement in comparison with major ranking methods. The improvement of the performance is particularly more noticeable in the top part of the search results lists, which are most frequently visited by the Web users. Manuscript profile
• Open Access Article
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  2 - Effective Query Recommendation with Medoid-based Clustering using a Combination of Query, Click and Result Features
  Elham Esmaeeli-Gohari Sajjad Zarifzadeh
  10.7508/jist.2020.01.004
  Query recommendation is now an inseparable part of web search engines. The goal of query recommendation is to help users find their intended information by suggesting similar queries that better reflect their information needs. The existing approaches often consider the More

  Query recommendation is now an inseparable part of web search engines. The goal of query recommendation is to help users find their intended information by suggesting similar queries that better reflect their information needs. The existing approaches often consider the similarity between queries from one aspect (e.g., similarity with respect to query text or search result) and do not take into account different lexical, syntactic and semantic templates exist in relevant queries. In this paper, we propose a novel query recommendation method that uses a comprehensive set of features to find similar queries. We combine query text and search result features with bipartite graph modeling of user clicks to measure the similarity between queries. Our method is composed of two separate offline (training) and online (test) phases. In the offline phase, it employs an efficient k-medoids algorithm to cluster queries with a tolerable processing and memory overhead. In the online phase, we devise a randomized nearest neighbor algorithm for identifying most similar queries with a low response-time. Our evaluation results on two separate datasets from AOL and Parsijoo search engines show the superiority of the proposed method in improving the precision of query recommendation, e.g., by more than 20% in terms of p@10, compared with some well-known algorithms. Manuscript profile
• Open Access Article
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  3 - A Mathematical Model for Customer Behavior Prediction Based on Click Stream Analysis
  M. M. Sepehri F. Mahdavi Pajouh
  Click stream analysis is known as an effective method for customer’s viewing route prediction in a particular web site. Predicting Customer viewing behavior provides considerable advantages in different areas such as e-commerce, e-business and customer relationship mana More

  Click stream analysis is known as an effective method for customer’s viewing route prediction in a particular web site. Predicting Customer viewing behavior provides considerable advantages in different areas such as e-commerce, e-business and customer relationship management. This paper aims to provide a 0-1 mathematical model based on Markov models for evaluating the most probable viewing route of a customer in a website. This problem can be formulated as an especial case of well-known Prize Collecting Traveling Salesman Problem (PCTSP) which is a NP-hard problem and its sub tour elimination constraints are increased drastically by increasing the model parameters. Also an effective algorithm is introduced in this paper to solve this NP-hard model. For model validation, the proposed model was implemented by using the log files of a university web site server for 20 different users. Comparison of the results with commonly used Giudici algorithm shows that the proposed model yields better and exacter solutions. Manuscript profile