arXiv subjects

Mahashweta Das

Publications and source records attributed to Mahashweta Das.

21 records · Page 2Linked to original sources

arXiv2016

Web Item Reviewing Made Easy By Leveraging Available User Feedback

The widespread use of online review sites over the past decade has motivated businesses of all types to possess an expansive arsenal of user feedback to mark their reputation. Though a significant proportion of purchasing decisions are driven by average rating, detailed reviews are critical for activities like buying expensive digital SLR camera. Since writing a detailed review for an item is usually time-consuming, the number of reviews available in the Web is far from many. Given a user and an item our goal is to identify the top-$k$ meaningful phrases/tags to help her review the item easily. We propose general-constrained optimization framework based on three measures - relevance (how well the result set of tags describes an item), coverage (how well the result set of tags covers the different aspects of an item), and polarity (how well sentiment is attached to the result set of tags). By adopting different definitions of coverage, we identify two concrete problem instances that enable a wide range of real-world scenarios. We develop practical algorithms with theoretical bounds to solve these problems efficiently. We conduct experiments on synthetic and real data crawled from the web to validate the effectiveness of our solutions.

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arXiv2013

Top-K Product Design Based on Collaborative Tagging Data

The widespread use and popularity of collaborative content sites (e.g., IMDB, Amazon, Yelp, etc.) has created rich resources for users to consult in order to make purchasing decisions on various products such as movies, e-commerce products, restaurants, etc. Products with desirable tags (e.g., modern, reliable, etc.) have higher chances of being selected by prospective customers. This creates an opportunity for product designers to design better products that are likely to attract desirable tags when published. In this paper, we investigate how to mine collaborative tagging data to decide the attribute values of new products and to return the top-k products that are likely to attract the maximum number of desirable tags when published. Given a training set of existing products with their features and user-submitted tags, we first build a Naive Bayes Classifier for each tag. We show that the problem of is NP-complete even if simple Naive Bayes Classifiers are used for tag prediction. We present a suite of algorithms for solving this problem: (a) an exact two tier algorithm(based on top-k querying techniques), which performs much better than the naive brute-force algorithm and works well for moderate problem instances, and (b) a set of approximation algorithms for larger problem instances: a novel polynomial-time approximation algorithm with provable error bound and a practical hill-climbing heuristic. We conduct detailed experiments on synthetic and real data crawled from the web to evaluate the efficiency and quality of our proposed algorithms, as well as show how product designers can benefit by leveraging collaborative tagging information.

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arXiv2012

Who Tags What? An Analysis Framework

The rise of Web 2.0 is signaled by sites such as Flickr, del.icio.us, and YouTube, and social tagging is essential to their success. A typical tagging action involves three components, user, item (e.g., photos in Flickr), and tags (i.e., words or phrases). Analyzing how tags are assigned by certain users to certain items has important implications in helping users search for desired information. In this paper, we explore common analysis tasks and propose a dual mining framework for social tagging behavior mining. This framework is centered around two opposing measures, similarity and diversity, being applied to one or more tagging components, and therefore enables a wide range of analysis scenarios such as characterizing similar users tagging diverse items with similar tags, or diverse users tagging similar items with diverse tags, etc. By adopting different concrete measures for similarity and diversity in the framework, we show that a wide range of concrete analysis problems can be defined and they are NP-Complete in general. We design efficient algorithms for solving many of those problems and demonstrate, through comprehensive experiments over real data, that our algorithms significantly out-perform the exact brute-force approach without compromising analysis result quality.

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