Implementation of an Intelligent Product Recommender System in an e-Store

Bahrainian, Seyed Ali; Bahrainian, Seyed Mohammad; Salarinasab, Meytham; Dengel, Andreas

doi:10.1007/978-3-642-15470-6_19

Cited by 8 publications

(4 citation statements)

References 5 publications

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“…In this application, the weights of a shopper's needs on each component are collected and the most satisfied candidates are then generated according to a fuzzy similarity measure model. In the e-shopping system proposed in (Bahrainian et al 2010), a recommendation framework is implemented as part of the electronic customer relationship management (E-CRM) strategy. By incorporating collaborative and non-collaborative filtering methods, the user interface (UI) can alter automatically as the customer profile changes, enabling personalized dynamic recommendations to be provided for users.…”

Section: ) B2c Recommender Systemsmentioning

confidence: 99%

Tree Similarity Measure-based Recommender Systems

Wu¹

2020

Recommender Systems

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The rapid growth of web information provides excellent opportunities for developing e-services in many applications but also caused increasingly severe information overload problems whereby users are not able to locate relevant information to exactly meet their needs efficiently by using the current Internet search functions. A personalised recommender system aims to handle this issue.A big challenge in current recommender system research is: the items and user profiles in many recommender system applications nowadays, such as the e-business and e-learning recommender systems, are so complex that they can only be described in complicated tree structures. Therefore, the item or user similarity measure, as the core technique of the recommendation approach, becomes a tree similarity measure, which existing recommender systems cannot provide. Another challenge is that in many real life situations, online recommendations to customers in selecting the most suitable products/services are often made under incomplete and uncertain information, which needs fuzzy set theory and techniques to deal with. Thus, how to use fuzzy set techniques to handle data uncertainty issues in tree-structured items or user profiles needs to be investigated.This research aims to handle these two challenges in both theoretical and practical aspects. It first defines a tree-structured data model, which can be used to model treestructured items, user profiles and user preferences. A comprehensive similarity measure on tree-structured data considering all the information on tree structures, nodes' concepts, weights and values is then developed, which can be used to compute the semantic similarity between tree-structured items or users, and the matching degree of items to tree-structured user requests. Based on the tree-structured data model, the tree-structured iv items and user requirements are modelled as item trees and user request trees respectively. An item tree and user request tree-based hybrid recommendation approach is then developed. To model users' fuzzy tree-structured preferences, a fuzzy preference tree model is proposed. A fuzzy preference tree-based recommendation approach is then developed. Experimental results on an Australian business dataset and the Movielens dataset show that the proposed recommendation approaches have good performance and are well-suited in dealing with tree-structured data in recommender systems. By use of the proposed tree similarity measure and recommendation approaches based on that, two real world applications, a business partner recommender system, Smart BizSeeker, and an e-learning recommender system, ELRS, are designed and implemented, which demonstrate the applicability and effectiveness of the proposed approaches. v

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Section: ) B2c Recommender Systemsmentioning

confidence: 99%

Tree Similarity Measure-based Recommender Systems

Wu¹

2020

Recommender Systems

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“…Recommender systems try to predict the users' preferences in order to help them find interesting items. Research on recommender systems was first conducted in the 90s [17], and since then it has attracted a lot of attention for recommending products in e-commerce websites or information [15,21] (e.g., news, tweets). Recently, due to the availability of the Internet access on mobile devices and based on the fact that users interact with LBSNs more often, researchers have been focusing their interest in analyzing social aspects while recommending venues.…”

Section: Related Workmentioning

confidence: 99%

Venue Suggestion Using Social-Centric Scores

Aliannejadi¹,

Crestani²

2018

Preprint

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User modeling is a very important task for making relevant suggestions of venues to the users. These suggestions are often based on matching the venues' features with the users' preferences, which can be collected from previously visited locations. In this paper, we present a set of relevance scores for making personalized suggestions of points of interest. These scores model each user by focusing on the different types of information extracted from venues that they have previously visited. In particular, we focus on scores extracted from social information available on location-based social networks. Our experiments, conducted on the dataset of the TREC Contextual Suggestion Track, show that social scores are more effective than scores based venues' content.

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“…We propose here MuSIF, a Product Recommendation System Based on Multi-source IF. In contrast to most research on IF systems, we utilize different sources of information in addition to purchase history and try to construct an estimate of user preference towards an item [10], [11], [12], [13], [14] [15], [16].The ultimate goal would be to construct an equation that includes a vast number of implicit sources and accurately models user preference. Research exists for systems that use a combination of implicit and explicit feedback [17] [18] [19] or even knowledge derived from users social network [16], though we focus on a purely implicit approach with knowledge derived from user item interactions.…”

Section: Introductionmentioning

confidence: 99%

MuSIF: A Product Recommendation System Based on Multi-source Implicit Feedback

Schoinas

Tjortjis

2019

IFIP Advances in Information and Communication Technology

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Collaborative Filtering (CF) is a well-established method in Recommendation Systems. Recent research focuses on extracting recommendations also based on implicitly gathered information. Implicit Feedback (IF) systems present several new challenges that need to be addressed. This paper reports on MuSIF, a product recommendation system based solely on IF. MuSIF incorporates CF with Matrix Factorization and Association Rule Mining. It implements a hybrid recommendation algorithm in a way that different methods can be used to increase accuracy. In addition, it is equipped with a new method to increase the accuracy of matrix factorization algorithms via initialization of factor vectors, which, as far as we know, is tested for the first time in an implicit modelbased CF approach. Moreover, it includes methods for addressing data sparsity, a major issue for many recommendation engines. Evaluation shows that the proposed methodology is promising and can benefit customers and e-shop owners with personalization in real world scenarios

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Implementation of an Intelligent Product Recommender System in an e-Store

Cited by 8 publications

References 5 publications

Tree Similarity Measure-based Recommender Systems

Tree Similarity Measure-based Recommender Systems

Venue Suggestion Using Social-Centric Scores

MuSIF: A Product Recommendation System Based on Multi-source Implicit Feedback

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