International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 11 Issue: 04 | Apr 2024 www.irjet.net p-ISSN: 2395-0072
BOOK RECOMMENDATION SYSTEM AND AI POWERED BOOK SEARCH
Saini Jacob1 , Adheena Dev2 , Antony C Robert3 , Aavani S Kumar4 , Aswal K Sunil5
1Assistant professor, Dept. of Computer Science and Engineering, Sree Narayana Gurukulam College Of Engineering, Kadayirupppu, Ernakulam, Kerala, India
2,3,4,5UG student, Dept. of Computer Science and Engineering, Sree Narayana Gurukulam College Of Engineering, Kadayirupppu, Ernakulam, Kerala, India
Abstract - This literature survey paper navigates the landscape of book recommendation systems and AI-powered search methods, exploring various filtering models. In the realm of book recommendations, we delve into collaborative filtering, content-based filtering, and emergingtrends indee p learning. The survey comprehensively analyses user profiling, feature extraction, and similarity calculation techniques, highlighting the role of user feedback. Shifting to AI-powered search, we explore data indexing, semantic search, and probabilistic ranking functions like BM-25. Emphasising user feedback, we examine the effectiveness of combiningsemantic understanding with traditional ranking. The survey provides insights into the strengths, limitations, andemergingtrends in both domains, offering a concise overview of advancements and future directions)
Key Words: Artificial Intelligence (AI), Content-Based Filtering, Collaborative-based filtering, BM-25, Semantic search.
I. INTRODUCTION
In an age marked by an unprecedented wealth of literary options,thepursuitofpersonalisedcontentdiscoverystands as a central challenge for modern readers. This literature survey embarks on a comprehensive exploration of two pivotal elements shaping the reading experience: book recommendationsystemsandAI-poweredbooksearch.
Asreaderstraverseanever-growingexpanseofbooks,the demandforpersonalisedandeffectivecontentsuggestions hasbecomemoreevidentthanbefore.Ourexplorationinto book recommendation systems reveals a variety of sophisticated algorithms functioning as literary guides. Thesealgorithmssiftthroughextensivebookcollectionsto present readers with content aligned precisely with their unique preferences, historical reading behaviours, and demographic characteristics. We have gone over various recommendation models to find the best fit for our recommendation system. Content-based filtering, for instance, suggests books based on attributes and user preferences,whilecollaborativefilteringleveragescollective userbehaviourtomakerecommendations.Hybridmodels, blendingmultipleapproachesforenhancedaccuracy,stand asatestamenttothedynamismofrecommendationsystem design. We also explore how artificial intelligence plays a
crucial role in making searches more accurate. Semantic search mechanisms, which comprehend the context and intentbehinduserqueries,emergeascrucialinenhancing the precision of book search results. The incorporation of BM25, an information retrieval ranking function, further refines the search process by considering factors such as termfrequencyanddocumentlength,therebyenhancingthe relevanceofresults.
Thereforebyunderstandingthenuancesofuserpreferences andimplementingadvancedsearchcapabilities,wecanbuild asystemthatispoisedtoofferamorepersonalised,efficient, andenrichingliteraryexperience
II. LITERATURE SURVEY
[1]ThispaperintroducesthePRESrecommendersystem, designed to assist users in navigating a large website, particularlyfocusingonpersonalisedsuggestionsforsmall articles related to home improvements. PRES employs content-basedfilteringtechniques,necessitatingadynamic user model learned solely from positive feedback. The relevance feedback method is identified as a suitable approachforsuchadynamicusermodelduetoitsefficiency. Testresultsindicatethat,onaverage,slightlymorethanhalf of the suggestions made by PRES are deemed relevant. Challenges arise from the inherent ambiguity in language, where the same concept may be described using multiple terms, affecting the accuracy of user profiles. The short lengthofdocumentsandtheuser'stendencytoselectonlya fewdocumentsonagiventopiccontributetothischallenge. The paper suggests that refining the vector space model could potentially enhance results. A notable limitation of content-basedfilteringsystemsistheirinabilitytopredict users'futureinterests.Collaborativefilteringsystems,which consider user preferences beyond past selections, are proposedascomplementarytocontent-basedapproaches. TheeffectivenessofPREScouldbefurtherenhancedthrough acombinationofcontent-basedandcollaborativefiltering techniques. The impact of menu structure on PRES's effectivenessishighlighted,notingthatrecommendingitems already present in the current menu is not useful. Recommendations become more challenging when users selectmenus containingmostrelevantitems.Theaverage precisionofPRESrecommendationsdropsbyapproximately 15%whenaccountingforthemenu.Despitethischallenge,
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 11 Issue: 04 | Apr 2024 www.irjet.net p-ISSN: 2395-0072
the paper argues that personalising a website based on users' interests can significantly improve information retrieval, especially on larger websites that cannot be perfectlyoptimisedforalluserswithvariedinterests.
