Improving the Enhanced Recommended System Using Bayesian Approximation Method and Normalized Discounted Cumulative Gain

Jayashree, R.; Christy, A.

doi:10.1016/j.procs.2015.04.057

Cited by 14 publications

(8 citation statements)

References 10 publications

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“…Statistical Splay Tree Policy Filters (SSF-BSPL) [6] optimize the early rejection of unwanted flows, and the acceptance of repeated Filtering processing time for the unwanted packets reduces by arranging policy fields in descending order starting from the area with the highest rejection statistics. Normalized Discounted Cumulative Gain is a metric for measuring ranking correctness [17]. In software documentation retrieval environment application the place of recommended items in the list is important for the recommendation and normalized discounted cumulative gain (NDCG) is a frequently used metric for measuring ranking correctness, considering item ranking position [33].…”

Section: Motivation and Backgroundmentioning

confidence: 99%

“…In software documentation retrieval environment application the place of recommended items in the list is important for the recommendation and normalized discounted cumulative gain (NDCG) is a frequently used metric for measuring ranking correctness, considering item ranking position [33]. Reputation is the sum of scores given by peer contributors of the website which later ordered to find ranking among users [17]. The registered users gain more mean points than anonymous users and registered users are more trusted [17].…”

Section: Motivation and Backgroundmentioning

confidence: 99%

“…Reputation is the sum of scores given by peer contributors of the website which later ordered to find ranking among users [17]. The registered users gain more mean points than anonymous users and registered users are more trusted [17].…”

Section: Motivation and Backgroundmentioning

confidence: 99%

“…In our earlier work, we presented an online rating calculation method for reputation management which approximates expectation of contributors" performances to derive simple update rules for online ranking using Bayesian approximation method and Normalized Discounted Cumulative Gain as a metric for measuring ranking correctness [13]. DCG measures the quality of the results in a ranked list [17]. Contributors" reputations are predicted.…”

Section: Introductionmentioning

confidence: 99%

“…Contributors" reputations are predicted. Calculate users" overall rating and ranking and verify ranked contributors and their reputation scores using NDCG algorithm [17]. In this paper, we discuss user reputation-based ranking system with the splay tree and NDCG as a significant concept and develop the re-ranking with the semi-splay algorithm to prevent weak users from downfall thus, improve their ranks.…”

Section: Introductionmentioning

confidence: 99%

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Enhanced User-driven Ranking System with Splay Tree

Jayashree

Christy

2018

TELKOMNIKA

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E-learning is one of the information and communication technology products used for teaching and learning process [35]. An efficient and effective way to construct trust relationship among peer users in e-learning environment is ranking. User-driven ranking systems are b ased only on the feedb ack or rating provided b y the users. In [46][47][48] the authors provide a variety of trust and reputation methods. Certified Belief in Strength (CBS) [45] is a novel trust measurement method b ased on reputation and strength . In [38] author presents a recommendation system b ased on the relevant feedb ack review to predict the user's interests, that are ranked b ased on the recommendations history they provide previously. Users with higher rating ob tain high reputation compared to less scored users. In question answering web sites like StackOverflow, new or low scored users are ignored b y the community. This discourage them and their involvement with the community reduces further down, as power law states, alleged low users are pu shed to the b ottom of the ranking list. Avoid this condition b y encouraging less reputed users and prevent them from moving further down in ranking level. Thus, low reputed users are provided with few more chances to participate actively in the e-learning environments. A splay tree is a Binary Search Tree with self-b alancing skill. The splay tree b rings the recently accessed item to the top of the tree, thus active users are always on the top of the tree. A splay tree is used to represent user's ranks, and to semi-splay low ranked users again in the tree thus preventing them from further drowning in the ranking list. The focus of this research work is to find and enhance low reputed users in reputation system b y providing few more chances to take part actively in the e-learning environment using the splay tree. Normalized discounted cumulative gain (NDCG) acts as a decision part for identifying drowning users.

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Section: Motivation and Backgroundmentioning

confidence: 99%

Section: Motivation and Backgroundmentioning

confidence: 99%

Section: Motivation and Backgroundmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Enhanced User-driven Ranking System with Splay Tree

Jayashree

Christy

2018

TELKOMNIKA

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A novel approach to alleviate data sparsity and generate dynamic fruit recommendations from point‐of‐sale data

Gupta

Katarya

2022

Concurrency and Computation

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Summary Recommender systems have become a core part of the retail experience. Retailers often rely on recommender systems to help them drive more conversions through targeted communication and advertisements. However, recommender systems are not one size fits all. Specialized retailers require specialized recommender systems to consider various features, attributes, and dynamics about the product category. In this paper, we have proposed a novel fruit recommender system that generates dynamic recommendations while remediating the problem of data sparsity. We have developed a novel fruit recommender system that considers the temporal dynamics in the fruit market, like price fluctuations, fruit seasonality, and quality variations that occur throughout the year. To perform this task, we have used Recurrent Recommender Network (RRN), which uses the deep learning method Long Short‐Term Memory (LSTM) to implement the system model. To ensure that our work and results obtained are practical, we have worked in a real‐world setting, by tying up with a specialty fruit retailer based in New Delhi to get the real‐world Point‐of‐Sale (POS) data of consumers. The result of the study suggests our algorithm performs better than other benchmark algorithms along NDCG and RMSE metrics.

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