Browsing by Author "Goel, S."

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Aspect Based Neural Recommender Using Adaptive Prediction
(Institute of Electrical and Electronics Engineers Inc., 2023) Bhojwani, A.; Jolly, V.; Goel, S.; Anand Kumar, M.
Recommendation systems are information processing systems that analyze user behavior and make suggestions relevant to the users' interests. These systems recommend movies, products etc., based on many different factors. These recommended items are the items which are most likely in the interest of the users. This work focuses on aspects of users' textual comments to make recommendations that are relevant to users. We propose a model that recommends the items based on extracted aspects, taking the reviews and information from the items. Based on the item's aspect level importance, a user may rate them high or low, and we have modelled our recommendation system considering the same. This calculation is based on the neural co-attention mechanism. The proposed model points to various shortcomings of the model that were introduced before. We have used the datasets from amazon, which are extracted using web scraping. The existing recommendation system predicts ratings alone, while the proposed model gives rankings and ratings. The model's efficiency is checked using precision, recall and F1 score. Â© 2023 IEEE.
Investigating the "wisdom of crowds" at scale
(2015) Mysore, A.S.; Yaligar, V.S.; Ibarra, I.A.; Simoiu, C.; Goel, S.; Arvind, R.; Sumanth, C.; Srikantan, A.; Bhargav, H.S.; Pahadia, M.; Dobhal, T.; Ahmed, A.; Shankar, M.; Agarwal, H.; Agarwal, R.; Anirudh-Kondaveeti, S.; Arun-Gokhale, S.; Attri, A.; Chandra, A.; Chilukuri, Y.; Dharmaji, S.; Garg, D.; Gupta, N.; Gupta, P.; Jacob, G.M.; Jain, S.; Joshi, S.; Khajuria, T.; Khillan, S.; Konam, S.; Kumar-Kolla, P.; Loomba, S.; Madan, R.; Maharaja, A.; Mathur, V.; Munshi, B.; Nawazish, M.; Neehar-Kurukunda, V.; Nirmal-Gavarraju, V.; Parashar, S.; Parikh, H.; Paritala, A.; Patil, A.; Phatak, R.; Pradhan, M.; Ravichander, A.; Sangeeth, K.; Sankaranarayanan, S.; Sehgal, V.; Sheshan, A.; Shibiraj, S.; Singh, A.; Singh, A.; Sinha, P.; Soni, P.; Thomas, B.; Tuteja, L.; Varma-Dattada, K.; Venkataraman, S.; Verma, P.; Yelurwar, I.
In a variety of problem domains, it has been observed that the aggregate opinions of groups are often more accurate than those of the constituent individuals, a phenomenon that has been termed the "wisdom of the crowd." Yet, perhaps surprisingly, there is still little consensus on how generally the phenomenon holds, how best to aggregate crowd judgements, and how social influence affects estimates. We investigate these questions by taking a meta wisdom of crowds approach. With a distributed team of over 100 student researchers across 17 institutions in the United States and India, we develop a large-scale online experiment to systematically study the wisdom of crowds effect for 1,000 different tasks in 50 subject domains. These tasks involve various types of knowledge (e.g., explicit knowledge, tacit knowledge, and prediction), question formats (e.g., multiple choice and point estimation), and inputs (e.g., text, audio, and video). To examine the effect of social influence, participants are randomly assigned to one of three different experiment conditions in which they see varying degrees of information on the responses of others. In this ongoing project, we are now preparing to recruit participants via Amazon's Mechanical Turk.
Investigating the "wisdom of crowds" at scale
(Association for Computing Machinery, Inc acmhelp@acm.org, 2015) Mysore, A.S.; Yaligar, V.S.; Ibarra, I.A.; Simoiu, C.; Goel, S.; Arvind, R.; Sumanth, C.; Srikantan, A.; Bhargav, H.S.; Pahadia, M.; Dobhal, T.; Ahmed, A.; Shankar, M.; Agarwal, H.; Agarwal, R.; Anirudh-Kondaveeti, S.; Arun-Gokhale, S.; Attri, A.; Chandra, A.; Chilukuri, Y.; Dharmaji, S.; Garg, D.; Gupta, N.; Gupta, P.; Jacob, G.M.; Jain, S.; Joshi, S.; Khajuria, T.; Khillan, S.; Konam, S.; Kumar-Kolla, P.; Loomba, S.; Madan, R.; Maharaja, A.; Mathur, V.; Munshi, B.; Nawazish, M.; Neehar-Kurukunda, V.; Nirmal-Gavarraju, V.; Parashar, S.; Parikh, H.; Paritala, A.; Patil, A.; Phatak, R.; Pradhan, M.; Ravichander, A.; Sangeeth, K.; Sankaranarayanan, S.; Sehgal, V.; Sheshan, A.; Shibiraj, S.; Singh, A.; Singh, A.; Sinha, P.; Soni, P.; Thomas, B.; Tuteja, L.; Varma-Dattada, K.; Venkataraman, S.; Verma, P.; Yelurwar, I.
In a variety of problem domains, it has been observed that the aggregate opinions of groups are often more accurate than those of the constituent individuals, a phenomenon that has been termed the "wisdom of the crowd." Yet, perhaps surprisingly, there is still little consensus on how generally the phenomenon holds, how best to aggregate crowd judgements, and how social influence affects estimates. We investigate these questions by taking a meta wisdom of crowds approach. With a distributed team of over 100 student researchers across 17 institutions in the United States and India, we develop a large-scale online experiment to systematically study the wisdom of crowds effect for 1,000 different tasks in 50 subject domains. These tasks involve various types of knowledge (e.g., explicit knowledge, tacit knowledge, and prediction), question formats (e.g., multiple choice and point estimation), and inputs (e.g., text, audio, and video). To examine the effect of social influence, participants are randomly assigned to one of three different experiment conditions in which they see varying degrees of information on the responses of others. In this ongoing project, we are now preparing to recruit participants via Amazon's Mechanical Turk.
Secure Decentralized Carpooling Application Using Blockchain and Zero Knowledge Proof
(Science and Technology Publications, Lda, 2024) Goel, S.; Sawant, S.V.; Rudra, B.
Blockchain extends its reach far beyond cryptocurrencies such as Bitcoin, encompassing a broader spectrum of applications. It acts as a transparent, distributed, and unchangeable ledger where every participant in the network possesses a copy of the blockchain. This decentralized system secures all data and transactions through encryption, ensuring reliability. The key components of blockchain-based applications include Smart Contracts, which house the applicationâ€™s logic and operate on the blockchain. In traditional carpooling systems, centralized authorities like Uber or Ola control the entire process, collecting and managing data from both drivers and riders. However, by leveraging blockchain and smart contracts, a more secure and private carpooling system can be established, allowing riders and drivers to connect directly without intermediaries. Blockchain applications encounter challenges, primarily related to scalability and privacy. Every node in the system processing transactions limits scalability. Moreover, the practice of publishing all data at each node for processing raises privacy concerns. To tackle these issues, an approach using non-interactive proofs for off-chain computations can enhance efficiency. This approach verifies correctness without exposing private data, thus improving privacy. ZoKrates, a toolbox, simplifies this process by providing a domain-specific language (DSL), compiler, and generators for proofs and verification of Smart Contracts, streamlining complex zero-knowledge proof tasks and promoting their adoption. Â© 2024 by SCITEPRESS â€“ Science and Technology Publications, Lda.