Theauthorsof[2]presentanovelcontent-basedjournaland conference recommender system tailored for computer science and technology, asserting its uniqueness in the absence of similar systems or published methodologies. Given the absence of a suitable dataset, the authors developed a web crawler to collect data, creating training and testing sets. Various feature selection methods were employed, and experiments were conducted to refine the strategy and reconstruct the feature space. Ultimately, a softmax regression model was employed to offer a threeclassrecommendationsolution,aidingusersinidentifying potential journalsorconferences.It'snoteworthythatthe proposed method utilises only the abstract of a paper for recommendations,a distinctivefeatureofthesystem. The versatilityoftheapproachishighlightedasitcanbeadapted for other e-library Recommender systems. For instance, recommendations generated by the method could assist readers in swiftly determining a paper's domain or retrievingsimilarpapers.Themethodisalsosuggestedfor application in e-business, where a business user's preferences,summarisedastext(akintoapaperabstract), could yield recommendations for engaged business areas. This, in turn, aids business users, such as buyers, in the decision-making process for selecting qualified business partners.Whilethesystemachievesa61.37%accuracyfor paper recommendations, the authors acknowledge the potential for further improvement in accuracy and Fmeasure, identifying this as a focus for future work. This study not only introduces an innovative recommender systemforcomputersciencepublicationsbutalsohighlights the adaptability of the proposed method for broader applications in e library systems and e-business decision support.
[3]discussesabookrecommendationsystemdevelopedby Ms.SushamaRajpurkar,Ms.DarshanaBhatt,andMs.Pooja MalhotrafromtheDepartmentofInformationTechnologyat KJSCE,Mumbai.Theobjectiveofthesystemistorecommend books to buyers based on their interests, utilising a combinationofcontentfiltering,collaborativefiltering,and association rule mining. The role of recommendation algorithms, comprising user and item databases as inputs andgeneratingrecommendationsasoutputs,isemphasised. The system's approach involves content-based filtering to matchitemstousers,collaborativefilteringtocollectratings from customers and recommend similar items, and association rule mining to find interesting associations amonglargedatasetsofitems.Thecontentrecommendation systemisexplainedtousecontent-basedfilteringtomatch itemsbasedontheircontentsandrecommendsimilaritems basedonusers'previouspreferences.Collaborativefiltering, anothertechniqueforrecommendersystems,isdescribedas
collectingopinionsfromcustomersintheformofratingson items, services, or service providers. The item-based collaborativerecommendationalgorithm,whichcomputes thesimilarityofitemsthetargetuserhasratedandprovides recommendationsbasedonweightedaverages,isdetailed.0
Additionally, association rule mining is introduced as a method to find interesting associations and correlation relationshipsamonglargedatasetsofitems,withafocuson marketbasketanalysisasatypicalexample.
Thekeystepsinvolvedinthebookrecommendationsystem proposedinthedocumentareasfollows:
1. Recording the books bought by users and finding the categoryofbooksfromtheuser'sbuyinghistory.
2. Performing content-based filtering to find all the books accordingtothecategoryfoundinstep1.
3.Utilisingitem-basedcollaborativefilteringtoevaluatethe quality of the recommended books based on the ratings givenbyotherbuyers.
