Leveraging collaborative tagging for web item design

Das, Mahashweta; Das, Gautam; Hristidis, Vagelis

doi:10.1145/2020408.2020493

Cited by 12 publications

(11 citation statements)

References 13 publications

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“…Moreover, tags are directly published and discussed on the Web and may be applied to any kinds of items, even people. Collaborative tagging can play a key role in sharing content in social networks and also, it is an effective technique to solve the two problems such as, the sparsity problem and the cold start problem that happen in collaborative filtering despite, its success and popularity [25,26]. In this paper, we have developed a new method for collaborative tagging based on web page recommendation using fuzzy classifier.…”

Section: Co-published By Atlantis Press and Taylor And Francismentioning

confidence: 99%

An Approach to Enhance the Quality of Recommendation Using Collaborative Tagging

Banda¹,

Bharadwaj²

2014

IJCIS

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Collaborative labeling portrays the process by which numerous users put in metadata in the form of keywords to shared data. Nowadays, collaborative labeling has grown in reputation on the web, on sites that permit users to label bookmarks, photographs and other details. It has been recently become useful and well known as one effective way of classifying items for future search, sharing information, and filtering. So, as to predict the future search of users, we propose a novel collaborative tagging-based page recommendation algorithm using fuzzy classifier. The method consists of three phases: Grouping, Rule Generation Phase and Page Recommendation Phase. In the proposed method, we calculate the resemblance of users in selecting tags and thereby, calculate the nearest neighbors of each user and cluster them. Then, the priority of tags and items for each user is calculated for constructing a Nominal Label Matrix and Nominal Page Matrix. Finally, the fuzzy rules are generated for page recommendation. The experimentation is carried out on delicious datasets and the experimental results ensured that the proposed algorithm has achieved the maximum hit ratio of 6.6% for neighborhood size of 20, which is higher than the existing technique which obtained only 5.5%.

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Section: Co-published By Atlantis Press and Taylor And Francismentioning

confidence: 99%

An Approach to Enhance the Quality of Recommendation Using Collaborative Tagging

Banda¹,

Bharadwaj²

2014

IJCIS

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“…Note that the task of finding the best snippet that maximizes Equation 5 is difficult, even for k = 1 [2]. Hence, we introduce the idea of composite tag.…”

Section: Problem Frameworkmentioning

confidence: 99%

“…There are several research challenges associated with finding the best ad [10].We consider the novel task of snippet generation by leveraging collaborative tagging feedback. Collaborative Tagging Mining: The dynamics of social tagging has been an active research area in recent years, with several papers focusing on leveraging collaborative tagging feedback for improving recommendation [3], designing new products [2], etc. Several paper focuses on the task of tag prediction, with [7] using Naive Bayes for tag prediction.…”

Section: Related Workmentioning

confidence: 99%

“…influence tagging behavior. We refer to related literature [2] and choose to focus on content-based tagging feedback to identify the salient features of an item.…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, the task of finding the best set of attributes maximizing the probability of an item snippet receiving all desirable tags requires us to exhaustively evaluate an exponential number of combinations. In this paper, we consider the very popular Naive Bayes Classifier with the simplistic conditional independence assumption for tag and attribute modeling because of its success in [2]. We introduce the idea of composite tag, a single tag representing all the desirable tags that alleviates the computational challenges associated with finding the best snippet for an item.…”

Section: Introductionmentioning

confidence: 99%

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Generating informative snippet to maximize item visibility

Das

Rahman

Das

et al. 2013

Proceedings of the 22nd ACM International Conference on Information &Amp; Knowledge Management

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The widespread use and growing popularity of online collaborative content sites has created rich resources for users to consult in order to make purchasing decisions on various items such as e-commerce products, restaurants, etc. Ideally, a user wants to quickly decide whether an item is desirable, from the list of items returned as a result of her search query. This has created new challenges for producers/manufacturers (e.g., Dell) or retailers (e.g., Amazon, eBay) of such items to compose succinct summarizations of web item descriptions, henceforth referred to as snippets, that are likely to maximize the items' visibility among users. We exploit the availability of user feedback in collaborative content sites in the form of tags to identify the most important item attributes that must be highlighted in an item snippet. We investigate the problem of finding the top-k best snippets for an item that are likely to maximize the probability that the user preference (available in the form of search query) is satisfied. Since a search query returns multiple relevant items, we also study the problem of finding the best diverse set of snippets for the items in order to maximize the probability of a user liking at least one of the top items. We develop an exact top-k algorithm for each of the problem and perform detailed experiments on synthetic and real data crawled from the web to to demonstrate the utility of our problems and effectiveness of our solutions.

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