Abstract

A temporal database is a collection of transactions, ordered by their timestamps. Discovering periodic patterns in temporal databases has numerous applications. However, to the best of our knowledge, no work has considered mining periodic patterns in temporal databases where items have dissimilar support and periodicity, despite that this type of data is very common in real-life. Discovering periodic patterns in such non-uniform temporal databases is challenging. It requires defining (i) an appropriate measure to assess the periodic interestingness of patterns, and (ii) a method to efficiently find all periodic patterns. While a pattern-growth approach can be employed for the second sub-task, the first sub-task has to the best of our knowledge not been addressed. Moreover, how these two tasks are combined has significant implications. In this paper, we address this challenge. We introduce a model to assess the periodic interestingness of patterns in databases having a non-uniform item distribution, which considers that periodic patterns may have different period and minimum number of cyclic repetitions. Moreover, the paper introduces a pattern-growth algorithm to efficiently discover all periodic patterns. Experimental results demonstrate that the proposed algorithm is efficient and the proposed model may be utilized to find prior knowledge about event keywords and their associations in Twitter data.

Abstract

Time and frequency are two important dimensions to determine the interestingness of a pattern in a database. Periodic-frequent patterns are an important class of regularities that exist in a database with respect to these two dimensions. Current studies on periodic-frequent pattern mining have focused on discovering full periodic-frequent patterns, i.e., finding all frequent patterns that have exhibited complete cyclic repetitions in a database. However, partial periodic-frequent patterns are more common due to the imperfect nature of real-world. This paper proposes a flexible and generic model to find partial periodic-frequent patterns. A new interesting measure, periodic-ratio, has been introduced to determine the periodic interestingness of a frequent pattern by taking into account its proportion of cyclic repetitions in a database. The proposed patterns do not satisfy the anti-monotonic property. A novel pruning technique has been introduced to reduce the search space effectively. A pattern-growth algorithm to find all partial periodic-frequent patterns has also been presented in this paper. Experimental results demonstrate that the proposed model can discover useful information, and the algorithm is efficient.

Abstract

Query recommendation (QR) support search engine to provide alternative queries as a recommendation using similarity-based approaches. In the literature, orthogonal query recommendation (OQR) has been proposed to compute the diversity of QR when the user does not formulate proper queries. The OQR uses dissimilarity measure in QR to recommend completely different queries. In this paper, we propose an approach in QR by extending association rules, diverse patterns, and unbalanced concept hierarchy of search terms. We conceptualize association rules based QR, and order the rules based on confidence and diversity. Subsequently, the high ranked rules based on confidence and diversity are provided in QRs. The experimental results on real world AOL click-through dataset show that the diverse QRs improve the performance significantly

Abstract

Search queries follow a long tail distribution which results in harder management of ad space for sponsored search. During keyword auctions, advertisers also tend to target head query keywords, thereby creating an imbalance in demand for head and tail keywords. This leads to under-utilization of ad space of tail query keywords. In this paper, we have explored a mechanism that allows the advertisers to bid on concepts rather than keywords. The tail query keywords are utilized by allocating a mix of head and tail keywords related to the concept. In the literature, an effort has been made to improve sponsored search by extracting the knowledge of coverage patterns among the keywords of transactional query logs. In this paper, we propose an improved approach to allow advertisers to bid on high level concepts instead of keywords in sponsored search. The proposed approach utilizes the knowledge of levelwise coverage patterns to allocate incoming search queries to advertisers in an efficient manner by utilizing the long tail. Experimental results on AOL search query data set show improvement in ad space utilization and reach of advertisers.

Abstract

Periodic-frequent patterns are an important class of regularities which exists in a transactional database. A frequent pattern is called periodic-frequent if it appears at regular intervals in a transactional database. In the literature, a model of periodic-frequent patterns was proposed and pattern growth like approaches to extract patterns are being explored. In these approaches, a periodic-frequent pattern tree is built in which a transaction-id list is maintained at each path’s tail-node. As the typical size of transactional database is very huge in the modern e-commerce era, extraction of periodic-frequent patterns by maintaining transaction-ids in the tree requires more memory.In this paper, to reduce the memory requirements, we introduced a notion of period summary by capturing the periodicity of the patterns in a sequence of transaction-ids. While building the tree,the period summary of the transactions is computed and stored at the tail-node of the tree instead of the transaction-ids. We have also proposed a merging framework for period summaries for mining periodic-frequent patterns. The performance could be improved significantly as the memory required to store the period summaries is significantly less than the memory required to store the transaction-id list. Experimental results show that the pro-posed approach reduces the memory consumption significantly and also improves the runtime efficiency considerably over the existing approaches.

