An online recommendation system based on web usage mining and Semantic Web using LCS Algorithm

Sneha, Y. S.; Mahadevan, G.; Prakash, M.

doi:10.1109/icectech.2011.5941689

Cited by 14 publications

(5 citation statements)

References 4 publications

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“…However, researchers have not widely used it in recommendation. Notwithstanding this, there are some works where it is used as a pattern finding algorithm, as in [33], where the authors analyzed the potential of applying LCS in e-commerce applications by recommending items that might be relevant for the users, reporting metrics like precision, recall, and F1. In [34], the potential utility of LCS is introduced in an online Web Usage Mining system, obtaining a best case accuracy of 73%.…”

Section: Related Workmentioning

confidence: 99%

Building user profiles based on sequences for content and collaborative filtering

Sánchez

Bellogín

2019

Information Processing & Management

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Section: Related Workmentioning

confidence: 99%

Building user profiles based on sequences for content and collaborative filtering

Sánchez

Bellogín

2019

Information Processing & Management

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“…The user profile is not necessary in the ephemeral personalization. In this case the recommendations are created according to the users' behaviours during a current session, their navigation and selection [1]. In this technique the recommendations are the same for all users.…”

Section: Ephemeral Personalizationmentioning

confidence: 99%

“…As delineate by Breese et. al [1], it tries to seek out users that area unit almost like the active user (i.e. the users we would like to form predictions for), and uses their preferences to predict ratings for the active user.…”

Section: Memory Primarily Based Algorithmsmentioning

confidence: 99%

A Review on Current Recommendation Systems

Sharma¹,

Dhir²

2015

IJCA

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In recent years, E-commerce, web service and web information system have been used explosively. Massive explosion of the worldwide web and the emergence of ecommerce have encouraged designers to develop recommendation systems. Web users demonstrate a variety of navigational patterns by clicking series of web pages. These patterns can be understood by mining user logs using WUM. One of the widely used applications of Web Usage Mining is Online Recommendation and prediction. Generally, all the recommendation systems follow a framework for generating efficient recommendations. Various recommendation systems use different approaches based on the sources of information they utilize. The accessible sources are user information (demographics), the product information (keywords, genres) and the user-item ratings. This paper gives introductive information about recommendation system, their techniques, and algorithms and also describes some existing works.

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“…Therefore, the accuracy and exposure of personalized recommended consequences are not suitable to gratify consumers [22]. Additionally, clarification on web page suggestions are regarded as personalization and contextualization, which are measured as essential characteristics to gather the inclination of different consumers [16]. Web recommendation also includes some other methods that are exactly derived from learning web logs and subsequently suggests consumers through a record of pages that are applicable to the consumer contrast their response and to optimize the search outcome by rescheduling or re-ranking and this will diminish to investigate time of preferred web pages [2].…”

Section: Introductionmentioning

confidence: 99%

Prediction of User Future Request Utilizing the Combination of Both ANN and FCM in Web Page Recommendation

Raju

Srinivasan

2018

Journal of Intelligent Systems

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AbstractThis paper explains about the web page recommendation system. This procedure encompasses consumers’ upcoming demand and web page recommendations. In the proposed web page recommendation system, potential and non-potential data can be categorized by use of the Levenberg–Marquardt firefly neural network algorithm, and forecast can be made by using the K-means clustering algorithm. Consequently, the projected representation demonstrates the infrequent contact format with the help of the representation that integrates the comparable consumer access model data that belong to the further consumer. Thereafter, the impending user data are specified to the clustering progression. The third phase of the projected process is collecting potential data with the aid of the improved fuzzy C-means clustering algorithm. The last step of our projected process is envisaging the upcoming demand for the subsequent consumer. The presentation of the projected procedure will be compared to the obtainable procedure.

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An online recommendation system based on web usage mining and Semantic Web using LCS Algorithm

Cited by 14 publications

References 4 publications

Building user profiles based on sequences for content and collaborative filtering

Building user profiles based on sequences for content and collaborative filtering

A Review on Current Recommendation Systems

Prediction of User Future Request Utilizing the Combination of Both ANN and FCM in Web Page Recommendation

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