4. Analyzing the book transaction database to find transactionswiththesamecategoryasidentifiedinstep1 andapplyingassociationruleminingtoidentifyfrequently purchasedbooks.
5.Findingtheintersectionoftheresultsfromsteps3and4 andarrangingtheintersectionresultindescendingorderof ratings.
6.Generatingthefinalrecommendationsforthebuyerbased ontheoutcomeofstep5.
7.Storingtherecommendationsinthebuyer'swebprofile for offline use, with the recommendations being automaticallygeneratedwhenthebuyercomesonlinenext time
Thesestepsdemonstratethecomprehensiveapproachofthe book recommendation system, which combines content filtering,collaborativefiltering,andassociationrulemining toprovideefficientandeffectiverecommendationstobuyers basedontheirinterestsandpreferences.
The master thesis [4] delves into content-based recommender algorithms, focusing on their relevance in overcomingthechallengesposedbyinformationoverload. Tackling issues like the cold start problem, the thesis employs a content-based approach with a diverse set of features,includingcontributionsfromotherresearchteams, toenhanceaccuracy.Section4.1outlinesthecomprehensive features incorporated into the recommender system, emphasizing diversity and accuracy. The introduction of cosinesimilarityandtheinnovativeTF-IIDF-DCapproachfor weight assignment contribute to refining the movie representation. The study specifically tailors the content-
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 11 Issue: 04 | Apr 2024 www.irjet.net p-ISSN: 2395-0072
based recommender system for VionLabs' movie website, prioritizingdiversityanduniquenessinfeatureextraction. TheintroductionofTF-IIDF-DCforsettingfeatureweightsis a key innovation, refining the movie representation. Evaluationusingk-NNandvariousmetricsdemonstratesthe positiveimpactofthenewapproach,confirmingimproved accuracy and representation. This research not only addresses information overload but also presents a novel and effective content-based recommender system, particularlydesignedforthemoviedomain.
YongenLiangandShimingWan[5]presentapersonalised technologyutilisingcollaborativefilteringtoenhancebook recommendationsforauniversitylibrary.Theprimaryfocus is on providing a tailored recommendation service exclusively to registered users. The proposed method employsbothuser-useranditem-itemcollaborativefiltering techniques, leveraging the collaborative nature of user preferences to refine the recommendation process. The system employs cosine similarity to measure the likeness between books and users, thereby offering precise recommendations.
CollaborativeFilteringTechniques:
•User-UserFiltering:Thesystemcalculatesthesimilarity between users based on their preferences and borrowing history.Thisapproachaimstorecommendbookslikedby userswithsimilartastes.
• Item-Item Filtering: Similarity calculations extend to books, facilitating recommendations based on the preferences of users who enjoyed similar books. This method enhances the accuracy of recommendations by consideringthecharacteristicsofbooks
The collaborative filtering relies on cosine similarity to quantifythelikenessbetweenusersandbooks.Thismetric ensures a nuanced understanding of user preferences, contributingtotheaccuracyoftherecommendationsystem. Onenotablechallengeaddressedisthe'coldstart'problem, common when new users lack historical data. The paper introduces a solution to the 'cold start' problem. The proposedExpertandNewBookRecommendationmodule aims to recommend books that are currently popular or highly rated. This module becomes particularly useful for newusers,providingthemwithrelevantrecommendations despitetheabsenceofpriorengagementwiththesystem.
