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Bharat Bhasker

Bio: Bharat Bhasker is an academic researcher from Indian Institute of Management Lucknow. The author has contributed to research in topics: Recommender system & Cluster analysis. The author has an hindex of 10, co-authored 50 publications receiving 372 citations. Previous affiliations of Bharat Bhasker include Indian Institute of Management Raipur & STX Corporation.

Papers
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Journal ArticleDOI
01 Jul 2015
TL;DR: This work has developed a novel system that considers sequential information present in web navigation patterns, along with content information, which helps in capturing the multiple interests of users in recommendation systems.
Abstract: With the rapid growth of information technology, the current era is witnessing an exponential increase in the generation and collection of web data. Projecting the right information to the right person is becoming more difficult day by day, which in turn adds complexity to the decision making process. Recommendation systems are intelligent systems that address this issue. They are widely used in e-commerce websites to recommend products to users. Most of the popular recommendation systems consider only the content information of users and ignore sequential information. Sequential information also provides useful insights about the behavior of users. We have developed a novel system that considers sequential information present in web navigation patterns, along with content information. We also consider soft clusters during clustering, which helps in capturing the multiple interests of users. The proposed system has utilized similarity upper approximation and singular value decomposition (SVD) for the generation of recommendations for users. We tested our approach on three datasets, the MSNBC benchmark dataset, simulated dataset and CTI dataset. We compared our approach with the first order Markov model as well as random prediction model. The results validate the viability of our approach.

92 citations

Journal ArticleDOI
TL;DR: The solution alleviates the cold start problem by integrating side information about users and items into a very deep neural network and uses a decreasing learning rate in conjunction with increasing weight decay, the values cyclically varied across epochs to further improve accuracy.
Abstract: We propose a novel deep learning hybrid recommender system to address the gaps in Collaborative Filtering systems and achieve the state-of-the-art predictive accuracy using deep learning. While collaborative filtering systems are popular with many state-of-the-art achievements in recommender systems, they suffer from the cold start problem, when there is no history about the users and items. Further, the latent factors learned by these methods are linear in nature. To address these gaps, we describe a novel hybrid recommender system using deep learning. The solution uses embeddings for representing users and items to learn non-linear latent factors. The solution alleviates the cold start problem by integrating side information about users and items into a very deep neural network. The proposed solution uses a decreasing learning rate in conjunction with increasing weight decay, the values cyclically varied across epochs to further improve accuracy. The proposed solution is benchmarked against existing methods on both predictive accuracy and running time. Predictive Accuracy is measured by Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and R-squared. Running time is measured by the mean and standard deviation across seven runs. Comprehensive experiments are conducted on several datasets such as the MovieLens 100 K, FilmTrust, Book-Crossing and MovieLens 1 M. The results show that the proposed technique outperforms existing methods in both non-cold start and cold start cases. The proposed solution framework is generic from the outperformance on four different datasets and can be leveraged for other ratings prediction datasets in recommender systems.

86 citations

Journal ArticleDOI
01 Jan 2005
TL;DR: The procedure described here can be used by virtual buying agents for generating a hierarchical classification based on buyer's preference and a numerical example is illustrated to highlight the procedure.
Abstract: In this paper, a methodology has been introduced as a decision support tool to the consumers in the Internet business. This decision support tool takes into account the multiple attributes of the product, analyses them with respect to the consumer's desire, and finally classifies these products into different hierarchical levels as per the consumer's level of preference. The product attributes, which are in general conflicting, imprecise, and non-commensurable in nature, are well handled here by using the concepts of fuzzy logic. Concepts of linguistic quantifier are used to quantify the qualitatively defined items and also to classify the products into different preference levels as required by the customer. Classification of the products into preference levels in any business, particularly, in the business through the Internet, gives a boost to the customer and helps him in a final product choice. The procedure described here can be used by virtual buying agents for generating a hierarchical classification based on buyer's preference. At the end, a numerical example is illustrated to highlight the procedure.

48 citations

Journal ArticleDOI
TL;DR: The key contribution of this paper is the combination of features from both a CNN and a bi-directional LSTM into a single architecture with a single optimizer, which beats existing benchmarks and scales to large datasets.
Abstract: Review sentiment influences purchase decisions and indicates user satisfaction. Inferring the sentiment from reviews is an essential task in Natural Language Processing and has managerial implications for improving customer satisfaction and item quality. Traditional approaches to polarity classification use bag-of-words techniques and lexicons combined with machine learning. These approaches suffer from an inability to capture semantics and context. We propose a Deep Learning solution called OSLCFit (Organic Simultaneous LSTM and CNN Fit). In our architecture, we include all the components of a CNN until but not including the final fully connected layer and do the same in case of a bi-directional LSTM. The final fully connected layer in our architecture consists of fixed length features from the CNN, and features for both variable length and temporal dependencies from the bi-directional LSTM. The solution fine-tunes Language Model embeddings for the specific task of polarity classification using transfer learning, enabling the capture of semantics and context. The key contribution of this paper is the combination of features from both a CNN and a bi-directional LSTM into a single architecture with a single optimizer. This combination forms an organic combination and uses embeddings fine-tuned to the reviews for the specific purpose of sentiment polarity classification. The solution is benchmarked on six different datasets such as SMS Spam, YouTube Spam, Large Movie Review Corpus, Stanford Sentiment Treebank, Amazon Cellphone & Accessories and Yelp, where it beats existing benchmarks and scales to large datasets. The source code is available for the purposes of reproducible research on GitHub. 1

27 citations

Journal ArticleDOI
TL;DR: A novel meta-path based framework, HeteClass, for transductive classification of target type objects, which is flexible to utilize any suitable classification algorithm for transductionive classification and can be applied on heterogeneous information networks with arbitrary network schema.
Abstract: Transductive classification using labeled and unlabeled objects in a heterogeneous information network for knowledge extraction is an interesting and challenging problem. Most of the real-world networks are heterogeneous in their natural setting and traditional methods of classification for homogeneous networks are not suitable for heterogeneous networks. In a heterogeneous network, various meta-paths connecting objects of the target type, on which classification is to be performed, make the classification task more challenging. The semantic of each meta-path would lead to the different accuracy of classification. Therefore, weight learning of meta-paths is required to leverage their semantics simultaneously by a weighted combination. In this work, we propose a novel meta-path based framework, HeteClass, for transductive classification of target type objects. HeteClass explores the network schema of the given network and can also incorporate the knowledge of the domain expert to generate a set of meta-paths. The regularization based weight learning method proposed in HeteClass is effective to compute the weights of symmetric as well as asymmetric meta-paths in the network, and the weights generated are consistent with the real-world understanding. Using the learned weights, a homogeneous information network is formed on target type objects by the weighted combination, and transductive classification is performed. The proposed framework HeteClass is flexible to utilize any suitable classification algorithm for transductive classification and can be applied on heterogeneous information networks with arbitrary network schema. Experimental results show the effectiveness of the HeteClass for classification of unlabeled objects in heterogeneous information networks using real-world data sets.

27 citations


Cited by
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01 Jan 2002

9,314 citations

01 Aug 2000
TL;DR: Assessment of medical technology in the context of commercialization with Bioentrepreneur course, which addresses many issues unique to biomedical products.
Abstract: BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

4,833 citations