Cluster searching strategies for collaborative recommendation systems

Altıngövde, İsmail Sengör; Subakan, Özlem N.; Ulusoy, Özgür

doi:10.1016/j.ipm.2012.07.008

Cited by 29 publications

(6 citation statements)

References 27 publications

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“…The collaborative filtering approaches : They makes recommendations to each customer based on information provided by those customers that have the most in common with his/her, ignoring the products representation (Altingovde et al ., 2013; Bobadilla et al ., 2011; Choi et al ., 2016; Formoso et al ., 2013; Ortega et al ., 2013; Wang et al ., 2016). For example, Amazon.com uses an item-based collaborative filtering technique to recommend products that are similar to products purchased by the customer previously (Linden et al ., 2003).…”

Section: Related Work On Product Recommendationmentioning

confidence: 99%

Product recommendation incorporating the consideration of product performance and customer service factors

Fan

Qiao

2017

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Purpose The purpose of this paper is to present a novel framework for recommending desirable products to active customers with the consideration of not only their preferences but also the products’ quality performances and their e-retailers’ service performances under e-commerce. Design/methodology/approach A framework in support of the product recommendation is presented. Three modules are involved in the framework, i.e. data collection and preference analysis module, hybrid recommendation module and recommendation generation module. First, preferences of different types of customers are inferred through analysis of their behavioral data and then a paradigm is adopted based on cased-based reasoning to generate candidate recommendation products. Further, recommendation lists for different types of customers are obtained through measurement of the quality and service performances concerning each candidate recommendation product. Findings To illustrate the performance of the presented framework, a simulation study comparing the approach developed based on the framework and the traditional approach is conducted. The experiment results show that the developed approach outperformed the traditional approach in term of average rank score. This means that incorporating the consideration of product performance and customer service factors can play an important role in product recommendations. Originality/value The presented framework can overcome the defect that low conversion rate of recommended products to actually purchased ones suffered by the traditional approach. In addition, the use of the presented framework can not only help customers to obtain desirable products and save searching time but also supervise and urge e-retailers to pay more attention to the quality and service performances.

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Section: Related Work On Product Recommendationmentioning

confidence: 99%

Product recommendation incorporating the consideration of product performance and customer service factors

Fan

Qiao

2017

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“…The CF technique utilizes users' rating information on items to represent their preference on corresponding items and predicts a target user's ratings of items based on the user's similarity in ratings [19,20].…”

Section: Collaborative Filteringmentioning

confidence: 99%

Extended Collaborative Filtering Technique for Mitigating the Sparsity Problem

Choi¹,

Suh²,

Yoo

2016

INT J COMPUT COMMUN

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Many online shopping malls have implemented personalized recommendation systems to improve customer retention in the age of high competition and information overload. Sellers make use of these recommendation systems to survive high competition and buyers utilize them to find proper product information for their own needs. However, transaction data of most online shopping malls prevent us from using collaborative filtering (CF) technique to recommend products, for the following two reasons: 1) explicit rating information is rarely available in the transaction data; 2) the sparsity problem usually occurs in the data, which makes it difficult to identify reliable neighbors, resulting in less effective recommendations. Therefore, this paper first suggests a means to derive implicit rating information from the transaction data of an online shopping mall and then proposes a new user similarity function to mitigate the sparsity problem. The new user similarity function computes the user similarity of two users if they rated similar items, while the user similarity function of traditional CF technique computes it only if they rated common items. Results from several experiments using an online shopping mall dataset in Korea demonstrate that our approach significantly outperforms the traditional CF technique.

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“…In fact, we set Q to be D , that is, every point in D is considered as a query. It is one of the primitive operations performed in data mining, information retrieval, recommender systems and machine learning [9–23]. There are three groups of algorithms for k -NN graph construction: LSH-based algorithms, clustering-based algorithms and heuristic or data-distribution based algorithms.…”

Section: Related Workmentioning

confidence: 99%

Query-specific signature selection for efficient k-nearest neighbour approximation

Park

Hwang

Lee

2016

Journal of Information Science

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Finding k-nearest neighbours ( k-NN) is one of the most important primitives of many applications such as search engines and recommendation systems. However, its computational cost is extremely high when searching for k-NN points in a huge collection of high-dimensional points. Locality-sensitive hashing (LSH) has been introduced for an efficient k-NN approximation, but none of the existing LSH approaches clearly outperforms others. We propose a novel LSH approach, Signature Selection LSH (S2LSH), which finds approximate k-NN points very efficiently in various datasets. It first constructs a large pool of highly diversified signature regions with various sizes. Given a query point, it dynamically generates a query-specific signature region by merging highly effective signature regions selected from the signature pool. We also suggest S2LSH-M, a variant of S2LSH, which processes multiple queries more efficiently by using query-specific features and optimization techniques. Extensive experiments show the performance superiority of our approaches in diverse settings.

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Cluster searching strategies for collaborative recommendation systems

Cited by 29 publications

References 27 publications

Product recommendation incorporating the consideration of product performance and customer service factors

Product recommendation incorporating the consideration of product performance and customer service factors

Extended Collaborative Filtering Technique for Mitigating the Sparsity Problem

Query-specific signature selection for efficient k-nearest neighbour approximation

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