[6] highlights the potential of content-based recommendationsystems,specificallyLibra,asapromising alternative to collaborative filtering. Unlike collaborative methods, content-based systems hold the promise of effectivelyrecommendingunrateditemsandprovidinghigh qualityrecommendationsforuserswithunique,individual tastes. Libra, introduced as an initial content-based book recommender,utilisesasimpleBayesianlearningalgorithm andweb-derivedinformationtorecommendtitlesbasedon
individualuserinput.InitialexperimentssuggestthatLibra canefficientlyprovideaccuraterecommendationseven in theabsenceofsufficientuserdata,showcasingitspotential inrecommendingunrateditems.Libraincorporatescontent informationaboutbooks,suchasdescriptionsandauthors, obtained from collaborative methods. This information is experimentally shown to positively contribute to the system's performance. Collaborative methods excel in recommending well-known items to users with similar tasteswhensufficientuserdataisavailable.Content-based methodsareeffectiveinrecommendingunpopularitemsto userswithuniquetastes,especiallywhenuserdataislimited but effective content information is readily obtainable. Integrating these approaches may offer a comprehensive solution,providingtheadvantagesofbothcollaborativeand content-basedsystems.Thepaperarguesinfavourofusing randomly selected examples for evaluation, emphasising the need for a more robust and unbiased assessment of recommendersystems.Insummary,theconclusionsuggests thatcontent-basedapproaches,exemplifiedbyLibra,have distinct advantages, especially in scenarios where collaborative methods may face limitations. The potential integrationofcollaborativeandcontent-basedapproachesis proposed as a way to capitalise on the complementary strengthsofeachmethod,ultimatelyprovidingmorerobust and accurate recommendations for users. [7] presents a process for developing a hybrid recommendation system which involves several key steps. Initially, datasets are collected from MovieLens and Netflix, which are then integratedintoadatabaseandnormalisedforconsistency. Followingthis,atheoreticalframeworkforbookindexingis established. User-book preferences are determined by assessing the frequency of interactions, and a sub-table is designed to associate users with their ratings. Book-book similarityiscomputedtoidentifyneighbourhoodsofsimilar books, forming another sub-table that links books with similarratings.Thehybridrecommendationsystemmerges collaborativefiltering,representinguserpreferences,with content-based filtering, reflecting book similarities. An intermediate list is then generated, comprising books and their scores, combining both collaborative and contentbasedmetrics.Theresultinghybridmodelaimstoprovide userswithexcellentpreferencesbyleveragingthestrengths ofbothcollaborativeandcontent-basedfilteringtechniques.
[7]alsoproposesasolutionforthe“ColdStart”problemin recommendation systems, where it is challenging to recommend items for new users who lack a substantial purchase or interaction history. The approach involves creating an initial list of user book preferences and then expandingthislistbasedontheBook-BookSimilarityIndex. Thealgorithmincorporatescollaborativetechniques,using an intermediate list strategy, to refine the expanded user preferencesandgeneraterecommendations.Theexpanded list considers cumulative effects and adjustments in book ratings, considering the Book-Book Similarity Index. The algorithm outlines scenarios where ratings may change
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 11 Issue: 04 | Apr 2024 www.irjet.net p-ISSN: 2395-0072
betweentheinitialandexpandedlistsduetotheinfluenceof similarbooks.ThepresentedColdStartModeloffersasyst ematic waytoprovid1eaccuraterecommendationsfor users with limited or no historical data, addressing the challengesassociatedwiththecoldstartproblem.
The paper [8] titled "Book Recommendation System throughContent-BasedandCollaborativeFilteringMethod" isauthoredbyMs.PraveenaMathew,Ms.BincyKuriakose, andMr.VinayakHegde.Thestudyfocusesondevelopinga bookrecommendationsystemthatcombinescontent based andcollaborativefilteringmethods.Theintegrationofthese twoapproachesaimstoenhancetheaccuracyandefficiency of book recommendations. The proposed system utilises content-basedfiltering,whichconsidersitemcharacteristics, and collaborative filtering, which leverages user behavior andpreferences.Thehybridnatureoftheapproachsuggests acomprehensivestrategyforbookrecommendations,taking advantageofbothcontentrelevanceanduserinteractions. Themethodologyinvolvesacombinationofcontent-based and collaborative filtering techniques. Content-based filtering selects and recommends items based on their characteristicsandcontent.Inthiscontext,thecontentofthe book,alongwiththepurchasehistoryofaparticularbookby theuser,isconsideredtorecommendadditionalbookswith similar content. Collaborative filtering, on the other hand, builds a recommendation model based on user behavior, includingpastpurchasesandratings.Thesystempredicts and recommends books based on the opinions and preferences of other users, providing a personalised experience. The paper likely provides details on the algorithms used for content-based and collaborative filtering,thestepsinvolvedindatapreprocessing,andthe overall architecture of the recommendation system. The hybrid algorithm proposed in the methodology aims to leverage the strengths of both filtering methods, ensuring efficientandeffectivebookrecommendationsbasedonuser interests.