Abstract

Periodic-frequent pattern mining involves finding all frequent patterns that have occurred at regular intervals in a transactional database. The basic model considers a pattern as periodic-frequent, if it satisfies the user-specified minimum support (min Sup) and maximum periodicity (maxP er) constraints. The usage of a single minSupandmaxP erfor an entire database leads to the are-item problem.When confronted with this problem in real-world applications, researchers have tried to address it using the item-specific minSupandmaxP er con-straints. It was observed that this extended model still generates a sig-nificant number of uninteresting patterns, and moreover, suffers from the issue of specifying item-specific minSupandmaxP er constraints.This paper proposes a novel model to address the rare-item problem in periodic-frequent pattern mining. The proposed model considers a pat-tern as interesting if its support and periodicity are close to that of its individual items. The al l-confidence is used as an interestingness measure to filter out uninteresting patterns in support dimension. In addition, anew interestingness measure, called periodic-al l-confidence, is being pro-posed to filter out uninteresting patterns in periodicity dimension. We have proposed a model by combining both measures and proposed a pattern-growth approach to resolve the rare-item problem and extract interesting periodic-frequent patterns. Experimental results show that the proposed model is efficient

Abstract

Building efficient search systems to extract relevant in-formation from a huge volume of legal judgments is a research is-sue. In the literature, efforts are being made to build efficient search systems in the legal domain by extending information retrieval approaches. We are making efforts to investigate improved approaches to extract relevant legal judgments for a given input judgment by exploiting text and citation information of legal judgments. Typically,legal judgments are very large text documents and contain several intricate legal concepts. In this paper, we analyze how the paragraph-level and citation information of the judgments could be exploited for retrieving relevant legal judgments for the given judgment. In this paper, we have proposed improved ranking approach to find the relevant legal judgments of a given judgment based on the similarity be-tween the paragraphs of the judgments by employing Okapi retrieval model and citation information. The user evaluation study on legal judgments data set delivered by Supreme Court of India shows that the proposed approach improves the ranking performance over the baseline approach. Overall, the analysis shows that there is a scope to exploit the paragraph-level and citation information of the judgments to improve the search performance.

Abstract

Recommender system (RS) helps customers to select appropriate products from millions of products and has become a key component in e-commerce systems. Collaborative filtering (CF) based approaches are widely employed to build RSs. In CF, recommendation to the target user is computed after forming the corresponding neighbour-hood of users. Neighborhood of a target user is extracted based on the similarity between the product rating vector of the target user and the product rating vectors of individual users. In CF, the methodology employed for neighborhood formation influences the performance. In this paper, we have made an effort to improve the performance of CF by proposing a different approach to compute recommendations by considering two kinds of neighborhood. One is the neighborhood by considering the product ratings of the user as a single vector and the other is based on the neighborhood of the corresponding virtual users. For the target user,the virtual users are formed by dividing the ratings based on the category of products. We have proposed a combined approach to compute better recommendations by considering both kinds of neighborhoods.The experiments results on real world Movie Lens dataset show that the proposed approach improves the performance over CF.

Abstract

Community-driven Question Answering (Q&A) platforms are gaining popularity now-a-days and the number of posts on such platforms are increasing tremendously. Thus, the challenge to keep these platforms noise-free is attracting the interest of research community. Stack Overflow is one such popular computer programming related Q&A platform. The established users on Stack Overflow have learnt the accept-able format and scope of questions in due course. Even if their questions get closed, they are aware of the required ed-its, therefore the chances of their questions being reopened increases. On the other hand, non-established users have not adapted to the Stack Overflow system and find difficulty in editing their closed questions. In this work, we aim to identify features which help differentiate editing approaches of established and non-established users, and motivate the need of recommendation model. Such a recommendation model can assist every user to edit their closed questions leveraging the edit-style of the established users of the platform.

Abstract

n the banner advertising scenario, an advertiser aims to reach the maximum number of potential visitors and a publisher tries to meet the requests of increased number of advertisers to maximize the revenue. In the literature, a model was introduced to extract the knowledge of coverage patterns from transactional database. In this paper, we propose an ad slots allocation approach by extending the notion of coverage patterns to select distinct sets of ad slots to meet the requests of multiple advertisers. The preliminary experimental results on a real world dataset show that the proposed approach meets the requests of increased number of advertisers when compared with the baseline approach of allocation.