scispace - formally typeset
Search or ask a question
Author

Sujoy Datta

Bio: Sujoy Datta is an academic researcher. The author has an hindex of 1, co-authored 1 publications receiving 54 citations.

Papers
More filters
Journal ArticleDOI
TL;DR: This survey paper compare's and details the various type of recommender system and popular recommendation algorithms and its uses.
Abstract: Recommendation systems have become extremely common in recent years. It helps the customer to discover information and settle on choices where they do not have the required learning to judge a specific item. It can be utilized as a part of different diverse approaches to encourage its customer with effective information sorting. It is a software tool and techniques that provide suggestion based on the customer's taste to discover new appropriate thing for them by filtering personalized information based on the user's preferences from a large volume of information. Users taste and preferences should be constructed accurately in order to provide most relevant suggestions. This survey paper compare's and details the various type of recommender system and popular recommendation algorithms and its uses.

110 citations


Cited by
More filters
Proceedings ArticleDOI
Shi-Yong Chen1, Yang Yu1, Qing Da2, Jun Tan2, Haikuan Huang2, Haihong Tang2 
19 Jul 2018
TL;DR: This paper proposes two techniques to alleviate the unstable reward estimation problem in dynamic environments, the stratified sampling replay strategy and the approximate regretted reward, which address the problem from the sample aspect and the reward aspect, respectively.
Abstract: Deep reinforcement learning has shown great potential in improving system performance autonomously, by learning from iterations with the environment. However, traditional reinforcement learning approaches are designed to work in static environments. In many real-world problems, the environments are commonly dynamic, in which the performance of reinforcement learning approaches can degrade drastically. A direct cause of the performance degradation is the high-variance and biased estimation of the reward, due to the distribution shifting in dynamic environments. In this paper, we propose two techniques to alleviate the unstable reward estimation problem in dynamic environments, the stratified sampling replay strategy and the approximate regretted reward, which address the problem from the sample aspect and the reward aspect, respectively. Integrating the two techniques with Double DQN, we propose the Robust DQN method. We apply Robust DQN in the tip recommendation system in Taobao online retail trading platform. We firstly disclose the highly dynamic property of the recommendation application. We then carried out online A/B test to examine Robust DQN. The results show that Robust DQN can effectively stabilize the value estimation and, therefore, improves the performance in this real-world dynamic environment.

130 citations

Proceedings ArticleDOI
01 Feb 2019
TL;DR: Survey about recommendation systems, techniques, challenges the face recommender systems and list some research papers solve these challenges are introduced.
Abstract: Today’s Recommender system is a relatively new area of research in machine learning. The recommender system’s main idea is to build relationship between the products, users and make the decision to select the most appropriate product to a specific user. There are four main ways that recommender systems produce a list of recommendations for a user – content-based, Collaborative, Demographic and hybrid filtering. In content-based filtering the model uses specifications of an item in order to recommend additional items with similar properties. Collaborative filtering uses past behavior of the user like items that a user previously viewed or purchased, In summation to any ratings the user gave those items rate and similar conclusions made by other user’s items list. To predicts items that the user may find interesting. Demographic filtering is view user profile data like age category, gender, education and living area to find similarities with other profiles to get a new recommender list. Hybrid filtering combines all three filtering techniques. This paper introduces survey about recommendation systems, techniques, challenges the face recommender systems and list some research papers solve these challenges.

77 citations

Proceedings ArticleDOI
01 Sep 2020
TL;DR: An overview of various multi-layers IoT architectures, and IoT-based recommendation systems with an emphasis on their advantages, disadvantages, application domains, and validation metrics for quality assessment is provided.
Abstract: Internet of Things (IoT) has emerged in many industries, such as health care, transportation, agriculture, manufacturing, smart homes, to name a few. It paves the path for massive applications on the user level to enhance the quality of life or service, and on the decision-makers’ level to provide a sustainable increase in revenue. IoT principally connects different physical objects (e.g., sensors) and enables them to communicate, collect, and share data. In the Era of IoT, Recommendation systems provide personalized recommendations based on the user's historical datasets collected from the IoT devices. These recommendations enable an efficient decision-making process by suggesting relevant products, resources, and information. This paper provides an overview of various multi-layers IoT architectures, and IoT-based recommendation systems with an emphasis on their advantages, disadvantages, application domains, and validation metrics for quality assessment.

21 citations

Journal ArticleDOI
TL;DR: A configurable approach for recommendations which determines the suitable recommendation method for each field based on the characteristics of its data, the method includes determining the suitable technique for selecting a representative sample of the provided data.
Abstract: This study presents a configurable approach for recommendations which determines the suitable recommendation method for each field based on the characteristics of its data, the method includes determining the suitable technique for selecting a representative sample of the provided data. Then selecting the suitable feature weighting measure to provide a correct weight for each feature based on its effect on the recommendations. Finally, selecting the suitable algorithm to provide the required recommendations. The proposed configurable approach could be applied on different domains. The experiments have revealed that the approach is able to provide recommendations with only 0.89 error rate percentage.

21 citations

01 Jan 2016
TL;DR: This paper proposes a scalable clustering-based CF method that can help provide a balance by re-locating elements in the cluster model and improves the MAE and the response time.
Abstract: The large amount of information that is currently being collected (the so-called “big data”), have resulted in model-based Collaborative Filtering (CF) methods to encountering limitations, e.g., the sparsity problem and the scalability problem. It is difficult for model-based CF methods to address the scalability-performance trade-off. Therefore, we propose a scalable clustering-based CF method in this paper that can help provide a balance by re-locating elements in the cluster model. The proposed method is evaluated by performing a comparison against existing methods in terms of measurements for the Mean Absolute Error (MAE) and response time to assess the performance and scalability. The experimental results show that the proposed method improves the MAE and the response time by 50.79% and 48.25%, respectively. * This paper is significantly extended from an earlier version presented at the 3 International Conference on Smart Media Applications in December 2014. † These authors contributed equally to this work as the first author. ‡ Corresponding author. O-J. Lee et al. Adaptive Collaborative Filtering Based on Scalable Clustering for Recommender Systems – 180 –

18 citations