[9] "The Probabilistic Relevance Framework: BM25 and Beyond"isaresearchpaperthatthoroughlyinvestigatesthe probabilisticrelevanceframework,withaprimaryemphasis on the BM25 algorithm and its extensions. Serving as a widelyusedprobabilisticinformationretrievalmodel,BM25 ranksdocumentsbasedontheirrelevancetouserqueries. ThepaperdelvesintothetheoreticalfoundationsofBM25, providingadetailedexplorationofitsunderlyingprinciples, includingtermfrequency,documentlengthnormalization, and inverse document frequency. The authors extend the discussion by proposing modifications, refinements, or additionalfeaturestoenhancetheoriginalBM25algorithm, addressingspecificchallengesorlimitations.Experimental resultsandevaluationsarelikelypresentedtovalidatethe effectivenessof these extensions,comparingthem against thebaselineBM25algorithmandpotentiallyotherretrieval models.Theoverarchinggoalistoshowcasetheimproved performanceandadditionalbenefitsgainedbyincorporating
theseproposedenhancements.Insummary,thepapernot onlyelucidatesthetheoreticalunderpinningsofBM25but alsocontributesadvancementsthatextendbeyondthebasic formulation,aimingtooptimizethealgorithm'saccuracyand applicabilityinpracticalretrievalscenarios.
[10]addressesthechallengeofaccessingthegrowinglinked open data on the internet for non expert users without knowledge of SPARQL language. While modern semantic searchenginesofferstandarduserinterfaces,theymaynot be user friendly for those unfamiliar with semantic web technologies.Theproposedsolutionintroducesanefficient methodforinformationretrieval,utilisingasystemtomatch user-enteredkeywordswithrelevantontologiesandconvert them into standard SPARQL queries. The paper presents algorithms for query tagging and keyword mapping, outlining a framework with prototype user interface examples.The goal isto enhanceaccessibility to semantic data foruserswithlimited technical expertise. Thispaper addresses the significant challenge of understanding user queriesinsemanticsearchengines.Theauthorsrecognise the diversity of user queries and propose a solution by categorisingthemintopuresemanticqueriesandfull-text patternmatching(e.g.,formoviesorbooks).Theyintroduce a 'Bag of Keywords' concept, storing CPI values for each keyword, aiding the Query optimiser in tagging keywords effectively. The solution, tested with open semantic data from the DBpedia SPARQL endpoint, employs a generic approach,enhancingflexibilityforapplicationacrossdiverse repositoriesatalargerscale.
[11] provides an overview of Semantic Search, Semantic Web,andvariousSemanticSearchEngines,highlightingthe advantages of Semantic Search. Hakia, a semantic search engine, prioritises meaning-based results over keyword matchingandemploystechnologieslikeOntoSemandQDEX. Kngine,anintelligentengine,focusesonfact-basedquestion answeringusingdeeplearningandbigdata.Kosmixactsasa guide to web content, categorising pages based on topics, while DuckDuckGo offers a feature-rich semantic search experiencewithdisambiguationcapabilities.Powerset,now under Microsoft, emphasizes intuitive search through natural language processing and integrates Wikipedia results.Sensebotutilisestextminingforsemanticanalysis, providingconciseoverviewsofsearchoutcomes.Cognition, a meaning-centric search engine, offers link results and promotesontologyusage.Swoogle,acrawler-basedengine fortheSemanticWeb,usestextminingandsummarisation to extract meaning from web pages. Each search engine introduces innovative approaches to enhance semantic searchexperiencesacrossvariousdomains.Intheresearch, itwasfoundthatDuckDuckGoretrievesahighernumberof relevant records compared to Google, with 76 out of 100 records being deemed applicable. [11] The paper acknowledgestheevolvingnatureofontologiesandtheneed for automated processes. Future directions include addressingcontextualinformationfortailoredsearchresults
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 11 Issue: 04 | Apr 2024 www.irjet.net p-ISSN: 2395-0072
and aspiring toward advanced capabilities like query answering within specific domains. The paper envisions Semantic Search becoming a crucial and potent search techniqueglobally,withsuggestedimprovementsfocusedon meetingevolvingtechnologicalstandards.
