Introducing Hybrid Technique for Optimization of Book Recommender System

Chandak, Manisha; Girase, Sheetal; Mukhopadhyay, Debajyoti

doi:10.1016/j.procs.2015.03.075

Cited by 44 publications

(26 citation statements)

References 6 publications

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“…TRoLL(Tool for regression analysis of literacy level) tool was used to compute readability level while ABET (appeal based extraction tool) was used to extract appeal term automatically from description of books. Content Similarity using bag of words using cosine similarity measured based on WCF(word Correlation Factor) Manisha Chandak et al [8] proposes a hybrid technique that uses the recommendation given by Collaborative Filtering and filter the users who rated the books in recommendation. Further filtration is done based on demographic features.…”

Section: A) Literature Reviewmentioning

confidence: 99%

“…It recommends and selects top N items that matches with user previously liked items and meet user needs [9]. Similarity calculation algorithms like Adjusted cosine similarity [14], Slope One algorithm for prediction of Rating [8], Pearson Similarity [20], Jaccard Similarity [21], Eucidean Similarity [22], Cosine Similarity [22] are used. Once the similarity weights are calculated, top-K users with maximum weights are treated as experts to predict ratings.…”

Section: A Collaborative Filteringmentioning

confidence: 99%

“…Content Based : Items are recommended by comparing its features with user profiles. Techniques used in content based filtering includes LSH/MinHash [8], TFIDF(Term Frequency inverse document frequency) [14], [9], Bayesian classifier, Decision Tree, Relevance Feedback [25]. Content based systems can give explanation why a particular recommendation is provided which can increase the user confidence [26].…”

Section: A Collaborative Filteringmentioning

confidence: 99%

“…Ontology based recommender systems are knowledge based recommender systems that kept the knowledge of user, item and relationship between them for recommendations. Similarity is found between users using user"s profiles [8]. Wpath technique is used to calculate the semantic similarity between concepts of Ontology [14].…”

Section: Ontology Basedmentioning

confidence: 99%

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Framework for Hybrid Book Recommender System based on Opinion Mining

Kumar*,

Chawla

2019

IJRTE

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Recommender system plays an important role in automatic filtering out the important and personalized information for the intended user from a large amount of available information on internet. Recommender systems for books provide personalized recommendations to the readers for reading and to the librarians for book acquisition process. The objective of this research paper is four folds. Firstly, it conducts an extensive literature review pertaining to book recommender systems, secondly it specifies the popular recommendation techniques being used in specific application area of books, thirdly the paper reflects on the methodology followed and evaluation techniques being used based on the techniques discussed. Lastly, the paper proposes a framework for a book recommender system using best-suited recommendation techniques.

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Section: A) Literature Reviewmentioning

confidence: 99%

Section: A Collaborative Filteringmentioning

confidence: 99%

Section: A Collaborative Filteringmentioning

confidence: 99%

Section: Ontology Basedmentioning

confidence: 99%

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Framework for Hybrid Book Recommender System based on Opinion Mining

Kumar*,

Chawla

2019

IJRTE

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“…The computational intelligence methods apply intelligence techniques to construct proper recommendation models, including artificial neural networks [30], clustering techniques [9,31], evolutionary algorithms [17,28] and fuzzy set techniques [18]. Hybrid technologies integrate CF, CB or CI methods with the goal of developing better recommendations for items [4].…”

Section: Introductionmentioning

confidence: 99%

Evolving temporal association rules in recommender system

Zhou

Hirasawa²

2017

Neural Comput & Applic

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This research involves implementation of genetic network programming (GNP) and ant colony optimization (ACO) to solve the sequential rule mining problem for commercial recommendations in time-related transaction databases. Excellent recommender systems should be capable of detecting the customers' preference in a proactive and efficient manner, which requires exploring customers' potential needs with an accurate and timely approach. Due to the changing nature of customers' preferences and the differences with the traditional find-allthen-prune approach, the interesting temporal association rules are extracted by the metaheuristics, genetic algorithms-based method of GNP. Additionally, a useful model is constructed using the obtained rules to forecast future customer needs and an ACO approach to evolve the online recommender system continuously. The methodology is experimentally evaluated in a real-world application by analysing the customer database of an online supermarket.

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Hey Alexa, What Should I Read? Comparing the Use of Social and Algorithmic Recommendations for Different Reading Genres

Zhang

Buchanan

McKay

2021

Diversity, Divergence, Dialogue

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Users often seek reading recommendations for what to read, across a variety of topics of interest and genres. While there has been extensive research on the development of recommender algorithms, our understanding of social factors relating to reading recommendation in the digital era is poor. We have no holistic view of how readers interact with diverse resources, social and digital, to obtain reading recommendations. Users can consult computer-generated summaries and human-created reviews. How much or how often the typical user relies on one or other source, or what variations there are by genre of intended reading, are both open questions. To narrow these research gaps, we conducted a diary study to capture a comprehensive picture of readers’ use of algorithm- and social-sourced information to inform their future reading choices. Based on a qualitative analysis of these diaries, we produced a survey to investigate in-depth readers’ recommendation preferences across fictional reading, factual reading, academic resources, and news and articles. We show that users rely on different sources of recommendation information in different ways across different genres, and that modern social media plays an increasing role alongside established mass media, especially for fiction.

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Introducing Hybrid Technique for Optimization of Book Recommender System

Cited by 44 publications

References 6 publications

Framework for Hybrid Book Recommender System based on Opinion Mining

Framework for Hybrid Book Recommender System based on Opinion Mining

Evolving temporal association rules in recommender system

Hey Alexa, What Should I Read? Comparing the Use of Social and Algorithmic Recommendations for Different Reading Genres

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