Survey on Collaborative Filtering, Content-based Filtering and Hybrid Recommendation System

Thorat, Poonam B.; Goudar, R M; Barve, Sukratu

doi:10.5120/19308-0760

Cited by 198 publications

(99 citation statements)

References 23 publications

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“…There are mainly three types of recommendation systems (Thorat, Goudar, & Barve, 2015), namely, content-based systems, collaborative filtering systems, and hybrid systems. The content-based recommendation systems generate recommendations on the basis of the profile of user's preference and the item's description (Lops, De Gemmis, & Semeraro, 2011).…”

Section: Related Workmentioning

confidence: 99%

Personalized question recommendation for English grammar learning

et al. 2017

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Learning English grammar is a very challenging task for many students especially for nonnative English speakers. To learn English well, it is important to understand the concepts of the English grammar with lots of practise on exercise questions. Previous recommendation systems for learning English mainly focused on recommending reading materials and vocabulary. Different from reading material and vocabulary recommendations, grammar question recommendation should recommend questions that have similar grammatical structure and usage to the question of interest. The content similarity calculation methods used in existing recommendation methods cannot represent the similarity between grammar questions effectively. In this paper, we propose a content‐based approach for personalized grammar question recommendation, which recommends similar grammatical structure and usage questions for further practising. Specifically, we propose a novel structure named parse‐key tree to capture the grammatical structure and usage of grammar questions. We then propose 3 measures to compute the similarity between the question query and database questions for grammar question recommendation. Additionally, we incorporated the proposed recommendation method into a Web‐based English grammar learning system and presented its performance evaluation in this paper. The experimental results have shown that the proposed approach outperforms other classical and state‐of‐the‐art methods in recommending relevant grammar questions.

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Section: Related Workmentioning

confidence: 99%

Personalized question recommendation for English grammar learning

et al. 2017

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“…A booming growth in social media in recent years in particular, calls for development and progress in techniques and tools for filtering out textual noises [1]. Regarding that aspect, content-based filters and models are particularly successful [2], and the ones for the English language have already been widely used in various fields, including information retrieval [3,4], network security [5,6], personalized information extraction and inference [7], content-based recommendation systems [8], knowledge discovery [9,10], Short Message Service (SMS) spam filtering [11,12], and many other areas.…”

Section: Introductionmentioning

confidence: 99%

Text Filtering through Multi-Pattern Matching: A Case Study of Wu–Manber–Uy on the Language of Uyghur

et al. 2019

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Given its generality in applications and its high time-efficiency on big data-sets, in recent years, the technique of text filtering through pattern matching has been attracting increasing attention from the field of information retrieval and Natural language Processing (NLP) research communities at large. That being the case, however, it has yet to be seen how this technique and its algorithms, (e.g., Wu-Manber, which is also considered in this paper) can be applied and adopted properly and effectively to Uyghur, a low-resource language that is mostly spoken by the ethnic Uyghur group with a population of more than eleven-million in Xinjiang, China. We observe that technically, the challenge is mainly caused by two factors: (1) Vowel weakening and (2) mismatching in semantics between affixes and stems. Accordingly, in this paper, we propose Wu-Manber-Uy, a variant of an improvement to Wu-Manber, dedicated particularly for working on the Uyghur language. Wu-Manber-Uy implements a stem deformation-based pattern expansion strategy, specifically for reducing the mismatching of patterns caused by vowel weakening and spelling errors. A two-way strategy that applies invigilation and control on the change of lexical meaning of stems during word-building is also used in Wu-Manber-Uy. Extra consideration with respect to Word2vec and the dictionary are incorporated into the system for processing Uyghur. The experimental results we have obtained consistently demonstrate the high performance of Wu-Manber-Uy.Information 2019, 10, 246 2 of 15 and practical improvements on the problem to find better solutions. Not only restricted to major languages in the world such as English or Chinese, the need to deal with Uyghur in big data is particularly urgent in the following three senses, as we see them.(1) With the rapid development of large-scale storage technology and services in internet and digital communications, massive amounts of digitalized text data in Uyghur have been created in an accelerated speed and on a day-to-day basis.(2) While the lawful use of information extracted from these massive text data should be encouraged and promoted, the existence and spread of harmful or unauthentic information poses serious and in many cases severe threats to regional security and stability in Xinjiang, China. (3) Though Uyghur itself belongs to the category of low-resource languages, it is also is an agglutinating language, and several unique linguistic features only exist in Uyghur.Whether it is for information acquisition or for protection against unwanted information, from our discussion above, technologies and tools for text filtering in Uyghur are urgently needed, and, unfortunately at the same time, existing text filters only work for either English, Chinese, or Arabic. These cannot be directly applied for our purpose. Research reported in this paper aims to bridge this gap. In the expansion aspect, it combines the Uyghur-Chinese bidirectional dictionary and the deep-learning tool Word2vec [13], and it uses multi-pattern matchin...

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“…Recommendation systems are the software tools used to give suggestions to users on the basis of their requirements [8].…”

Section: Fig 2 Categories Of Data Types With Examplesmentioning

confidence: 99%

A Framework for Development of Recommender System for Financial Data Analysis

Kanaujia¹,

Pandey²,

Rautaray³

2017

IJIEEB

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Abstract-The huge amount of data is being created every day by various organisations and users all over the world. Structured, semi-structured and unstructured data is being created at a very rapid speed from heterogeneous sources like reviews, ratings, feedbacks, shopping details, etc., it is termed as Big Data. This data generated from different users share many common patterns which can be filtered and analysed to give some recommendation regarding the product, goods or services in which a user is interested. Recommendation systems are the software tools used to give suggestions to users on the basis of their requirements. Today no system is available for suggesting a person on how to use their money for saving, where to invest and how to manage expenditures. Few consulting systems are available which provide investment and saving tips but they are not much effective and are much complex. The presented paper proposed a collaborative filtering based recommender system for financial analysis based on Saving, Expenditure and Investment using Apache Hadoop and Apache Mahout. Many savings and investment consulting systems are available but no system provides effective and efficient recommendation regarding management and beneficial utilisation of salary. The advantage of proposed recommender system is that it provides better suggestion to a person for saving, expenditure and investment of their salary which in turns maximises their wealth. Due to enormous amount of data involved, Apache Hadoop framework is used for distributed processing. Collaborative filtering and Apache Mahout is used for analysing the data and implementation of the recommender system.

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Survey on Collaborative Filtering, Content-based Filtering and Hybrid Recommendation System

Cited by 198 publications

References 23 publications

Personalized question recommendation for English grammar learning

Personalized question recommendation for English grammar learning

Text Filtering through Multi-Pattern Matching: A Case Study of Wu–Manber–Uy on the Language of Uyghur

A Framework for Development of Recommender System for Financial Data Analysis

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