[12]Thepaper,authoredbyBalamuruganS,Suriyavarman D,andChellaiyaAfromApolloArtsandScienceCollegein Chennai, delves into contemporary issues in AI search engines. It emphasizes the use of machine learning and naturallanguageprocessingforimprovedsearchaccuracy and relevance. The study highlights the advantages of AI searchengines,includingpersonalizedresultsandtheability to understand natural language queries, while also addressing challenges such as data requirements and potential biases. Objectives range from understanding AI search engine principles to assessing their impact on the student community. The paper concludes by introducing various AI search engine brands, underscoring their promisingfutureandtheneedforethicaluse
[13]ThisarticlebyYodhiYuniarexplorestheapplicationof artificial intelligence (AI) in Search Engine Optimization (SEO). The author categorizes AI into three methods: evolutionary computation, fuzzy logic, and classifiers and statistical models. The study identifies various prototypes and applications of AI in SEO, including Polidoxa, Fuzzy Inference System, commercial packages like SPSS Clementine and SearchDex Hyperloop, Support Vector Machine,andtheK-NearestNeighborAlgorithm.Thearticle emphasizes the challenges associated with the confidentiality of algorithms used by SEO companies and search engines. It also discusses the impact of AI on SEO, highlightingthepotentialbenefitsandconcernsrelatedto contextual-based search mechanisms. Overall, the study providesinsightsintotheevolvingrelationshipbetweenAI and SEO, showcasing the role of AI in improving search enginerankingsandoptimizingwebpages
TABLE 1. COMPARATIVE STUDY OF RECOMMENDATION SYSTEM
AUTHER STUDY
Robin van Meteren and Maarten van Someren
UsingContent-Based Filtering for Recommendation
SUMMARY
The paper presents the development and implementation of a content-based filtering recommender system called PRES, which suggests small articles about home improvements It discussesthestructureof web pages, content representation,similarity measurements, implementation details, and the evaluation of PRES using fictitious users with different interests
MAIN FINDINGS TECHNIQUES
PRES Overview: Tailored for do-it yourself home improvementSpecialisedin personalisedsuggestions
Efficient and dynamic approach employed by PREShetestresultssuggest thatslightlymorethanhalf ofthesuggestionsmadeby PRES are relevant, indicating its effectiveness in providing personalised recommendations
USED
•content-based filtering
•relevance feedbackmethod
•vector space model for representing documentcontent
•cosine measure for calculating similarity and determining novelty and proximity of documents for recommendation
Donghui Wang, Yanchun Liang, Dong Xu, Xiaoyue Feng,RenchuGua
A content-based recommender systemforcomputer sciencepublications
It includes information about the system's accuracy, coverage, and the types of recommendation results it provides The paper highlights the use of a web crawler to collect data and the use of differentfeatureselection methods to reconstruct thefeaturespace
Content-Based Recommender System is developed for computer science Achieved 61.37% accuracy. Relies solely on manuscript abstracts for recommendations Accessibleasawebservice Potential for integration into e library recommendersystem
•hybrid model basedonchisquare feature selection and softmax regression
• term frequency and inverse document frequency(TF-IDF) for recognising important words orphrases
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 11 Issue: 04 | Apr 2024 www.irjet.net p-ISSN: 2395-0072
Ms Sushama, Rajpurkar Ms, DarshanaBhatt,Ms PoojaMalhotra
Master’s Thesis at VionLabs Supervisor: Chang Gao Examiner: MihhailMatskin
Content-based Recommender System for Movie Website
The paper presents a book recommendation system based on combined features of content filtering, collaborative filtering, and association rule mining.Thesystemaims torecommendbooksthat suit the buyer's interest andworksoffline,storing recommendations in the buyer'swebprofile.
