A review on e-commerce recommender applications

Sterlin, Stefi; Sandhya, A.; Merlin, S. Sharon; Sam, B. Baron

doi:10.1109/iccpeic.2017.8290399

Cited by 2 publications

(6 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Decision Tree: Splits the data into splitting factor (based on information gain and gini index) and then the decision is made. [14] There are more algorithms followed up but we see how the recommendations are made and suggested to the user. Now let's study about the kinds of recommender system existing in the scenario.…”

Section: Existing Systemmentioning

confidence: 99%

“…The popular example for collaborative filtering is Netflix; everything which is displayed on their site is carried out by the selection of their customer, which if made regularly or frequently enough, then it gets turned in to a recommendation. [14]Netflix ranks these recommendations in such a way that highest ranking items are more visible to the user in order to be get clicked or selected by them. The advantage of the collaborative filtering is that is gives a broader exposure to the consumer for many different products and this helps in the continuity of the relationship between site and the user and hence creates a better experience.…”

Section: Existing Systemmentioning

confidence: 99%

“…It removes the problem of cold start that is a new products also gets the engagements and are pushed up and also it is free from user that is it does not need user interferences only the profile is required but this system lacks when a new user comes in as the user profile is not having enough of the information that a recommendation can be made. [14]…”

Section: Existing Systemmentioning

confidence: 99%

“…The big advantage of HiveQL is that it provides users a SQL kind of language to interact with the data and process it which is very fast to learn and easy to go with so it becomes familiar for the developer and moreover it provides us scalability and extensibility to our system. [14] The process of executing a HiveQL query in the Hive tool: 1) First, the hive query is sent to the database driver (such as ODBC, JDBC, etc) by the help of command line or web UI for its execution.…”

Section: B Hivementioning

confidence: 99%

See 3 more Smart Citations

Optimized Recommendation System for E Commerce on Product Features and User Behavior

G.¹,

Singh²

2019

IJRTE

View full text Add to dashboard Cite

Big data is a late of huge information stored in it and all we need is to dig into get the important information out of it and create a useful system which can be very helpful in improving the current scenario. There are various applications where big data is being used and even there are few fields that are learning techniques to go with big data and evaluate their work and get an improve decision. This paper particularly concentrates on the e commerce system which is highly trending on the market field. [20] E commerce also known as electronic commerce is a market place which gives you a platform to enjoy various services from both buyers as well as sellers. It is a place with various varieties are provided that can help the consumer to choose from and the buyer can get a platform where he can show case his product and get millions of the customer at the same time and he does not have to look for site all the time, it’s the system that take care of it. Now big data is playing a vital role in e commerce as it reads about user behavior and provides him a suitable product that he may need according to his behavior and query. There are various machine learning algorithms that are working on this and improving the services. [11] Basically in this paper we will read the user information and combine it with the product attributes and get a suitable suggestion for the user that will be most likely to be purchased by him. In the existing system we just look at one part of the case and give suggestion but in this paper we looked at both the sides, that is we looked after the product entities (the attributes and features that it poses) and the user behavior (the information given by the user and its previous history) that will better prediction and improve the system. Moreover for the optimized working of the system we included an enhanced version of HPCA scheduling algorithm for the Hadoop distributed file system also known as HDFS, which is very suitable for the heterogeneous system, the existing algorithm looks after the overall capacity of the node and then the tasks were assigned but here we will consider the health and the left over capacity of the nodes and arrange the queue for the same which will be refreshed all the time after the task is completed by any node.[18] The aim of the paper is to provide fast and most suitable suggestions to the users which can play a vital role in improving the sales of the company and getting the target done soon and faster

show abstract

Section: Existing Systemmentioning

confidence: 99%

Section: Existing Systemmentioning

confidence: 99%

Section: Existing Systemmentioning

confidence: 99%

Section: B Hivementioning

confidence: 99%

See 2 more Smart Citations

Optimized Recommendation System for E Commerce on Product Features and User Behavior

G.¹,

Singh²

2019

IJRTE

View full text Add to dashboard Cite

show abstract

“…However, there are many different approaches to this and the question arises which approach is best-suited for a typical SOS. The contribution of this paper is comparing the performance of SimTrust and WSES on certain use cases [7], [8], [9] and defining possible combinations where they can or can not benefit from each other. In Section 2, the use of trust in SOS is explained with a focus on deriving from self-tagging by entities and explicit rating of thirds.…”

Section: Introductionmentioning

confidence: 99%

Combining SimTrust and Weighted Simple Exponential Smoothing

Latta

2021

Preprint

View full text Add to dashboard Cite

In the domain of Autonomic and Organic Computing, the entities of a distributed system are variable as well as the efficiency and the intention of their work. Therefore, a scalable mechanism to incentivise/sanction entities which contribute towards/against the system goal is needed. Trust is a suited metric to find benevolent entities. In this paper, we focus for one on the SimTrust model which introduces trust on entities when they share interest and opinions using tagging information. The second model is the Weighted Simple Exponential Smoothing Trust metric (WSES) which functions on explicitly rated items. WSES follows two basic rules which ensure a logic rating mechanism. When putting these two models in context, SimTrust has advantages on items that have not been rated yet or can not easily be rated. WSES is a trust metric which returns good results on explicit rank values. We propose concepts on combining both approaches and state in which cases they are incompatible.

show abstract

A review on e-commerce recommender applications

Cited by 2 publications

References 0 publications

Optimized Recommendation System for E Commerce on Product Features and User Behavior

Optimized Recommendation System for E Commerce on Product Features and User Behavior

Combining SimTrust and Weighted Simple Exponential Smoothing

Contact Info

Product

Resources

About