Effective Product Recommendation using the Real-Time Web

Esparza, Sandra Garcia; O’Mahony, Michael P.; Smyth, Barry

doi:10.1007/978-0-85729-130-1_1

Cited by 13 publications

(11 citation statements)

References 26 publications

(22 reference statements)

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“…It draws on detailed representations of items to build a user profile (Balabanović and Shoham 1997;Garcia Esparza et al 2010;Lops et al 2011;Pazzani and Billsus 2007). Specifically, it first assumes that each item can be defined by a profile in the form of a vector X = (x 1 , x 2 , .…”

Section: Content-based Approachmentioning

confidence: 99%

“…The extracted terms can then be used to characterize the reviewer with a term-based user profile. In (Garcia Esparza et al 2010, 2011, the built profile is leveraged into the content-based approach to generate recommendations (see Sect. 4.1).…”

Section: Review Elementsmentioning

confidence: 99%

“…In this sub-branch of approaches, users are characterized by the textual content of their reviews (Garcia Esparza et al 2010). The term-based user profile {t 1 , .…”

Section: Term-based Profilementioning

confidence: 99%

“…, t n } includes keywords extracted from user reviews, and each keyword t j is assigned a weight U i, j by TF-IDF (see Sect. 2.1) that indicates its importance to the reviewer U i ; this is called term-based user index in (Garcia Esparza et al 2010). Similarly, a term-based product index P i, j can also be constructed using terms extracted from reviews posted to the product P i .…”

Section: Term-based Profilementioning

confidence: 99%

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Recommender systems based on user reviews: the state of the art

Chen

Wang

2015

User Model User-Adap Inter

312

187

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In recent years, a variety of review-based recommender systems have been developed, with the goal of incorporating the valuable information in user-generated textual reviews into the user modeling and recommending process. Advanced text analysis and opinion mining techniques enable the extraction of various types of review elements, such as the discussed topics, the multi-faceted nature of opinions, contextual information, comparative opinions, and reviewers' emotions. In this article, we provide a comprehensive overview of how the review elements have been exploited to improve standard content-based recommending, collaborative filtering, and preference-based product ranking techniques. The review-based recommender system's ability to alleviate the well-known rating sparsity and cold-start problems is emphasized. This survey classifies state-of-the-art studies into two principal branches: review-based user profile building and review-based product profile building. In the user profile sub-branch, the reviews are not only used to create term-based profiles, but also to infer or enhance ratings. Multi-faceted opinions can further be exploited to derive the weight/value preferences that users place on particular features. In another sub-branch, the product profile can be enriched with feature opinions or comparative opinions to better reflect its assessment quality. The merit of each branch of work is discussed in terms of both algorithm development and the way in which the proposed

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Section: Content-based Approachmentioning

confidence: 99%

Section: Review Elementsmentioning

confidence: 99%

“…In this sub-branch of approaches, users are characterized by the textual content of their reviews (Garcia Esparza et al 2010). The term-based user profile {t 1 , .…”

Section: Term-based Profilementioning

confidence: 99%

Section: Term-based Profilementioning

confidence: 99%

See 2 more Smart Citations

Recommender systems based on user reviews: the state of the art

Chen

Wang

2015

User Model User-Adap Inter

312

187

View full text Add to dashboard Cite

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“…Recommender systems (RS) facilitate prospective buyers to select any product or service having features matching his or her choice as also giving consideration to the popularity amongst other users [3] [4]. It has varied applications like in purchasing product (Amazon), listening a song (Last.fm) or selecting a hotel (TripAdvisor) etc.…”

Section: Introductionmentioning

confidence: 99%

Review based Feature Matrix for Predicting ratings in Recommender System

Bafna¹,

Shirwaikar²,

Pramod³

et al. 2017

IJET

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Abstract-Recommender systems are acquiring extensive popularity and have become essential component of on-line business handling tools because of their capability of providing personalized guidance in selecting products and services. Collaborative Filtering that brings in the popularity aspect of the item amongst the user base, heavily depends on the ratings provided by the users, while Contentbased Filtering that brings out the item's features matching the user's taste, requires content information as also user's preference information. Service providers usually invite users to share their experience about the use of service in the form of reviews and ratings. Reviews which are verbose contain a rich source of information about the service's features as also user's preferences while ratings are usually sparse due to user's reluctance to quantify. A feature matrix generated by processing review information using semantic similarity based on synsets can be used along with sparse ratings to generate the complete predicted ratings matrix. The paper presents a modified matrix factorization approach for recommendations using the review based feature matrix Keyword-Recommender system, synsets, predictive model. I. INTRODUCTION Recommender systems(RS) have become common place in the digital market arena, by providing the on line user of products and services, a personalized selection guidelines. Generally technology used by RS is categorized into two groups as Content-based and Collaborative [1]. Content based systems are based on user profile and item description, giving importance to various features of items that may interest the user or match his taste.It recommends the item that is having the features that user has liked in the previously consumed items [2].Collaborative Filtering harnesses on popularity element or standing of the item across theuser base and works by collecting user feedback in the form of ratings of items. The challenges faced by collaborative filtering are i. The sparsity of rating matrix due to reluctance on the part of user to rate all the items. ii.The unavailability of data for new user or new item which is also known as cold start problem. iii. Hijacking of the RS by pushing own product rating to the higher value and lowering the competitor's rating leading to reduction in the quality of recommendations. Matrix factorization is widely used, model based approach that estimates ratings. It is a class of latent factor models, where user and items are represented as unknown feature vectors along latent dimensions. The feature vectors are learned using known ratings and learnt features are then used to predict unknown ratings. In the presence of sparse ratings, reviews in textual form present a rich source of feature information. The reviews are converted into the Feature Matrix with group of terms based on synsets as a feature, utilizing Semantic relationship existing between the terms in the form of Synonyms and meronyms. In this paper Matrix factorization approach uses the above generated fe...

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