•Utilisescombinedfeatures of content filtering, collaborative filtering, and associationrulemining
• Collaborative filtering involves analyzing ratings byotherbuyers
• Associative model enhances the strength of recommendations
• Recommender system operates offline, avoiding performanceissues
•HybridApproach
• Uses Big Data Analysis
•Offline processing
• Comprehensive Techniques
The Design and Implementation of
Recommendation System
The summary of the paper includes the development and implementation of a content-based recommendersystemfor amoviewebsite,focusing on the extraction of diverse and unique featuresfrommovies,the construction of a movie model, and the introduction of a new approach for setting feature weights to improve movie representation. The evaluationdemonstrates a positive impact of the newapproach
Abooksrecommendation system based on collaborative filtering algorithm can basically meet the needs of users torecommendfunctions.
The main findings are the introduction of a contentbasedrecommendersystem for the movie website of VionLabs, emphasizing the importance of quantitative analysis of content for building the system, the system'sabilitytocompare userandproductprofilesto recommend the most similar products, and the development and continuation of collaborativefilteringinthe original content-based recommendersystem.
The techniques used in K E Ma, Chang Gao include machine learning for building user and product profiles,analysisof text information for making recommendations, and a new approach for setting the weight of features to improve movie representation. The paper also discusses techniques for calculating the similaritybetween movies.
• Personalised Recommendation Technology
•CollaborativeFiltering
•SystemEffectiveness
•personalised recommendation technology
• collaborative filteringalgorithm.
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 11 Issue: 04 | Apr 2024 www.irjet.net p-ISSN: 2395-0072
RaymondJ.Mooney andPaulN.Bennett andLorieneRoy
M Sarumathi, S Singarani
BookRecommending Using Text Categorization with Extracted Information
Ms. Praveena Mathew, Ms.bincy kuriakose and Mr. VinayakHedge
SystematicApproach for Cold Start Issues inRecommendations System
The paper presents a content-based recommendersystemfor suggestingitemstousers by constructing profiles from rated examples. Unlike traditional methods, it uses semi structured information from the web, particularly for recommending books. Initial experiments indicate fairly accurate recommendations, emphasising the effectiveness of simple information extraction techniques in enhancing thesystem'sperformance
The paper introduces a system that overcomes traditional collaborative filtering challenges by combining content and collaborative techniques to reduce cold start issues. Using movielens andNetflixdata,itselects attributes, determines recommendations based on user preferences and book-booksimilarity,and establishes relationships among customers with similarbookpreferences
Book Recommendation System Through Content Based and Collaborative FilteringMethod
ThispaperpresentsBook RecommendationSystem (BRS)basedoncombined featuresofcontentbased filtering (CBF), collaborative filtering (CF)andassociationrule mining to produce efficient and effective recommendation
•Introduction of contentbased recommender systems
• Description of a book recommendingsystem
• Utilization of semistructured information fromtheweb
• Demonstration of the potential for accurate recommendationswiththis approach
• Text categorization method
• Semi-structured information gathering
• Simple information extraction techniques
•MahoutRecommendation System:Recommendshighrated books. Uses content and collaborative filtering forcoldstartissues.
• Big Data Analysis: Ensures accuracy and efficiency with large datasets.
• Hybrid Filtering Approach: Combines content and collaborative methods. Provides optimal results.
•Hybrid Book Recommendation System (BRS): Introduces a hybrid algorithm combining content- based filtering, collaborative filtering, and associationrulemining.
• Issue with Existing Systems: Identifies a problem with current recommendationsystems
•AddresstheColdstart
•collaborative filtering
•contentfiltering clustering
• neighbourhood formation
• Jaccard's coefficient for similarity calculation
• Apache Mahout for implementing distributed algorithms
CombinedFeatures Of Content Based Filtering (CBF), Collaborative Filtering (CF) And Association Rule Mining
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 11 Issue: 04 | Apr 2024 www.irjet.net p-ISSN: 2395-0072
III. CONCLUSION
In conclusion, this literature survey navigates the diverse landscapeofbookrecommendationsystemsandAI-powered search methodologies, shedding light on the evolving paradigmswithinthesedomains.Ourexplorationofvarious filteringmodels,includingcollaborativeandcontent-based filtering,underscorestheintricateinterplaybetweenuser preferences, feature extraction, and recommendation accuracy. The examination of emerging trends, notably in deep learning, reveals the dynamic nature of book recommendationsystems.Simultaneously,theinsightsinto AI poweredsearchmethods,encompassingsemanticsearch and probabilistic ranking with BM-25, underscore the importanceofaligningsearchalgorithmswithuserintent. Userfeedbackemergesasarecurringtheme,influencingthe continuousrefinementofbothrecommendationsystemsand search functionalities. The quest for more personalised, efficient,andcontext-awarerecommendations,coupledwith search results tailored to user intent, propels these areas intotheforefrontofintelligentinformationretrieval.
IV. REFERENCES
[1] Using Content-Based Filtering for Recommendation Robin van Meteren and Maarten van Someren NetlinQ Group,GerardBrandtstraat26-28,1054JK,Amsterdam,The Netherlands, robin@netlinq.nl University of Amsterdam, Roeterstraat18,TheNetherlands,marten@swi.psy.uva.nl
[2] A content-based recommender system for computer sciencepublicationsbyDonghuiWang,YanchunLiang,Dong Xu, Xiaoyue Feng, Renchu Guan https://doi.org/10.1016/j.knosys.2018.05.001
[3] Book Recommendation System by Ms Sushama, RajpurkarMs,DarshanaBhatt,MsPoojaMalhotraIJIRST–InternationalJournalforInnovativeResearch inScience& Technology|Volume1|Issue11|April2015ISSN(online): 2349-6010
[4]Content-basedRecommenderSystemforMovieWebsite - Master’s Thesis at VionLabs Supervisor: Chang Gao Examiner:MihhailMatskin
[5] The Design and Implementation of Books RecommendationSystemYongenLiang,ShimingWanDOI: 10.1109/ICSESS.2018.8663914Publisher:IEEE
[6] Book recommendation using Categorization with Extracted Information Raymond J. Mooney and Paul N. BennettandLorieneRoy
[7] Systematic Approach for Cold Start Issues in Recommendations SystemM Sarumathi, S Singarani, S. Thameemaa,V.Umayal,S.Archana,K.Indira,M.K.Kavitha DeviDOI:10.1109/ICRTIT.2016.7569601
[8]BookRecommendationSystemThroughContentBased AndCollaborativeFiltering MethodMs.PraveenaMathew, Ms.bincy kuriakose and Mr. Vinayak hedge DOI: 10.1109/SAPIENCE.2016.7684166
[9] The Probabilistic Relevance Framework: BM25 and Beyond Stephen E. Robertson, Hugo Zaragoza DOI:10.1561/1500000019
[10]UserQueriesforSemanticSearchEngineAroojFatima, CristinaLuca,GeorgeWilsonDOI:10.5013/IJSSST.a.16.04.11
[11] The Technique of Different Semantic Search Engines UpasanaSinha,VikasDubeyInternationalJournalofRecent Technology and Engineering (IJRTE) ISSN: 2277-3878 (Online),Volume-9Issue-1,May2022
[12]AStudyonContemporaryIssuesinAiSearchEngine-A Conceptual Perspective , Balamurugan s, Suriyavarman ,Chellaiya
[13] Application of Artificial Intelligence (AI) in Search EngineOptimization(SEO)September2017 DOI:10.1109/ICSIIT.2017.15